Statistics

An application of probability, with intimate ties to machine learning, non-demonstrative inference and induction.

Since June 2005, I have been a (very, very junior) professor of statistics. This made me interested in how to teach it.

Dependent data: Statistical inference for stochastic processes, a.k.a. time-series analysis. Signal processing and filtering. Spatial statistics.
Model selection: Especially: adapting to unknown characteristics of the data, like unknown noise distributions, or unknown smoothness of the regression function.
Model discrimination: That is, designing experiments so as to discriminate between competing classes of model. Adaptation to data issues here.
Rates of convergence of estimators to true values: Empirical process theory. (Cf. some questions in ergodic theory).
Estimating distribution functions: And estimating entropies, or other functionals of distributions.
Non-parametric methods: Both those that are genuinely distribution-free, and those that would more accurately be mega-parametric (even infinitely-parametric) methods, such as neural networks
Regression
Bootstrapping and other resampling methods
Cross-validation
Sufficient statistics
Exponential families
Information Geometry
Partial identification of parametric statistical models
Causal Inference
Decision theory: Conventional, and the sorts with some connection to how real decisions are made.
Graphical models
Monte Carlo and other simulation methods
"De-Bayesing": Ways of taking Bayesian procedures and eliminating dependence on priors, either by replacing them by initial point-estimates, or by showing the prior doesn't matter, asymptotically or hopefully sooner. See: Frequentist consistency of Bayesian procedures.
Computational Statistics
Statistics of structured data
Grammatical Inference
Factor analysis
Multiple testing
Predictive distributions: ... especially if they have confidence/coverage properties
Density estimation: especially conditional density estimation; and density estimation on graphical models
Indirect inference

Francis Galton, "Statistical Inquiries into the Efficacy of Prayer," Fortnightly Review 12 (1872): 125--135 [online]
Larry Gonick and Woollcott Smith, The Cartoon Guide to Statistics
Ian Hacking, The Taming of Chance [Putting chance to work in the 19th century]
D. Huff, How to Lie with Statistics
Theodore Porter, The Rise of Statistical Thinking, 1820--1900
Constance Reid, Neyman from Life [Biography of Jerzy Neyman, one of the makers of modern statistical theory, and, I am happy to say, among the brighter lights of my alma mater. Reid does an excellent job of explaining Neyman's work in terms accessible to the general reader. There is a new edition, titled simply Neyman, but otherwise unchanged. Review by Steve Laniel]
Edward R. Tufte
- The Visual Display of Quantitative Information
- Visual Explanations

Ole E. Barndorff-Nielsen and David R. Cox, Inference and Asymptotics
M. S. Bartlett
- "Inference and Stochastic Processes", Journal of the Royal Statistical Society A 130 (1967): 457--478 [JSTOR]
- "Chance or Chaos?", Journal of the Royal Statistical Society A 153 (1990): 321--347 [JSTOR]
Richard A. Berk, Regression Analysis: A Constructive Critique [My comments]
Leo Breiman, "Statistical Modeling: The Two Cultures", Statistical Science 16 (2001): 199--231 [very much including the discussion by others and the reply by Breiman. Thanks to Chris Wiggins for alerting me to this.]
D. R. Cox and Christl A. Donnelly, Principles of Applied Statistics [Review forthcoming]
Harald Cramér, Mathematical Methods of Statistics [Review]
C. David Garson, Statnotes: An Online Textbook
Peter Guttorp, Stochastic Modeling of Scientific Data [Good introduction to using dependent data]
Trever Hastie, Robert Tibshirani and Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction [Website, with full text free in PDF]
Robert E. Kass, "Statistical Inference; The Big Picture", Statistical Science 26 (2011): 1--19, arxiv:1106.2895
Tony Lin [Prof. Dr. Lin was working on his doctorate when I was an undergrad at Berkeley; we became friends at the I-House, if that is the word I want for someone who offered to keep my brain alive in a jigger-glass and subject it to random electrical shocks ("Jzzt! Jzzt!"). But despite his questionable tastes in acquaintances, he's a damn good statistician and a model teacher.]
- Virtual Statistics 50 [Intro. statistics]
- Virtual Statistics 154A [Intro. statistics with algebra and calculus]
Deborah Mayo, Error and the Growth of Experimental Knowledge [Review: We Have Ways of Making You Talk, or, Long Live Peircism-Popperism-Neyman-Pearson Thought!]
NIST, Electronic Handbook of Statistical Methods [Full text free online]
E. J. G. Pitman, Some Basic Theory for Statistical Inference [Review: Intermediate Statistics from an Advanced Point of View]
Jorma Rissanen, Stochastic Complexity in Statistical Inquiry [Review: Less Is More, or, Ecce data!]
Mark Schervish, Theory of Statistics
John R. Taylor, An Introduction to Error Analysis: The Study of Uncertainties in Physical Measurements [a.k.a. "the book with the train-wreck on the cover"]
Larry Wasserman
- All of Statistics
- All of Nonparametric Statistics

A. C. Atkinson and A. N. Donev, Optimum Experimental Design [Review]
F. Bacchus, H. E. Kyburg and M. Thalos, "Against Conditionalization," Synthese 85 (1990): 475--506 [Why "Dutch book" arguments do not, in fact, mean that rational agents must be Bayesian reasoners]
Andrew Barron and Nicolas Hengartner, "Information theory and superefficiency", Annals of Statistics 26 (1998): 1800--1825
M. S. Bartlett, "The Statistical Significance of Odd Bits of Information", Biometrika 39 (1952): 228--237 [A goodness-of-fit test based on fluctuations of the entropy. JSTOR]
M. J. Bayarri and James O. Berger, "P Values for Composite Null Models", Journal of the American Statistical Association 95 (2000) 127--1142 [To be read in conjunction with Robins, van der Vaart and Ventura, below. JSTOR]
Anil K. Bera and Aurobindo Ghosh, "Neyman's Smooth Test and Its Applications in Econometrics", pp. 177--230 in Aman Ullah, Alan T. K. Wan and Anoop Chaturvedi (eds.), Handbook of Applied Econometrics and Statistical Inference, SSRN/272888
Julian Besag, "A Candidate's Formula: A Curious Result in Bayesian Prediction", Biometrika 76 (1989): 183 [A wonderful and bizarre expression for the Bayesian predictive density, in terms of how adding a new data point would change the posterior. JSTOR]
David Blackwell and M. A. Girshick, Theory of Games and Statistical Decisions
Leo Breiman, "No Bayesians in Foxholes", IEEE Expert: Intelligent Systems and Their Applications 12 (1997): 21--24 [PDF reprint; comments by Andy Gelman]
Jochen Brocker, "A Lower Bound on Arbitrary f-Divergences in Terms of the Total Variation" arxiv:0903.1765
Peter Bühlmann and Sara van de Geer, Statistics for High-Dimensional Data: Methods, Theory and Applications [Mini-review]
Ronald W. Butler, "Predictive Likelihood Inference with Applications", Journal of the Royal Statistical Society B 48 (1986): 1--38 ["in the predictive setting, all parameters are nuisance parameters". JSTOR]
Hwan-sik Choi and Nicholas M. Kiefer, "Differential Geometry and Bias Correction in Nonnested Hypothesis Testing" [PDF preprint via Kiefer]
A. C. Davison and D. V. Hinkley, Bootstrap Methods and their Applications
J. Bradford DeLong and Kevin Lang, "Are All Economic Hypotheses False?", Journal of Political Economy 100 (1992): 1257--1272 [PDF preprint. The point is about abuses of hypothesis testing, not economic hypotheses as such.]
Earman, Bayes or Bust? A Critical Account of Bayesian Confirmation Theory
Bradley Efron
- "Bootstrap Methods: Another Look at the Jackknife", Annals of Statistics 7 (1979): 1--26 [The original paper; staggeringly understandable]
- The Jackknife, the Bootstrap, and Other Resampling Plans
- "Maximum Likelihood and Decision Theory", The Annals of Statistics 10 (1982): 340--356
Steve Fienberg, The Analysis of Cross-Classified Categorical Data
Don Fraser, "Is Bayes posterior just quick and dirty confidence", Statistical Science 26 (2011): 299--316, arxiv:1112.5582 [See also the discussions by others, and Fraser's reply. My answer to the question posed in Fraser's title is "yes", or rather "YES!"]
Andrew Gelman, Jennifer Hill and Masanao Yajima, "Why we (usually) don't have to worry about multiple comparisons" [PDF preprint]
Andrew Gelman and Iain Pardoe, "Average predictive comparisons for models with nonlinearity, interactions, and variance components", Sociological Methodology forthcoming (2007) [PDF preprint, Gelman's comments]
Christopher Genovese, Peter Freeman, Larry Wasserman, Robert C. Nichol and Christopher Miller, "Inference for the Dark Energy Equation of State Using Type IA Supernova Data", Annals of Applied Statistics 3 (2009): 144--178, arxiv:0805.4136 [I am biased, because Genovese and Wasserman are friends, but this seems to me a model of a modern applied statistics paper: use interesting statistical ideas to say something helpful about an important scientific problem on its own terms, rather than distorting the problem until it "looks like a nail".]
Charles J. Geyer, "Le Cam Made Simple: Asymptotics of Maximum Likelihood without the LLN or CLT or Sample Size Going to Infinity" [PDF preprint, via Prof. Geyer. — There are two separable points here. One is that much of the usual asymptotic theory of maximum likelihood follows from the quadratic form of the likelihood alone; whenever and however that is reached, those consequences follow. Approximately quadratic likelihoods imply approximations to the usual asymptotics. This is unquestionably correct. The other is some bashing of results like the law of large numbers and central limit theorem, which seems misguided to me.]
Tilmann Gneiting, "Making and Evaluating Point Forecasts", Journal of the American Statistical Association 106 (2011): 746--762, arxiv:0912.0902
Trygve Haavelmo, "The Probability Approach in Econometrics", Econometrica 12 (1944, supplement): iii--115 [JSTOR]
Mark S. Handcock and Martina Morris, Relative Distribution Methods in the Social Sciences [Review: Beyond Mean and Deviance]
Bruce E. Hansen
- "The Likelihood Ratio Test Under Nonstandard Conditions: Testing the Markov Switching Model of GNP", Journal of Applied Econometrics 7 (1992): S61--S82 [I very much like the approach of treating the likelihood ratio as an empirical process; why haven't I seen it before? (Also, the state-of-the-art in simulating Gaussian processes must be much better now than what Hansen had in '92, which would make this even more practical. PDF reprint.]
- "Inference when a nuisance parameter is not identified under the null hypothesis", Econometrica 64 (1996): 413--430
Jeffrey D. Hart, Nonparametric Smoothing and Lack-of-Fit Tests [comments]
Nils Lid Hjort and David Pollard, "Asymptotics for minimisers of convex processes", arxiv:1107.3806 [Very elegant]
Peter J. Huber, "On the Non-Optimality of Optimal Procedures" [Full text]
Wilbert C. M. Kallenberg and Teresa Ledwina, "Data-driven smooth tests when the hypothesis is composite", Journal of the American Statistical Association 92 (1997): 1094--1104 [Abstract, PDF reprint; JSTOR]
Gary King, A Solution to the Ecological Inference Problem: Reconstructing Individual Behavior from Aggregate Data [Review]
Solomon W. Kullback, Information Theory and Statistics
Michael Lavine and Mark J. Schervish, "Bayes Factors: What They Are and What They Are Not" [PS preprint]
Steffen Lauritzen, Extremal Families and Systems of Sufficient Statistics [See comments under sufficient statistics]
J. F. Lawless and Marc Fredette, "Frequentist prediction intervals and predictive distributions", Biometrika 92 (2005): 529--542 ["Frequentist predictive distributions are defined as confidence distributions .... A simple pivotal-based approach that produces prediction intervals and predictive distributions with well-calibrated frequentist probability interpretations is introduced, and efficient simulation methods for producing predictive distributions are considered. Properties related to an average Kullback-Leibler measure of goodness for predictive or estimated distributions are given."]
Lucien Le Cam
- "Neyman and Stochastic Models" [PDF. Some vignettes of Neyman putting together models, and his model-building process.]
- "Maximum Likelihood; An Introduction" [PDF. Not really an introduction, but rather a collection of examples of where it just does not work, or at least doesn't work well.]
Erich L. Lehmann, "On likelihood ratio tests", math.ST/0610835
Jing Lei, James Robins, Larry Wasserman, "Efficient Nonparametric Conformal Prediction Regions", arxiv:1111.1418
Bing Li, "A minimax approach to consistency and efficiency for estimating equations," Annals of Statistics 24 (1996): 1283--1297 [online version]
Bruce Lindsay and Liawei Liu, "Model Assessment Tools for a Model False World", Statistical Science 24 (2009): 303--318, arxiv:1010.0304 [Their model-adequacy index is, essentially, the number of samples needed to detect the falsity of the model with some reasonable, pre-set level of power, with fixed size/significance level. This is a very natural quantity. In fact, by results which go back to Kullback's book, the power grows exponentially, with a rate equal to the Kullback-Leibler divergence rate. (More exactly, one minus the power goes to zero exponentially at that rate, but you know what I meant.) Large deviations theory includes generalizations of this result. Many statisticians, I'd guess, would prefer the Lindsay-Liu index because will feel it more natural to them to gauge error in terms of a sample size rather than bits, but to each their own.]
Brad Luen and Philip B. Stark, "Testing earthquake predictions", pp. 302--315 in Deborah Nolan and Terry Speed (eds.), Probability and Statistics; Essays in Honor of David A. Freedman [The issues arise however not just for earthquakes, but for all sorts of clustered events]
Charles Manski, Identification for Prediction and Decision [Mini-review]
Deborah G. Mayo and D. R. Cox, "Frequentist statistics as a theory of inductive inference", math.ST/0610846
M. B. Nevel'son and R. Z. Has'minskii, Stochastic Approximation and Recursive Estimation
Andrey Novikov, "Optimal sequential multiple hypothesis tests", arxiv:0811.1297
David Pollard
- "Asymptotics via Empirical Processes", Statistical Science 4 (1989): 341--354
- Empirical Processes: Theory and Applications
Jeffrey S. Racine, "Nonparametric Econometrics: A Primer", Foundations and Trends in Econometrics 3 (2008): 1--88 [Good primer of nonparametric techniques for regression, density estimation and hypothesis testing; next to no economic content (except for examples). Presumes reasonable familiarity with parametric statistics. PDF reprint]
James M. Robins and Ya'acov Ritov, "Toward a curse of Dimensionality Appropriate (CODA) Asymptotic Theory for Semi-Parametric Models", Statistics in Medicine 16 (1997): 285--319 [PDF reprint via Prof. Robins]
James M. Robins, Aad van de Vaart and Valérie Ventura, "Asymptotic Distribution of P Values in Composite Null Models", Journal of the American Statistical Association 95 (2000): 1143--1156 [JSTOR. Paired article with Bayarri and Berger, above. The discussions and rejoinders (pp. 1157--1172) are valuable.]
George G. Roussas, Contiguity of Probability Measures: Some Applications in Statistics [Mini-review]
C. Scott and R. Nowak, "A Neyman-Pearson Approach to Statistical Learning", IEEE Transactions on Information Theory 51 (2005): 3806--3819 [Comments: Learning Your Way to Maximum Power]
Jeffrey S. Simonoff, Smoothing Methods in Statistics
Spyros Skouras, "Decisionmetrics: Towards a Decision-Based Approach to Econometrics," SFI Working Paper 2001-11-064 [Applies far outside econometrics. If what you really want to do is to minimize a known loss function, optimizing a conventional accuracy measure, e.g. least squares, can be highly counterproductive.]
Aris Spanos
- "The Curve-Fitting Problem, Akaike-type Model Selection, and the Error Statistical Approach" [Or: could your model selection tell you that Kepler is better than Ptolemy? Technical report, economics dept., Virginia Tech, 2006. PDF]
- "Where do statistical models come from? Revisiting the problem of specification", math.ST/0610849
Sara van de Geer, Empirical Process Theory in M-Estimation [Finding non-asymptotic rates of convergence for common estimators]
Quang H. Vuong, "Likelihood Ratio Tests for Model Selection and Non-Nested Hypotheses", Econometrica 57 (1989): 307--333
Grace Wahba, Spline Models for Observational Data
Michael E. Wall, Andreas Rechtsteiner and Luis M. Rocha, "Singular Value Decomposition and Principal Component Analysis," physics/0208101
Larry Wasserman, "Low Assumptions, High Dimensions", RMM 2 (2011): 201--209
Halbert White, Estimation, Inference and Specification Analysis [mini-review]
Achilleas Zapranis and Apostolos-Paul Refenes, Principles of Neural Model Identification, Selection and Adequacy, with Applications to Financial Econometrics
Sven Zenker, Jonathan Rubin, Gilles Clermont, "From Inverse Problems in Mathematical Physiology to Quantitative Differential Diagnoses", PLoS Computational Biology 3 (2007): e205

Nicola Giocoli, "From Wald to Savage: homo economicus becomes a Bayesian statistician" [preprint]
Erich L. Lehmann, "On the history and use of some standard statistical models", pp. 114--126 in Deborah Nolan and Terry Speed (eds.), Probability and Statistics: Essays in Honor of David A. Freedman
Henry Scheffe, "Statistical Inference in the Non-Parametric Case", Annals of Mathematical Statistics 14 (1943): 305--332 [Recommended not as a historical study, but a historical document]

Andrew Gelman and CRS, "Philosophy and the practice of Bayesian statistics", submitted to the Journal of the American Statistical Association, arxiv:1006.3868
CRS, "The Bootstrap", American Scientist 98 (2010): 186--190 [Further]

R. Harald Baayen, Analyzing Linguistic Data: A Practical Introduction to Statistics Using R [blurb]
Vic Barnett, Comparative Statistical Inference
Bucklew, Large Deviation Techniques in Decision, Simulation, and Estimation
A. C. Davison, Statistical Models
Alan Julian Izenman, Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning
Erich L. Lehmann, Elements of Large-Sample Theory
National Institute of Standards and Technology, Engineering Statistics Handbook [All the sections I've looked at have been quite good.]
Yudi Pawitan, In All Likelihood: Statistical Modeling and Inference Using Likelihood
Aris Spanos, Probability Theory and Statistical Interference: Econometric Modeling with Observational Data

Odd O. Aalen, Per Kragh Andersen, \Ornulf Borgan, Richard D. Gill, Niels Keiding, "History of applications of martingales in survival analysis", Electronic Journal for History of Probability and Statistics 5 (2009), arxiv:1003.0188
Carolina Armenteros, "From Human Nature to Normal Humanity: Joseph de Maistre, Rousseau, and the Origins of Moral Statistics", Journal of the History of Ideas 68 (2007): 107--130 [Abstract, text links]
Ian Hacking, "The Theory of Probable Inference: Neyman, Peirce and Braithwaite," in Science, Belief and Behavior: Essays in Honor of R. B. Braithwaite ed. D. H. Mellor
Anders Hald, A History of Parametric Statistical Inference from Bernoulli to Fisher, 1713--1935 [Blurb]
Kendall and Plackett (eds.), Studies in the History of Statistics and Probability
Kyburg, Uncertain Inference
Erich Lehmann and Juliet Shadder, Fisher, Neyman, and the Creation of Classical Statistics [Blurb]
Mayo and Hollander (eds.), Acceptable Evidence: Science and Values in Risk Management
Leland Gerson Neuberg, Conceptual Anomalies in Economics and Statistics: Lessons from the Social Experiment [blurb]
Theodore Porter, Trust in Numbers
Stephen M. Stigler
- The History of Statistics: The Measure of Uncertainty before 1900
- Statistics on the Table: The History of Statistical Concepts and Methods
Gregory D. Wilson, Articulation Theory and Disciplinary Change: Unpacking the Bayesian-Frequentist Paradigm Conflict in Statistical Science [Ph.D. thesis, New Mexico State University, 2001]
S. L. Zabell, Symmetry and Its Discontents: Essays on the History of Inductive Probability [blurb]

Felix Abramovich, Yoav Benjamini, David L. Donoho and Iain M. Johnstone, "Adapting to Unknown Sparsity by controlling the False Discovery Rate", math.ST/0505374 [I don't really care about sparsity, but they promise novel relations between the FDR control and asymptotic minimaxity and complexity-penalized model selection.]
Sophie Achard, "A quadratic measure of dependence", math.ST/0609259 ["Asymptotic properties of a dimension-robust dependence measure are investigated. It is related to those used in independence tests, but is derivable, thus suitable for independent component analysis. An adjustable kernel allows to accelerate the convergence of the estimator without affecting the bias."]
Gianfranco Adimari and Annamaria Guolo, "A note on the asymptotic behaviour of empirical likelihood statistics", Statistical Methods and Applications 19 (2010): 463--476
Shotaro Akaho, "A kernel method for canonical correlation analysis", cs.LG/0609071
Elizabeth S. Allman, Catherine Matias, John A. Rhodes, "Identifiability of parameters in latent structure models with many observed variables", Annals of Statistics 37 (2009): 3099--3132, arxiv:0809.5032
Miguel A. Arcones, "Bahadur Efficiency of the Likelihood Ratio Test" [PDF preprint from 2005, presumably since published...]
Barry C. Arnold et al., Conditional Specification of Statistical Models
R. A. Bailey, Design of Comparative Experiments [Blurb]
Roger Barlow, "Asymmetric Errors", physics/0401042
Ole E. Barndorff-Nielsen and David R. Cox, "Prediction and Asymptotics", Bernoulli 2 (1996): 319--340
Ole E. Barndorff-Nielsen, David R. Cox and Claudia Klüppelberg (eds.), Complex Stochastic Systems
Zvika Ben-Haim and Yonina C. Eldar, "The Cramer-Rao Bound for Sparse Estimation", arxiv:0905.4378
Yoav Benjamini, Marina Bogomolov, "Adjusting for selection bias in testing multiple families of hypotheses", arxiv:1106.3670
Patrice Bertail, Paul Doukhan and Philippe Soulier (eds.), Dependence in Probability and Statistics ["recent developments in ... probability and statistics for dependent data... from Markov chain theory and weak dependence with an emphasis on ... dynamical systems, to strong dependence in times series and random fields. ... section on statistical estimation problems and specific applications". Full blurb, contents]
Rabi Bhattacharya and Vic Patrangenaru, "Large sample theory of intrinsic and extrinsic sample means on manifolds--II", math.ST/0507423 = Annals of Statistics 33 (2005): 1225--1259 [I need a notebook on statistics on manifolds, as opposed to statistical manifolds]
G. Biau and L. Gyorfi, "On the Asymptotic Properties of a Nonparametric $-Test Statistic of Homogeneity", IEEE Transactions on Information Theory 51 (2005): 3965--3973
David R. Bickel
- "The Strength of Statistical Evidence for Composite Hypotheses: Inference to the Best Explanation" [Preprint]
- "Resolving conflicts between statistical methods by probability combination: Application to empirical Bayes analyses of genomic data", arxiv:1111.6174
Peter J. Bickel, C. A. J. Klaassen, Y. Ritov and J. A. Wellner, Efficient and Adaptive Estimation for Semiparametric Models
Peter J. Bickel and Bo Li, "Regularization in Statistics", Test 15 (2006): 271--344 [PDF reprint]
Peter J. Bickel and Y. Ritov, "Non-Parametric Estimators Which Can Be `Plugged-In' " UCB Stat. Tech. Rep. 602 [abstract, pdf]
L. Birge, "A New Lower Bound for Multiple Hypothesis Testing", IEEE Transactions on Information Theory 51 (2005): 1611--1615
Gilles Blanchard, Sylvain Delattre, Etienne Roquain , "Testing over a continuum of null hypotheses", arxiv:1110.3599
Michael Blum, "Approximate Bayesian Computation: a non-parametric perspective", arxiv:0904.0635
Ingwer Borg and Patrick J. F. Groenen, Modern Multidimensional Scaling: Theory and Application
A. R. Brazzale and A. C. Davison, "Accurate Parametric Inference for Small Samples", Statistical Science 23 (2008): 465--484 [Apparently, a preview for the book.]
A. R. Brazzale, A. C. Davison and N. Reid, Applied Asymptotics: Case Studies in Small-Sample Statistics [blurb]
Trevor S. Breusch, "Hypothesis Testing in Unidentified Models", Review of Economic Studies 53 (1986): 635--651 [JSTOR]
Adam D. Bull, "Honest adaptive confidence bands and self-similar functions", arxiv:1110.4985
Florentina Bunea, Alexandre B. Tsybakov, Marten H. Wegkamp, Adrian Barbu, "Spades and Mixture Models", Annals of Statistics 38 (2010): 2525--2558, arxiv:0901.2044
Dizza Bursztyn and david M. Steinberg, "Comparison of designs for computer experiments", Journal of Statistical Planning and Inference 136 (2006): 1103--1119
T. Tony Cai, "Minimax and Adaptive Inference in Nonparametric Function Estimation", Statistical Science 27 (2012): 31--50
T. Tony Cai and Mark G. Low, "An adaptation theory for nonparametric confidence intervals", Annals of Statistics 32 (2004): 1805--1840 = math.ST/0503662
Emmanuel Candes and Terence Tao, "Near Optimal Signal Recovery from Random Projections and Universal Encoding Strategies", math.CA/0410542
Herve Cardot, Andre Mas and Pascal Sarda, "CLT in Functional Linear Regression Models", math.ST/0508073
Djalil Chafai and Didier Concordet, "On the strong consistency of approximated M-estimators", math.ST/0507102 [Sounds cool...]
In Hong Chang and Rahul Mukerjee, "Asymptotic results on the frequentist mean squared error of generalized Bayes point predictors", Statistics and Probability Letters 67 (2004): 65--71 [Note to self: file this one under "de-Bayesing".]
Sandra Chapman, George Rowlands and Nicholas Watkins
- "Extremum statistics: A framework for data analysis," cond-mat/0106015
- "Extremum Statistics and Signatures of Long Range Correlations," cond-mat/0106015
- "The relationship between extremum statistics and universal fluctuations," cond-mat/0007275
Jiahua Chen and Xianming Tan, "Inference for Multivariate Normal Mixtures", arxiv:0805.3906 [Little of the machinery looks like it depends on using mixtures of normals]
Xiaohong Chen, Markus Reiss, "On rate optimality for ill-posed inverse problems in econometrics", arxiv:0709.2003 [Non-parametric instrumental variables?]
Xinjia Chen, "Sequential Tests of Statistical Hypotheses with Confidence Limits", arxiv:1007.4278
N. N. Chentsov, Statistical Decision Rules and Optimal Inference
Zhiyi Chi, "Effects of statistical dependence on multiple testing under a hidden Markov model", Annals of Statistics 39 (2011): 439--473
Bertrand Clarke, "Desiderata for a Predictive Theory of Statistics", Bayesian Analysis 5 (2010): 1--36
Sandy Clarke, Peter Hall, "Robustness of multiple testing procedures against dependence", Annals of Statistics 37 (2009): 332--358, arxiv:0903.0464
Arthur Cohen and Harold B. Sackrowitz, "Decision theory results for one-sided multiple comparison procedures", math.ST/0504505 = Annals of Statistics 33 (2005): 126--144
Arthur Cohen, Harold B. Sackrowitz, Minya Xu, "A new multiple testing method in the dependent case", arxiv:0906.3082 = Annals of Statistics 37 (2009) 1518--1544
Daniel Commenges, "Statistical models: Conventional, penalized and hierarchical likelihood", Statistics Surveys 3 (2009): 1--17, arxiv:0808.4042
Daniel Commenges, Helene Jacqmin-Gadda, Cecile Proust, and Jeremie Guedj, "A Newton-Like Algorithm for Likelihood Maximization: The Robust-Variance Scoring Algorithm", math.ST/0610402
Cox and Wermuth, Multivariate Dependencies: Models, Analysis and Interpretation
Anirban DasGupta, Asymptotic Theory of Statistics and Probability [Blurb]
Alexandre d'Aspremont, Onureena Banerjee, Laurent El Ghaoui, "First-order methods for sparse covariance selection", math.OC/0609812 ["Given a sample covariance matrix, we solve a maximum likelihood problem penalized by the number of nonzero coefficients in the inverse covariance matrix. Our objective is to find a sparse representation of the sample data and to highlight conditional independence relationships between the sample variables."]
I. Dattner, A. Goldenshluger, A. Juditsky, "On deconvolution of distribution functions", arxiv:1006.3918 ["nonparametric estimation of a continuous distribution function from observations with measurement errors... rate optimal estimators based on direct inversion of empirical characteristic function"]
P. L. Davies
- "Data Features", Statistica Neerlandica 49 (1995): 185--245
- "Approximating Data", Journal of the Korean Statistical Society 37 (2008): 191--211 [With discussion and rejoinder. Open access?]
P. L. Davies, A. Kovac and M. Meise, "Nonparametric Regression, Confidence Regions and Regularization", arxiv:0711.0690
A. Philip Dawid, Steven de Rooij, Glenn Shafer, Alexander Shen, Nikolai Vereshchagin, Vladimir Vovk, "Martingales and p-values as measures of evidence", arxiv:0912.4269
Aurore Delaigle, Peter Hall and Jiashun Jin, "Robustness and accuracy of methods for high dimensional data analysis based on Student's t-statistic", Journal of the Royal Statistical Society B forthcoming (2011)
Joshua V Dillon, Guy Lebanon, "Stochastic Composite Likelihood", Journal of Machine Learning Research 11 (2010): 2597--2633, apparently the final version of arxiv:1003.0691
David L. Donoho, "Estimation by epsilon-nets" (Le Cam Lecture, 2003; find citation)
David L. Donoho and Richard C. Liu, "The ``Automatic'' Robustness of Minimum Distance Functionals", Annals of Statistics 16 (1988): 552--586
David L. Donoho and Jared Tanner, "Observed Universality of Phase Transitions in High-Dimensional Geometry, with Implications for Modern Data Analysis and Signal Processing", arxiv:0906.2530
Mathias Drton, "Likelihood ratio tests and singularities", Annals of Statistics 37 (2009): 979--1012, arxiv:math.ST/0703360
Mathias Drton and Seth Sullivant, "Algebraic statistical models", math.ST/0703609
Jin-Chuan Duan and Andras Fulop, "A stable estimator of the information matrix under EM for dependent data", Statistics and Computing 21 (2011): 83--91
Morris L. Eaton, Multivariate Statistics: A Vector Space Approach ["The purpose of this book is to present a version of multivariate statistical theory in which vector space and invariance methods replace, to a large extent, more traditional multivariate methods. The book is a text. Over the past ten years, various versions have been used for graduate multivariate courses at the University of Chicago, the University of Copenhagen, and the University of Minnesota. Designed for a one year lecture course or for independent study, the book contains a full complement of problems and problem solutions." Full text free online.]
Sam Efromovich
- "Distribution estimation for biased data", Journal of Statistical Planning and Inference 124 (2004): 1--43
- "Dimension Reduction and Adaptation in Conditional Density Estimation", Journal of the American Statistical Association 105 (2010): 761--774
- Nonparametric Curve Estimation
Bradley Efron, "Size, power and false discovery rates", Annals of Statistics 35 (2007): 1351--1377, arxiv:0710.2245
Werner Ehm, Jürgen Kornmeier, and Sven P. Heinrich, "Multiple testing along a tree", Electronic Journal of Statistics 4 (2010): 461--471 =? arxiv:0902.2296
Michael Evans and Gun Ho Jang, "Invariant P-values for model checking", Annals of Statistics 38 (2010): 512--525
S. N. Evans and P. B. Stark, "Inverse Problems as Statistics" [Abstract, PDF]
Jianqing Fan and Jian Zhang, "Sieve empirical likelihood ratio tests for nonparametric functions", Annals of Statistics 32 (2004): 1858--1907 = math.ST/0503667
Thomas S. Ferguson, A Course in Large Sample Theory
Jean-David Fermanian and Bernard Salanié "A Nonparametric Simulated Maximum Likelihood Estimation Method", Econometric Theory 20 (2004): 701--734
Ana K. Fermin and Carenne Ludena, "A Statistical view of Iterative Methods for Linear Inverse Problems", math.ST/0504064
Luisa Turrin Fernholz, von Mises Calculus for Statistical Functionals
S. E. Fienberg, P. Hersh, A. Rinaldo and Y. Zhou, "Maximum Likelihood Estimation in Latent Class Models For Contingency Table Data", arxiv:0709.3535
D. A. S. Fraser, N. Reid, E. Marras and G. Y. Yi, "Default priors for Bayesian and frequentist inference", Journal of the Royal Statistical Society B 72 (2010): 631--654
A. Fraysse, "Why minimax is not that pessimistic", arxiv:0902.3311 [Because, apparently, learning a generic function is just as hard as minimax leads you to think. Bummer if true.]
Magalie Fromont and Béatrice Laurent, "Adaptive goodness-of-fit tests in a density model", Annals of Statistics 34 (2006): 680--720 = math.ST/0607013
Kenji Fukumizu, Le Song, Arthur Gretton, "Kernel Bayes' rule", arxiv:1009.5736 ["A kernel method is proposed for realizing Bayes' rule, based on representations of probability distributions in reproducing kernel Hilbert spaces (RKHS). The empirical RKHS embeddings of the conditional probabilities and prior are expressed as feature mappings of samples, and an RKHS embedding of the posterior distribution is computed, again based on a feature mapping of a sample."]
Surya Ganguli and Haim Sompolinsky, "Statistical Mechanics of Compressed Sensing", Physical Review Letters 104 (2010): 188701
Seymour Geisser, Predictive Inference
Christopher R. Genovese and Larry Wasserman
- "Confidence sets for nonparametric wavelet regression", math.ST/0505632 = Annals of Statistics 33 (2005): 698--729
- "Adaptive Confidence Bands", math.ST/0701513
Christopher Genovese, Marco Perone-Pacifico, Isabella Verdinelli, Larry Wasserman, "Minimax Manifold Estimation", arxiv:1007.0549
Josep Ginebra, "On the Measure of the Information in a Statistical Experiment", Bayesian Analysis (2007): 167--212
Tilmann Gneiting, Fadoua Balabdaoui and Adrian E. Raftery, "Probabilistic forecasts, calibration and sharpness", Journal of the Royal Statistical Society 69 (2007): 243--268
Yuri Golubev, Vladimir Spokoiny, "Exponential bounds for minimum contrast estimators", arxiv:0901.0655
Grassberger and Nadal (eds.), From Statistical Physics to Statistical Inference and Back
Ulf Grenander, Abstract Inference
Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Sch�lkopf, Alexander Smola, "A Kernel Two-Sample Test", Journal of Machine Learning Research 13 (2012): 723--773
Arthur Gretton, László Györfi, "Consistent Nonparametric Tests of Independence", Journal of Machine Learning Research 11 (2010): 1391--1423
Robert Hable, "Asymptotic Normality of Support Vector Machines for Classification and Regression", arxiv:1010.0535
Peter Hall, Hans-Georg Müller, Fang Yao, "Estimation of functional derivatives", Annals of Statistics 37 (2009): 3307--3329, arxiv:0909.1157
Marc Hallin, Davy Paindaveine, and Miroslav Siman, "Multivariate quantiles and multiple-output regression quantiles: From L1 optimization to halfspace depth", Annals of Statistics 38 (2010): 635--669 [with discussion]
Wolfgang Härdle, Marlene Müller, Stefan Sperlich and Axel Werwatz, Nonparametric and Semiparametric Models: An Introduction [Full text online]
Matthew T. Harrison, "Valid p-Values using Importance Sampling", arxiv:104.2910
Heng Lian, "Empirical Likelihood Confidence Intervals for Nonparametric Functional Data Analysis", arxiv:0904.0843
David A. Hensher et al. Applied Choice Analysis: A Primer ["Application of quantitative statistical methods to study choices made by individuals. This primer provides an introduction to the main techniques of choice analysis and also includes details on data collection and preparation, model estimation and interpretation and the design of choice experiments". Full blurb]
Tim Hesterberg, Nam Hee Choi, Lukas Meier, Chris Fraley, "Least angle and $\ell_1$ penalized regression: A review", Statistics Surveys 2 (2008): 61--93, arxiv:0802.0964
Nils Lid Hjort, Ian W. McKeague, Ingrid Van Keilegom, "Extending the scope of empirical likelihood", arxiv:0904.2949 = Annals of Statistics 37 (2009): 1079--1111
Peter D. Hoff, "A hierarchical eigenmodel for pooled covariance estimation", Journal of the Royal Statistical Society B 71 (2009): 971--992
Marc Hoffmann and Richard Nickl, "On adaptive inference and confidence bands", Annals of Statistics 39 (2011): 2383--2409
Joel L. Horowitz, Semiparametric and Nonparametric Methods in Econometrics [blurb]
Serkan Hosten, Amit Khetan and Bernd Sturmfels, "Solving the Likelihood Equations", math.ST/0408270
Tzee-Ming Huang, "Testing conditional independence using maximal nonlinear conditional correlation", Annals of Statistics 38 (2010): 2047--2091
Peter J. Huber, "The behavior of maximum likelihood estimates under nonstandard conditions", Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1 (Univ. of Calif. Press, 1967), pp. 221-233
Mia Hubert, Peter J. Rousseeuw, Stefan Van Aelst, "High-Breakdown Robust Multivariate Methods", Statistical Science 23 (2008): 92--119, arxiv:0808.0657
Alexander Ilin, Tapani Raiko, "Practical Approaches to Principal Component Analysis in the Presence of Missing Values", Journal of Machine Learning Research 11 (2010): 1957--2000
Stefano M. Iacus and Davide La Torre
- "Approximating Distribution Functions by Iterated Function Systems," math.PR/0111152
- "Nonparametric estimation of distribution and density functions in presence of missing data: an IFS approach," math.PR/0302016
Ian H. Jermyn, "Invariant Bayesian estimation on manifolds", math.ST/0506296 = Annals of Statistics 33 (2005): 583--605
Adam M. Johansen, Arnaud Doucet and Manuel Davy, "Particle methods for maximum likelihood estimation in latent variable models", Statistics and Computing 18 (2008) : 47--57
Ana Justel, Daniel Pena, Ruben Zamar, "A multivariate Kolmogorov-Smirnov test of goodness of fit", Statistics and Probability Letters 35 (1997): 251--259 [PDF reprint via Prof. Pena]
Paul Kabaila and Kreshna Syuhada, "The Asymptotic Efficiency of Improved Prediction Intervals", arxiv:0901.1911
Ata Kaban, "Non-parametric detection of meaningless distances in high dimensional data", Statistics and Computing 22 (2011): 375--385
Sham Kakade, Ohad Shamir, Karthik Sindharan, Ambuj Tewari, "Learning Exponential Families in High-Dimensions: Strong Convexity and Sparsity", Journal of Machine Learning Research Proceedings 9 (2010): 381--388
Oscar Kempthorne, "The classical problem of inference--goodness of fit", Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1, 235--249
D. F. Kerridge, "Inaccuracy and Inference", Journal of the Royal Statistical Society B 23 (1961): 184--194
Yuichi Kitamura, "Empirical likelihood methods in econometrics: Theory and Practice", Cowles Foundation Discussion Paper No. 1569 (2006)
Ioannis Kontoyiannis and S. P. Meyn, "Computable exponential bounds for screened estimation and simulation", Annals of Applied Probability 18 (2008): 1491--1518, arxiv:math/0612040
Mikhail Kovtun, Igor Akushevich, Kenneth G. Manton and H. Dennis Tolley
- "Linear Latent Structure Analysis: Mixture Distribution Models with Linear Constraints", math.PR/0507025
- "A New Efficient Algorithm for Construction of LLS Models", math.PR/0507021
Jim Kuelbs and Anand N. Vidyashankar, "Asymptotic inference for high-dimensional data", Annals of Statistics 38 (2010): 836--869
Solomon Kullback, "Probability densities with given marginals," Annals of Mathematical Statistics 39 (1968): 1236--1243
Masayuki Kumon and Akimichi Takemura, "On a simple strategy weakly forcing the strong law of large numbers in the bounded forecasting game", math.PR/0508190 ["In the framework of the game-theoretic probability of Shafer and Vovk (2001) ... construct an explicit strategy weakly forcing the strong law of large numbers (SLLN) in the bounded forecasting game. ... simple finite-memory strategy based on the past average of Reality's moves, which weakly forces the strong law of large numbers with the convergence rate of $O(\sqrt{\log n/n})$.... We show that if Reality violates SLLN, then the exponential growth rate of Skeptic's capital process is explicitly described in terms of the Kullback divergence between the average of Reality's moves when she violates SLLN and the average when she observes SLLN."]
Tze Leung Lai, Shulamith T. Gross, and David Bo Shen, "Evaluating probability forecasts", Annals of Statistics 39 (2011): 2356--2382, arxiv:1202.5140
Mikhail Langovoy, "Data-driven goodness-of-fit tests", arxiv:0708.0169
Youngjo Lee and John A. Nelder, "Likelihood Inference for Models with Unobservables: Another View", Statistical Science 24 (2009): 255--269, arxiv:1010.0303 [with discussion and replies following]
E. L. Lehmann and Joseph P. Romano, "Generalizations of the Familywise Error Rate", math.ST/0507420 = Annals of Statistics 33 (2005): 1138--1154
Matthieu Lerasle, "Adaptive non-asymptotic confidence balls in density estimation", arxiv:1007.4528
M. Lerasle, R. I. Oliveira, "Robust Empirical Mean Estimators", arxiv:1112.3914
Feng Liang, Sayan Mukherjee, Mike West, "The Use of Unlabeled Data in Predictive Modeling", arxiv:0710.4618 = Statistical Science 22 (2007): 189--205
Perry Liang, Francis Bach, Guillaume Bouchard and Michael I. Jordan, "Asymptotically Optimal Regularization in Smooth Parametric Models" [PDF preprint via Prof. Jordan]
Victor S. L'vov, Anna Pomyalov and Itamar Procaccia, "Outliers, Extreme Events and Multiscaling," nlin.CD/0009049
Christian K. Machens, "Adaptive sampling by information maximization," physics/0112070
Robert Mariano, Til Schuermann and Melyvn J. Weeks (eds.), Simulation-Based Inference in Econometrics: Methods and Applications
McCabe and Tremayne, Modern Asymptotic Theory
P. McCullagh
- Tensor Methods in Statistics
- "What is a statistical model?", Annals of Statistics 30 (2002): 1225--1310 [With commentary and rejoinder. Not sure, from a superficial glance, if there's really anything to this or it's just what one of my mentors calls "algebraic noodling".]
Nicolai Meinshausen and John Rice, "Estimating the proportion of false null hypotheses among a large number of independently tested hypotheses", math.ST/0501289
Alexander Meister, Deconvolution Problems in Nonparametric Statistics ["e.g., density estimation based on contaminated data, errors-in-variables regression, and image reconstruction". Blurb]
Vladimir N. Minin, John D. O'Brien, Arseni Seregin, "Empirically corrected estimation of complete-data population summaries under model misspecification", arxiv:0911.0930
David Mumford and Agnes Desolneux, Pattern Theory: The Stochastic Analysis of Real-World Signals
Richard Nickl, "Donsker-type theorems for nonparametric maximum likelihood estimators", Probability Theory and Related Fields 138 (2007): 411--449
Andrey Novikov, "Sequential multiple hypothesis testing in presence of control variables", Kybernetika 45 (2009): 507--528, arxiv:0812.2712
Wojciech Olszewski, Alvaro Sandroni, "A nonmanipulable test", arxiv:0904.0338 = Annals of Statistics 37 (2009): 1013--1039
Giulio Palombo, "Multivariate Goodness of Fit Procedures for Unbinned Data: An Annotated Bibliography", arxiv:1102.2407
Leandro Pardo, Statistical Inference Based on Divergence Measures
Donald A. Pierce and Dawn Peters, "Improving on exact tests by approximate conditioning", Biometrika 86 (1999): 265--277 [Via Andrew Gelman's blog; PDF reprint]
Tomaz Podobnik and Tomi Zivko, "On Consistent and Calibrated Inference about the Parameters of Sampling Distributions", physics/0508017
Thorsten Poeschel, Werner Ebeling, and Helge Rose, "Guessing probability distributions from small samples," cond-mat/0203467 = Journal of Statistical Physics 80 (1995): 1443
David Pollard, "Some thoughts on Le Cam's statistical decision theory", arxiv:1107.3811
Benedikt M. Pötscher, "Confidence Sets Based on Sparse Estimators are Necessarily Large", arxiv:0711.1036
Joel Predd, Robert Seiringer, Elliott H. Lieb, Daniel Osherson, Vincent Poor, Sanjeev Kulkarni, "Probabilistic coherence and proper scoring rules", IEEE Transactions on Information Theory 55 (2009): 4786, arxiv:0710.3183
Ramsay and Silverman, Functional Data Analysis
C. Radhakrishna Rao, "Diversity: Its measurement, decomposition, apportionment and analysis", Sankhya: The Indian Journal of Statistics 44(A) (1982): 1--22 [Sankhya is not in JSTOR! Why is Sankhya not in JSTOR?!?!]
J. C. W. Rayner and D. J. Best, Smooth Tests of Goodness of Fit
R.-D. Reiss and M. Thomas, Statistical Analysis of Extreme Values: With Applications to Insurance, Finance, Hydrology and Other Fields
Irina Rish, Sparse Modeling; Theory, Algorithms, and Applications
James Robins and Aad van der Vaart, "Adaptive nonparametric confidence sets", Annals of Statistics 34 (2006): 229--253, arxiv:math/0605473 ["We construct honest confidence regions for a Hilbert space-valued parameter in various statistical models. The confidence sets can be centered at arbitrary adaptive estimators, and have diameter which adapts optimally to a given selection of models."]
Sylvain Rubenthaler, Tobias Ryden and Magnus Wiktorsson, "Fast simulated annealing in $\R^d$ and an application to maximum likelihood estimation", math.PR/0609353
Birgit Rudloff, Ioannis Karatzas, "Testing composite hypotheses via convex duality", Bernoulli 16 (2010): 1224--1239, arxiv:0809.4297
Emilio Seijo and Bodhisattva Sen, "A continuous mapping theorem for the smallest argmax functional", Electronic Journal of Statistics 5 (2011): 421--439
Galit Shmueli, "To Explain or to Predict?", Statistical Science 25 (2010): 289--310, arxiv:1101.0891
Ricardo Silva, Robert B. Gramacy, "Gaussian Process Structural Equation Models with Latent Variables", arxiv:1002.4802 [Heard the talk at UAI 2010, but I want the details.]
Kesar Singh, Minge Xie, William E. Strawderman, "Confidence distribution (CD) -- distribution estimator of a parameter", pp. 132--150 in Regina Liu, William Strawderman and Cun-Hui Zhang (eds.), Complex Datasets and Inverse Problems: Tomography, Networks and Beyond
Jascha Sohl-Dickstein, Peter Battaglino, Michael R. DeWeese, "Minimum Probability Flow Learning", arxiv:0906.4779
Christopher G. Small, The Statistical Theory of Shape
Aris Spanos
- "Revisiting the Omitted Variables Argument: Substantive vs. Statistical Adequacy" [PDF preprint]
- "Is Frequentist Testing Vulnerable to the Base-Rate Fallacy?", Philosophy of Science 77 (2010): 565--583
Vladimir Spokoiny
- "A penalized exponential risk bound in parametric estimation", arxiv:0903.1721
- "Parametric estimation. Finite sample theory", arxiv:1111.3029
Pablo Sprechmann, Ignacio Ramírez, Guillermo Sapiro, Yonina Eldar, "C-HiLasso: A Collaborative Hierarchical Sparse Modeling Framework", arxiv:1006.1346
Johan A.K. Suykens, Carlos Alzate, and Kristiaan Pelckmans, "Primal and dual model representations in kernel-based learning", Statistics Surveys 4 (2010): 148--183
Gábor J. Székely and Maria L. Rizzo, "Brownian Distance Covariance", Annals of Applied Statistics 3 (2009): 1236--1265, arxiv:1010.0297
Olivier Thas, Comparing Distributions [mostly about goodness-of-fit tests]
F. V. Tkachov, "Quasi-optimal observables: Attaining the quality of maximal likelihood in parameter estimation when only a MC event generator is available," physics/0108030
Mark J. van der Laan and Sherri Rose, Targeted Learning: Causal Inference for Observational and Experimental Data [Blurb]
Guenther Walther, "The Average Likelihood Ratio for Large-scale Multiple Testing and Detecting Sparse Mixtures", arxiv:1111.0328
Xiaogang Wang and James V. Zidek, "Selecting likelihood weights by cross-validation", math.ST/0505599 = Annals of Statistics 33 (2005): 463--500 ["The (relevance) weighted likelihood was introduced to formally embrace a variety of statistical procedures that trade bias for precision. Unlike its classical counterpart, the weighted likelihood combines all relevant information while inheriting many of its desirable features including good asymptotic properties. However, in order to be effective, the weights involved in its construction need to be judiciously chosen. Choosing those weights is the subject of this article in which we demonstrate the use of cross-validation. We prove the resulting weighted likelihood estimator (WLE) to be weakly consistent and asymptotically normal. An application to disease mapping data is demonstrated." Sounds interesting...]
Fabian L. Wauthier, Michael I. Jordan, "Heavy-Tailed Processes for Selective Shrinkage", arxiv:1006.3901
Holger Wendland, Scattered Data Approximation
Halbert White, Asymptotic Theory for Econometricians [Useful source, it seems, for non-IID central limit theorems]
Christopher K. I. Williams, "How to Pretend That Correlated Variables Are Independent by Using Difference Observations", Neural Computation 17 (2005): 1--6 ["In many areas of data modeling, observations at different locations (e.g., time frames or pixel locations) are augmented by differences of nearby observations.... These augmented observations are then often modeled as being independent. How can this make sense? We provide two interpretations, showing (1) that the likelihood of data generated from an autoregressive process can be computed in terms of 'independent' augmented observations and (2) that the augmented observations can be given a coherent treatment in terms of the products of experts model..."]
Wei Biao Wu, "On false discovery control under dependence", Annals of Statistics 36 (2008): 364--380, arxiv:0903.1971

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