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All of Statistics: A Concise Course in Statistical Inference
This book covers a much wider range of topics than a typical introductory text on mathematical statistics, and includes modern topics like nonparametric curve estimation, bootstrapping and classification, topics that are usually relegated to follow-up courses.
A Reference Bayesian Test for Nested Hypotheses and its Relationship to the Schwarz Criterion
Abstract To compute a Bayes factor for testing H 0: ψ = ψ0 in the presence of a nuisance parameter β, priors under the null and alternative hypotheses must be chosen. As in Bayesian estimation, an
Operating characteristics and extensions of the false discovery rate procedure
Summary. We investigate the operating characteristics of the Benjamini–Hochberg false discovery rate procedure for multiple testing. This is a distribution‐free method that controls the expected
The Selection of Prior Distributions by Formal Rules
Abstract Subjectivism has become the dominant philosophical foundation for Bayesian inference. Yet in practice, most Bayesian analyses are performed with so-called “noninformative” priors, that is,
The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs
A method is derived for estimating the nonparanormal, the method's theoretical properties are studied, and it is shown that it works well in many examples.
Sparse additive models
An algorithm for fitting the models is derived that is practical and effective even when the number of covariates is larger than the sample size, and empirical results show that they can be effective in fitting sparse non‐parametric models in high dimensional data.
All of statistics
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High Dimensional Semiparametric Gaussian Copula Graphical Models
It is proved that the nonparanormal skeptic achieves the optimal parametric rates of convergence for both graph recovery and parameter estimation, and this result suggests that the NonParanormal graphical models can be used as a safe replacement of the popular Gaussian graphical models, even when the data are truly Gaussian.
A stochastic process approach to false discovery control
This paper extends the theory of false discovery rates (FDR) pioneered by Benjamini and Hochberg [J. Roy. Statist. Soc. Ser B 57 (1995) 289-300]. We develop a framework in which the False Discovery
All of Nonparametric Statistics
This text provides the reader with a single book where they can find accounts of a number of up-to-date issues in nonparametric inference. The book is aimed at Masters or PhD level students in