Nonparametric Methods In Statistics And Related Topics
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Nonparametric Methods in Statistics and Related Topics
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Nonparametric Statistical Methods and Related Topics
Review papers. 1. On the scholarly work of P.K. Bhattacharya / P. Hall and F.J. Samaniego. 2. The propensity score and its role in causal inference / C. Drake and T. Loux. 3. Recent tests for symmetry with multivariate and structured data: a review / S.G. Meintanis and J. Ngatchou-Wandji -- Papers on general nonparametric inference. 4. On robust versions of classical tests with dependent data / J. Jiang. 5. Density estimation by sampling from stationary continuous time parameter associated processes / G.G. Roussas and D. Bhattacharya. 6. A Short proof of the Feigin-Tweedie theorem on the existence of the mean functional of a Dirichlet process / J. Sethuraman. 7. Max-min Bernstein polynomial estimation of a discontinuity in distribution / K.-S. Song. 8. U-statistics based on higher-order spacings / D.D. Tung and S.R. Jammalamadaka. 9. Nonparametric models for non-Gaussian longitudinal data / N. Zhang, H.-G. Muller and J.-L. Wang -- Papers on aspects of linear or generalized linear models. 10. Better residuals / R. Beran. 11. The use of Peters-Belson regression in legal cases / E. Bura, J.L. Gastwirth and H. Hikawa. 12. On a hybrid approach to parametric and nonparametric regression / P. Burman and P. Chaudhuri. 13. Nonparametric regression models with integrated covariates / Z. Cai. 14. A dynamic test for misspecification of a linear model / M.P. McAssey and F. Hsieh. 15. The principal component decomposition of the basic martingale / W. Stute -- Papers on time series analysis. 16. Fast scatterplot smoothing using blockwise least squares fitting / A. Aue and T.C.M. Lee. 17. Some recent advances in semiparametric estimation of the GARCH model / J. Di and A. Gangopadhyay. 18. Extreme dependence in multivariate time series: a review / R. Sen and Z. Tan. 19. Dynamic mixed models for irregularly observed water quality data / R.H. Shumway -- Papers on asymptotic theory. 20. Asymptotic behavior of the kernel density estimators for nonstationary dependent random variables with binned data / J.-F. Lenain, M. Harel and M.L. Puri. 21. Convergence rates of an improved isotonic regression estimator / H. Mukerjee. 22. Asymptotic distribution of the smallest eigenvalue of Wishart(N, n) When N, n ' [symbol] such that N/n --> 0 / D. Paul
All of Nonparametric Statistics
Author: Larry Wasserman
language: en
Publisher: Springer Science & Business Media
Release Date: 2006-09-10
There are many books on various aspects of nonparametric inference such as density estimation, nonparametric regression, bootstrapping, and wavelets methods. But it is hard to ?nd all these topics covered in one place. The goal of this text is to provide readers with a single book where they can ?nd a brief account of many of the modern topics in nonparametric inference. The book is aimed at master’s-level or Ph. D. -level statistics and computer science students. It is also suitable for researchersin statistics, machine lea- ing and data mining who want to get up to speed quickly on modern n- parametric methods. My goal is to quickly acquaint the reader with the basic concepts in many areas rather than tackling any one topic in great detail. In the interest of covering a wide range of topics, while keeping the book short, I have opted to omit most proofs. Bibliographic remarks point the reader to references that contain further details. Of course, I have had to choose topics to include andto omit,the title notwithstanding. For the mostpart,I decided to omit topics that are too big to cover in one chapter. For example, I do not cover classi?cation or nonparametric Bayesian inference. The book developed from my lecture notes for a half-semester (20 hours) course populated mainly by master’s-level students. For Ph. D.