Nonparametric Statistical Methods 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
Nonparametric Statistical Methods
Praise for the Second Edition “This book should be an essential part of the personal library of every practicing statistician.”—Technometrics Thoroughly revised and updated, the new edition of Nonparametric Statistical Methods includes additional modern topics and procedures, more practical data sets, and new problems from real-life situations. The book continues to emphasize the importance of nonparametric methods as a significant branch of modern statistics and equips readers with the conceptual and technical skills necessary to select and apply the appropriate procedures for any given situation. Written by leading statisticians, Nonparametric Statistical Methods, Third Edition provides readers with crucial nonparametric techniques in a variety of settings, emphasizing the assumptions underlying the methods. The book provides an extensive array of examples that clearly illustrate how to use nonparametric approaches for handling one- or two-sample location and dispersion problems, dichotomous data, and one-way and two-way layout problems. In addition, the Third Edition features: The use of the freely available R software to aid in computation and simulation, including many new R programs written explicitly for this new edition New chapters that address density estimation, wavelets, smoothing, ranked set sampling, and Bayesian nonparametrics Problems that illustrate examples from agricultural science, astronomy, biology, criminology, education, engineering, environmental science, geology, home economics, medicine, oceanography, physics, psychology, sociology, and space science Nonparametric Statistical Methods, Third Edition is an excellent reference for applied statisticians and practitioners who seek a review of nonparametric methods and their relevant applications. The book is also an ideal textbook for upper-undergraduate and first-year graduate courses in applied nonparametric statistics.
Nonparametric Methods in Change Point Problems
Author: E. Brodsky
language: en
Publisher: Springer Science & Business Media
Release Date: 1993-01-31
The explosive development of information science and technology puts in new problems involving statistical data analysis. These problems result from higher re quirements concerning the reliability of statistical decisions, the accuracy of math ematical models and the quality of control in complex systems. A new aspect of statistical analysis has emerged, closely connected with one of the basic questions of cynergetics: how to "compress" large volumes of experimental data in order to extract the most valuable information from data observed. De tection of large "homogeneous" segments of data enables one to identify "hidden" regularities in an object's behavior, to create mathematical models for each seg ment of homogeneity, to choose an appropriate control, etc. Statistical methods dealing with the detection of changes in the characteristics of random processes can be of great use in all these problems. These methods have accompanied the rapid growth in data beginning from the middle of our century. According to a tradition of more than thirty years, we call this sphere of statistical analysis the "theory of change-point detection. " During the last fifteen years, we have witnessed many exciting developments in the theory of change-point detection. New promising directions of research have emerged, and traditional trends have flourished anew. Despite this, most of the results are widely scattered in the literature and few monographs exist. A real need has arisen for up-to-date books which present an account of important current research trends, one of which is the theory of non parametric change--point detection.