Download or read book Testing for No Effect when Estimating a Smooth Function by Nonparametric Regression written by Jonathan Alan Raz and published by . This book was released on 1988 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Nonparametric Smoothing and Lack of Fit Tests written by Jeffrey Hart and published by Springer. This book was released on 2012-11-28 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: An exploration of the use of smoothing methods in testing the fit of parametric regression models. The book reviews many of the existing methods for testing lack-of-fit and also proposes a number of new methods, addressing both applied and theoretical aspects of the model checking problems. As such, the book is of interest to practitioners of statistics and researchers investigating either lack-of-fit tests or nonparametric smoothing ideas. The first four chapters introduce the problem of estimating regression functions by nonparametric smoothers, primarily those of kernel and Fourier series type, and could be used as the foundation for a graduate level course on nonparametric function estimation. The prerequisites for a full appreciation of the book are a modest knowledge of calculus and some familiarity with the basics of mathematical statistics.
Download or read book Nonparametric Smoothing and Lack of Fit Tests written by Jeffrey Hart and published by Springer Science & Business Media. This book was released on 2013-03-14 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: An exploration of the use of smoothing methods in testing the fit of parametric regression models. The book reviews many of the existing methods for testing lack-of-fit and also proposes a number of new methods, addressing both applied and theoretical aspects of the model checking problems. As such, the book is of interest to practitioners of statistics and researchers investigating either lack-of-fit tests or nonparametric smoothing ideas. The first four chapters introduce the problem of estimating regression functions by nonparametric smoothers, primarily those of kernel and Fourier series type, and could be used as the foundation for a graduate level course on nonparametric function estimation. The prerequisites for a full appreciation of the book are a modest knowledge of calculus and some familiarity with the basics of mathematical statistics.
Download or read book Smoothing Methods in Statistics written by Jeffrey S. Simonoff and published by Springer Science & Business Media. This book was released on 1996-06-06 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: Focussing on applications, this book covers a very broad range, including simple and complex univariate and multivariate density estimation, nonparametric regression estimation, categorical data smoothing, and applications of smoothing to other areas of statistics. It will thus be of particular interest to data analysts, as arguments generally proceed from actual data rather than statistical theory, while the "Background Material" sections will interest statisticians studying the field. Over 750 references allow researchers to find the original sources for more details, and the "Computational Issues" sections provide sources for statistical software that use the methods discussed. Each chapter includes exercises with a heavily computational focus based upon the data sets used in the book, making it equally suitable as a textbook for a course in smoothing.
Download or read book Resampling Methods written by Phillip I. Good and published by Springer Science & Business Media. This book was released on 2013-03-14 with total page 281 pages. Available in PDF, EPUB and Kindle. Book excerpt: "...the author has packaged an excellent and modern set of topics around the development and use of quantitative models...the author has the capability to work at a more modest level. He does that very effectively in this 2nd Edition... If you need to learn about resampling, this book would be a good place to start." -- Technometrics This work is a practical, table-free introduction to data analysis using the bootstrap, cross-validation, and permutation tests; new to the second edition are several additional examples and a chapter dedicated to regression, data mining techniques, and their limitations. The book’s many exercises, practical data sets, and use of free shareware make it an essential resource for students and teachers, as well as industrial statisticians, consultants, and research professionals.
Download or read book Adequacy Checking for the Variance Function in Nonparametric Regression written by Jia Liang and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Correctly specifying the parametric form of the variance function in regression models can help us make more efficient statistical inferences. Many existing Lack-of-fit testing procedures have already been proposed to decide the proper forms of the variance function, however, most of them are either checking the homoscedasticity, that is, to see if the variance function is a constant, or checking a pre-specified parametric forms of the variance function under the assumption of the mean regression function being known. In this report, we would like to construct some formal testing procedure to check the appropriateness of certain parametric forms for the variance function when the mean regression function is unknown. The report consists of two parts. In the first part, we propose a minimum distance-based test to check the forms of the variance function. The test statistics is a modified L2-distance between a nonparametric estimate and a parametric estimate of the variance function under the null hypothesis. The Nadaraya-Watson kernel regression function estimator is used to estimate the regression function. The large sample properties, including the consistency and asymptotic normality, of the minimum distance estimate for the parameters in the variance function are discussed; the asymptotic distribution of the test statistics under the null hypothesis is established, as well as the consistency of the test and the power under local alternative hypotheses. Simulation studies, comparison studies, as well as some applications to the real data sets, are carried out to evaluate the finite sample performance of the proposed test. In the second part, we proposed a computationally efficient test procedure for checking the parametric forms of the variance function. The test is based on an empirical smoothing of the fitted residuals by replacing the mean regression function with the Nadaraya-Watson estimator and a pre-obtained root-n consistent estimate of the parameter in the variance function. By multiplying the kernel density estimate at each individual sample points to the fitted residual, we successfully remove the constraint of compact support for design variables assumed in some existing work. Large sample properties of the proposed test under the null hypothesis is discussed alongside with consistency of the test and the power under local alternatives. Finally, some simulation studies are carried out showing the performance of the test under finite population.
Download or read book Frontiers In Statistics written by Jianqing Fan and published by World Scientific. This book was released on 2006-07-17 with total page 552 pages. Available in PDF, EPUB and Kindle. Book excerpt: During the last two decades, many areas of statistical inference have experienced phenomenal growth. This book presents a timely analysis and overview of some of these new developments and a contemporary outlook on the various frontiers of statistics.Eminent leaders in the field have contributed 16 review articles and 6 research articles covering areas including semi-parametric models, data analytical nonparametric methods, statistical learning, network tomography, longitudinal data analysis, financial econometrics, time series, bootstrap and other re-sampling methodologies, statistical computing, generalized nonlinear regression and mixed effects models, martingale transform tests for model diagnostics, robust multivariate analysis, single index models and wavelets.This volume is dedicated to Prof. Peter J Bickel in honor of his 65th birthday. The first article of this volume summarizes some of Prof. Bickel's distinguished contributions.
Download or read book Local Regression and Likelihood written by Clive Loader and published by Springer Science & Business Media. This book was released on 2006-05-09 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: Separation of signal from noise is the most fundamental problem in data analysis, arising in such fields as: signal processing, econometrics, actuarial science, and geostatistics. This book introduces the local regression method in univariate and multivariate settings, with extensions to local likelihood and density estimation. Practical information is also included on how to implement these methods in the programs S-PLUS and LOCFIT.
Download or read book Permutation Tests written by Phillip Good and published by Springer Science & Business Media. This book was released on 2013-04-17 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: A step-by-step manual on the application of permutation tests in biology, business, medicine, science, and engineering. Its intuitive and informal style make it ideal for students and researchers, whether experienced or coming to these resampling methods for the first time. The real-world problems of missing and censored data, multiple comparisons, nonresponders, after-the-fact covariates, and outliers are all dealt with at length. This new edition has more than 100 additional pages, and includes streamlined statistics for the k-sample comparison and analysis of variance plus expanded sections on computational techniques, multiple comparisons, multiple regression, comparing variances, and testing interactions in balanced designs. The comprehensive author and subject indexes, plus an expert-system guide to methods, provide for further ease of use, while the exercises at the end of every chapter have been supplemented with drills and a number of graduate-level thesis problems.
Download or read book Journal of the American Statistical Association written by American Statistical Association and published by . This book was released on 1997 with total page 896 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Testing for the Equality of Two Autoregressive and Regression Functions written by Fang Li and published by . This book was released on 2004 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book When Does Bootstrap Work written by Enno Mammen and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 205 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Nonparametric Tests Using a Kernel Estimation Method written by Byung Joo Lee and published by . This book was released on 1988 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Testing for Goodness of Fit Using Nonparametric Techniques written by Maragatha N. Ramachandran and published by . This book was released on 1992 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Journal of Statistical Planning and Inference written by and published by . This book was released on 2003 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Order Restricted Statistical Inference written by Tim Robertson and published by John Wiley & Sons Incorporated. This book was released on 1988 with total page 521 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work attempts to provide a comprehensive treatment of the topic of statistical inference under inequality constraints, in which much of the theory is based on the principles ofr maximum likelihood estimation and likelihood ratio tests.
Download or read book Nonparametric Smoothing and Lack Of Fit Tests written by Jeffrey Hart and published by . This book was released on 2014-01-15 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt:
