03909nam a22004215i 4500001001800000003000900018005001700027007001500044008004100059020001800100020001900118024003100137040000900168082001400177100002600191245010200217264004600319300004400365336002600409337002600435338003900461347002400500490005100524505047200575520197501047650001603022650002903038650001603067650003603083710003403119773002003153776003603173830005103209856009903260942001203359999001703371952009903388978-0-387-29053-9DE-He21320260521091851.0cr nn 008mamaa100301s2005    xxu|    s    |||| 0|eng d  a9780387290539  a997803872905397 a10.1007/0-387-29053-22doi  cCICY04a519.52231 aZhu, Lixing.eauthor.10aNonparametric Monte Carlo Tests and Their Applicationsh[recurso electrónico] /cby Lixing Zhu. 1aNew York, NY :bSpringer New York,c2005.  aXII, 184 p. 15 illus.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  arecurso en líneabcr2rdacarrier  atext filebPDF2rda1 aLecture Notes in Statistics,x0930-0325 ;v1820 aMonte Carlo Tests -- Testing for Multivariate Distributions -- Asymptotics of Goodness-of-fit Tests for Symmetry -- A Test of Dimension-Reduction Type for Regressions -- Checking the Adequacy of a Partially Linear Model -- Model Checking for Multivariate Regression Models -- Heteroscedasticity Tests for Regressions -- Checking the Adequacy of a Varying-Coefficients Model -- On the Mean Residual Life Regression Model -- Homegeneity Testing for Covariance Matrices.  aA fundamental issue in statistical analysis is testing the fit of a particular probability model to a set of observed data. Monte Carlo approximation to the null distribution of the test provides a convenient and powerful means of testing model fit. Nonparametric Monte Carlo Tests and Their Applications proposes a new Monte Carlo-based methodology to construct this type of approximation when the model is semistructured. When there are no nuisance parameters to be estimated, the nonparametric Monte Carlo test can exactly maintain the significance level, and when nuisance parameters exist, this method can allow the test to asymptotically maintain the level. The author addresses both applied and theoretical aspects of nonparametric Monte Carlo tests. The new methodology has been used for model checking in many fields of statistics, such as multivariate distribution theory, parametric and semiparametric regression models, multivariate regression models, varying-coefficient models with longitudinal data, heteroscedasticity, and homogeneity of covariance matrices. This book will be of interest to both practitioners and researchers investigating goodness-of-fit tests and resampling approximations. Every chapter of the book includes algorithms, simulations, and theoretical deductions. The prerequisites for a full appreciation of the book are a modest knowledge of mathematical statistics and limit theorems in probability/empirical process theory. The less mathematically sophisticated reader will find Chapters 1, 2 and 6 to be a comprehensible introduction on how and where the new method can apply and the rest of the book to be a valuable reference for Monte Carlo test approximation and goodness-of-fit tests. Lixing Zhu is Associate Professor of Statistics at the University of Hong Kong. He is a winner of the Humboldt Research Award at Alexander-von Humboldt Foundation of Germany and an elected Fellow of the Institute of Mathematical Statistics.> 0aSTATISTICS. 0aMATHEMATICAL STATISTICS.14aSTATISTICS.24aSTATISTICAL THEORY AND METHODS.2 aSpringerLink (Online service)0 tSpringer eBooks08iPrinted edition:z9780387250380 0aLecture Notes in Statistics,x0930-0325 ;v18240uhttp://dx.doi.org/10.1007/0-387-29053-2zVer el texto completo en las instalaciones del CICY  2ddccER  c32846d32846  00102ddc40708LEaCICYbCICYcELd2025-07-10l0o519.5r2025-07-10 08:39:43w2025-07-10yER