000			03962nam a22004575i 4500
001			978-0-387-29053-9
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005			20160614135112.0
007			cr nn 008mamaa
008			100301s2005 xxu| s |||| 0|eng d
020			_a9780387290539 _9978-0-387-29053-9
024	7		_a10.1007/0-387-29053-2 _2doi
049			_aAlfaisal Main Library
050		4	_aQA276-280
072		7	_aPBT _2bicssc
072		7	_aMAT029000 _2bisacsh
082	0	4	_a519.5 _223
100	1		_aZhu, Lixing. _eauthor.
245	1	0	_aNonparametric Monte Carlo Tests and Their Applications _h[electronic resource] / _cby Lixing Zhu.
264		1	_aNew York, NY : _bSpringer New York, _c2005.
300			_aXI, 184 p. 17 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aLecture Notes in Statistics, _x0930-0325 ; _v182
505	0		_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.
520			_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.>.
650		0	_aStatistics.
650	1	4	_aStatistics.
650	2	4	_aStatistical Theory and Methods.
655		7	_aElectronic books. _2local
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780387250380
830		0	_aLecture Notes in Statistics, _x0930-0325 ; _v182
856	4	0	_uhttp://ezproxy.alfaisal.edu/login?url=http://dx.doi.org/10.1007/0-387-29053-2
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