05487nam 2200661 450 991014318620332120170810191546.01-280-27278-397866102727850-470-34244-70-471-65404-30-471-72207-3(CKB)111087027110022(EBL)221340(OCoLC)76960784(SSID)ssj0000161447(PQKBManifestationID)11155008(PQKBTitleCode)TC0000161447(PQKBWorkID)10198889(PQKB)10273166(MiAaPQ)EBC221340(EXLCZ)9911108702711002220160816h20012001 uy 0engur|n|---|||||txtccrGeneralized, linear, and mixed models /Charles E. McCulloch, Shayle R. SearleNew York, [New York] :John Wiley & Sons, Ltd,2001.©20011 online resource (358 p.)Wiley Series in Probability and Statistics., Texts, References, and Pocketbooks SectionDescription based upon print version of record.0-471-19364-X Includes bibliographical references and index.CONTENTS; PREFACE; 1 INTRODUCTION; 1.1 MODELS; a. Linear models (LM) and linear mixed models (LMM); b. Generalized models (GLMs and GLMMs); 1.2 FACTORS, LEVELS, CELLS, EFFECTS AND DATA; 1.3 FIXED EFFECTS MODELS; a. Example 1: Placebo and a drug; b. Example 2: Comprehension of humor; c. Example 3: Four dose levels of a drug; 1.4 RANDOM EFFECTS MODELS; a. Example 4: Clinics; b. Notation; i. Properties of random effects in LMMs; ii. The notation of mathematical statistics; iii. Variance of y; iv. Variance and conditional expected values; c. Example 5: Ball bearings and calipers1.5 LINEAR MIXED MODELS (LMMs)a. Example 6: Medications and clinics; b. Example 7: Drying methods and fabrics; c. Example 8: Potomac River Fever; d. Regression models; e. Longitudinal data; f. Model equations; 1.6 FIXED OR RANDOM?; a. Example 9: Clinic effects; b. Making a decision; 1.7 INFERENCE; a. Estimation; i. Maximum likelihood (ML); ii. Restricted maximum likelihood (REML); iii. Solutions and estimators; iv. Bayes theorem; v. Quasi-likelihood estimation; vi. Generalized estimating equations; b. Testing; i. Likelihood ratio test (LRT); ii. Wald's procedure; c. Prediction1.8 COMPUTER SOFTWARE1.9 EXERCISES; 2 ONE-WAY CLASSIFICATIONS; 2.1 NORMALITY AND FIXED EFFECTS; a. Model; b. Estimation by ML; c. Generalized likelihood ratio test; d. Confidence intervals; i. For means; ii. For differences in means; iii. For linear combinations; iv. For the variance; e. Hypothesis tests; 2.2 NORMALITY, RANDOM EFFECTS AND ML; a. Model; i. Covariances caused by random effects; ii. Likelihood; b. Balanced data; i. Likelihood; ii. ML equations and their solutions; iii. ML estimators; iv. Expected values and bias; v. Asymptotic sampling variances; vi. REML estimationc. Unbalanced datai. Likelihood; ii. ML equations and their solutions; iii. ML estimators; d. Bias; e. Sampling variances; 2.3 NORMALITY, RANDOM EFFECTS AND REML; a. Balanced data; i. Likelihood; ii. REML equations and their solutions; iii. REML estimators; iv. Comparison with ML; v. Bias; vi. Sampling variances; b. Unbalanced data; 2.4 MORE ON RANDOM EFFECTS AND NORMALITY; a. Tests and confidence intervals; i. For the overall mean, μ; ii. For σ[sup(2)]; iii. For σ[sup(2)][sub(a)]; b. Predicting random effects; i. A basic result; ii. In a 1-way classification2.5 BERNOULLI DATA: FIXED EFFECTSa. Model equation; b. Likelihood; c. ML equations and their solutions; d. Likelihood ratio test; e. The usual chi-square test; f. Large-sample tests and intervals; g. Exact tests and confidence intervals; h. Example: Snake strike data; 2.6 BERNOULLI DATA: RANDOM EFFECTS; a. Model equation; b. Beta-binomial model; i. Means, variances, and covariances; ii. Overdispersion; iii. Likelihood; iv. ML estimation; v. Large-sample variances; vi. Large-sample tests and intervals; vii. Prediction; c. Legit-normal model; i. Likelihood; ii. Calculation of the likelihoodiii. Means, variances, and covariancesWiley Series in Probability and StatisticsA modern perspective on mixed modelsThe availability of powerful computing methods in recent decades has thrust linear and nonlinear mixed models into the mainstream of statistical application. This volume offers a modern perspective on generalized, linear, and mixed models, presenting a unified and accessible treatment of the newest statistical methods for analyzing correlated, nonnormally distributed data.As a follow-up to Searle's classic, Linear Models, and Variance Components by Searle, Casella, and McCulloch, this new work progressesWiley series in probability and statistics.Texts, references, and pocketbooks section.Linear models (Statistics)Electronic books.Linear models (Statistics)519.5519.535McCulloch Charles E.89127Searle S. R(Shayle R.),1928-MiAaPQMiAaPQMiAaPQBOOK9910143186203321Generalized, linear, and mixed models1950615UNINA01013nam0 22002651i 450 UON0020176320231205103256.78888-15-02953-220030730d1991 |0itac50 baitaIT|||| |||||ˆLa ‰ fabbrica degli deisaggio sulle passioni individuali e collettiveSerge MoscoviciBolognaIl Mulino1991. 528 p. ; 22 cm.001UON000250842001 ˆLe ‰Occasioni37Sociologia collettivaUONC034738FIITBolognaUONL000085MoscoviciSergeUONV12055049432Il MulinoUONV245824650ITSOL20250919RICASIBA - SISTEMA BIBLIOTECARIO DI ATENEOUONSIUON00201763SIBA - SISTEMA BIBLIOTECARIO DI ATENEOSI A C 0255 SI SC 30828 5 0255 BuonoFabbrica degli dei811399UNIOR