LEADER 05487nam 2200661 450 001 9910143186203321 005 20170810191546.0 010 $a1-280-27278-3 010 $a9786610272785 010 $a0-470-34244-7 010 $a0-471-65404-3 010 $a0-471-72207-3 035 $a(CKB)111087027110022 035 $a(EBL)221340 035 $a(OCoLC)76960784 035 $a(SSID)ssj0000161447 035 $a(PQKBManifestationID)11155008 035 $a(PQKBTitleCode)TC0000161447 035 $a(PQKBWorkID)10198889 035 $a(PQKB)10273166 035 $a(MiAaPQ)EBC221340 035 $a(EXLCZ)99111087027110022 100 $a20160816h20012001 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aGeneralized, linear, and mixed models /$fCharles E. McCulloch, Shayle R. Searle 210 1$aNew York, [New York] :$cJohn Wiley & Sons, Ltd,$d2001. 210 4$dİ2001 215 $a1 online resource (358 p.) 225 1 $aWiley Series in Probability and Statistics., Texts, References, and Pocketbooks Section 300 $aDescription based upon print version of record. 311 $a0-471-19364-X 320 $aIncludes bibliographical references and index. 327 $aCONTENTS; 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 calipers 327 $a1.5 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. Prediction 327 $a1.8 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 estimation 327 $ac. 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, ?; ii. For ?[sup(2)]; iii. For ?[sup(2)][sub(a)]; b. Predicting random effects; i. A basic result; ii. In a 1-way classification 327 $a2.5 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 likelihood 327 $aiii. Means, variances, and covariances 330 $aWiley Series in Probability and StatisticsA modern perspective on mixed modelsThe availability of powerful computing methods in recent decades has thrust linear and nonlinear mixed models into the mainstream of statistical application. This volume offers a modern perspective on generalized, linear, and mixed models, presenting a unified and accessible treatment of the newest statistical methods for analyzing correlated, nonnormally distributed data.As a follow-up to Searle's classic, Linear Models, and Variance Components by Searle, Casella, and McCulloch, this new work progresses 410 0$aWiley series in probability and statistics.$pTexts, references, and pocketbooks section. 606 $aLinear models (Statistics) 608 $aElectronic books. 615 0$aLinear models (Statistics) 676 $a519.5 676 $a519.535 700 $aMcCulloch$b Charles E.$089127 702 $aSearle$b S. R$g(Shayle R.),$f1928- 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910143186203321 996 $aGeneralized, linear, and mixed models$91950615 997 $aUNINA