LEADER 05827nam 2200745Ia 450 001 9910830159603321 005 20170919194609.0 010 $a1-282-30740-1 010 $a9786612307409 010 $a0-470-31685-3 010 $a0-470-31769-8 035 $a(CKB)1000000000687543 035 $a(EBL)468694 035 $a(OCoLC)264621195 035 $a(SSID)ssj0000665019 035 $a(PQKBManifestationID)12259379 035 $a(PQKBTitleCode)TC0000665019 035 $a(PQKBWorkID)10631148 035 $a(PQKB)10221802 035 $a(SSID)ssj0000344198 035 $a(PQKBManifestationID)11275588 035 $a(PQKBTitleCode)TC0000344198 035 $a(PQKBWorkID)10306765 035 $a(PQKB)10658547 035 $a(MiAaPQ)EBC468694 035 $a(PPN)159339294 035 $a(EXLCZ)991000000000687543 100 $a19910516d1992 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aVariance components$b[electronic resource] /$fShayle R. Searle, George Casella, Charles E. McCulloch 210 $aNew York $cWiley$dc1992 215 $a1 online resource (537 p.) 225 1 $aWiley series in probability and mathematical statistics. Applied probability and statistics 300 $a"A Wiley-Interscience publication." 311 $a0-470-00959-4 311 $a0-471-62162-5 320 $aIncludes bibliographical references (p. 475-489) and indexes. 327 $aVariance Components; CONTENTS; 1. Introduction; 1.1. Factors, levels, cells and effects; 1.2. Balanced and unbalanced data; a. Balanced data; b. Special cases of unbalanced data; -i. Planned unbalancedness; -ii. Estimating missing observations; c. Unbalanced data; 1.3. Fixed effects and random effects; a. Fixed effects models; Example 1 (Tomato varieties); Example 2 (Medications); Example 3 (Soils and, fertilizers); b. Random eflecrs models; Example 4 (Clinics); Example 5 (Dairy bulls); Example 6 (Ball bearings and calipers); c. Mixed models; Example 7 (Medications and clinics) 327 $aExample 8 ( Varieties and gardens)1.4. Fixed or random?; Example 9 (Mice and technicians); 1.5. Finite populations; 1.6. Summary; a. Characteristics of the fixed effects model and the random eflects model for the I-way classification; b. Examples; c. Fixed or random; 2. History and Comment; 2.1. Analysis of variance; 2.2. Early years: 1861-1949; a. Sources; b. Pre-1900; C. 1900-1939; -i. R. A. Fisher; -ii. L. C. Tippett; -iii. The late 1930s; -iv. Unbalanced data; d. The 1940s; 2.3. Great strides: 1950-1969; a. The Henderson methods; b. ANOVA estimation, in general; -i. Negative estimates 327 $a-ii. Unhiasedness-iii. Best unbiasedness; -iv. Minimal sujicient statistics; -v. Lack of uniqueness; 2.4 Into the 1970s and beyond; a. Maximum likelihood (M L); b. Restricted maximum likelihood (REML); c. Minimum norm estimation; d. The dispersion-mean model; e. Bayes estimation; f: The recent decade; 3. The I-Way Classification; 3.1. The model; a. The model equation; b. First moments; c. Second moments; 3.2. Matrix formulation of the model; a. Example 1; b. The general case; c. Dispersion matrices; -i. The traditional random model; -ii. Other alternatives; d. Unbalanced data; -i. Example 2 327 $a-ii. The general case-iii. Dispersion matrix; 3.3 Estimating the mean; 3.4 Predicting random effects; 3.5 ANOVA estimation-balanced data; a. Expected sums of squares; -i. A direct derivation; -ii. Using the matrix formulation; b. ANOVA estimators; c. Negative estimates; d. Normality assumptions; -i. X2-distributions of sums of squares; -ii. Independence of sums of squares; -iii. Sampling variances of estimators; -iv. An F-statistic to test H:? 2/a =0; -v. Confidence intervals; -vi. Probability of a negative estimate; -vii. Distribution of estimators; 3.6 ANOVA estimation-unbalanced data 327 $aa. Expected sums of squares-i. A direct derivation; -ii. Using the matrix formulation; b. ANOVA estimators; c. Negative estimates; d. Normality assumptions; -i. X2-distributions of sums oj squares; -ii. Independence of sums of squares; -iii. Sampling variances of estimators; -iv. The eflect of unhalancedness on sampling variances; -v. F-statistics; -vi. Confidence intervals; 3.7. Maximum likelihood estimation; a. Balanced data; -i. Likelihood; -ii. M L equations and their solutions; -iii. ML estimators; -iv. Expected values and bias; -v. Sampling variances; b. Unbalanced data; -i. Likelihood 327 $a-ii. ML equations and their solutions 330 $aWILEY-INTERSCIENCE PAPERBACK SERIES The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. "". . .Variance Components is an excellent book. It is organized and well written, and provides many references to a variety of topics. I recommend it to anyone with inte 410 0$aWiley series in probability and mathematical statistics.$pApplied probability and statistics. 606 $aAnalysis of variance 606 $aMathematical statistics 615 0$aAnalysis of variance. 615 0$aMathematical statistics. 676 $a519.5 676 $a519.538 700 $aSearle$b S. R$g(Shayle R.),$f1928-$0105121 701 $aCasella$b George$027435 701 $aMcCulloch$b Charles E$089127 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910830159603321 996 $aVariance components$91130889 997 $aUNINA