LEADER 05367nam 22006734a 450 001 9911019154703321 005 20200520144314.0 010 $a9786611221577 010 $a9781281221575 010 $a1281221570 010 $a9780470183410 010 $a0470183411 010 $a9780470183403 010 $a0470183403 035 $a(CKB)1000000000377286 035 $a(EBL)331445 035 $a(OCoLC)437198702 035 $a(SSID)ssj0000204124 035 $a(PQKBManifestationID)11175454 035 $a(PQKBTitleCode)TC0000204124 035 $a(PQKBWorkID)10187891 035 $a(PQKB)10084169 035 $a(MiAaPQ)EBC331445 035 $a(Perlego)2771545 035 $a(EXLCZ)991000000000377286 100 $a20070330d2008 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aModels for probability and statistical inference $etheory and applications /$fJames H. Stapleton 210 $aHoboken, N.J. $cWiley-Interscience$dc2008 215 $a1 online resource (466 p.) 225 1 $aWiley series in probability and statistics 300 $aDescription based upon print version of record. 311 08$a9780470073728 311 08$a0470073721 320 $aIncludes bibliographical references (p. 378-380) and index. 327 $aModels for Probability and Statistical Inference; Contents; Preface; 1. Discrete Probability Models; 1.1. Introduction; 1.2. Sample Spaces, Events, and Probability Measures; 1.3. Conditional Probability and Independence; 1.4. Random Variables; 1.5. Expectation; 1.6. The Variance; 1.7. Covariance and Correlation; 2. Special Discrete Distributions; 2.1. Introduction; 2.2. The Binomial Distribution; 2.3. The Hypergeometric Distribution; 2.4. The Geometric and Negative Binomial Distributions; 2.5. The Poisson Distribution; 3. Continuous Random Variables; 3.1. Introduction 327 $a3.2. Continuous Random Variables3.3. Expected Values and Variances for Continuous Random Variables; 3.4. Transformations of Random Variables; 3.5. Joint Densities; 3.6. Distributions of Functions of Continuous Random Variables; 4. Special Continuous Distributions; 4.1. Introduction; 4.2. The Normal Distribution; 4.3. The Gamma Distribution; 5. Conditional Distributions; 5.1. Introduction; 5.2. Conditional Expectations for Discrete Random Variables; 5.3. Conditional Densities and Expectations for Continuous Random Variables; 6. Moment Generating Functions and Limit Theory; 6.1. Introduction 327 $a6.2. Moment Generating Functions6.3. Convergence in Probability and in Distribution and the Weak Law of Large Numbers; 6.4. The Central Limit Theorem; 7. Estimation; 7.1. Introduction; 7.2. Point Estimation; 7.3. The Method of Moments; 7.4. Maximum Likelihood; 7.5. Consistency; 7.6. The ?-Method; 7.7. Confidence Intervals; 7.8. Fisher Information, Crame?r-Rao Bound and Asymptotic Normality of MLEs; 7.9. Sufficiency; 8. Testing of Hypotheses; 8.1. Introduction; 8.2. The Neyman-Pearson Lemma; 8.3. The Likelihood Ratio Test 327 $a8.4. The p-Value and the Relationship between Tests of Hypotheses and Confidence Intervals9. The Multivariate Normal, Chi-Square, t, and F Distributions; 9.1. Introduction; 9.2. The Multivariate Normal Distribution; 9.3. The Central and Noncentral Chi-Square Distributions; 9.4. Student's t-Distribution; 9.5. The F-Distribution; 10. Nonparametric Statistics; 10.1. Introduction; 10.2. The Wilcoxon Test and Estimator; 10.3. One-Sample Methods; 10.4. The Kolmogorov-Smirnov Tests; 11. Linear Statistical Models; 11.1. Introduction; 11.2. The Principle of Least Squares; 11.3. Linear Models 327 $a11.4. F-Tests for H(0): ? = ?(1)X(1) + · · · + ?(k)X(k) V(0), a Subspace of V11.5. Two-Way Analysis of Variance; 12. Frequency Data; 12.1. Introduction; 12.2. Confidence Intervals on Binomial and Poisson Parameters; 12.3. Logistic Regression; 12.4. Two-Way Frequency Tables; 12.5. Chi-Square Goodness-of-Fit Tests; 13. Miscellaneous Topics; 13.1. Introduction; 13.2. Survival Analysis; 13.3. Bootstrapping; 13.4. Bayesian Statistics; 13.5. Sampling; References; Appendix; Answers to Selected Problems; Index 330 $aThis concise, yet thorough, book is enhanced with simulations and graphs to build the intuition of readersModels for Probability and Statistical Inference was written over a five-year period and serves as a comprehensive treatment of the fundamentals of probability and statistical inference. With detailed theoretical coverage found throughout the book, readers acquire the fundamentals needed to advance to more specialized topics, such as sampling, linear models, design of experiments, statistical computing, survival analysis, and bootstrapping.Ideal as a textbook for a two-seme 410 0$aWiley series in probability and statistics. 606 $aProbabilities$xMathematical models 606 $aProbabilities$xIndustrial applications 615 0$aProbabilities$xMathematical models. 615 0$aProbabilities$xIndustrial applications. 676 $a519.2 700 $aStapleton$b James H.$f1931-$0105221 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911019154703321 996 $aModels for probability and statistical inference$94420015 997 $aUNINA