{"product_id":"maximum-likelihood-estimation-and-inference-9780470094822","title":"Maximum Likelihood Estimation and Inference","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eApplied Likelihood Methods provides an accessible and practical introduction to likelihood modeling, supported by examples and software. The book features applications from a range of disciplines, including statistics, medicine, biology, and ecology.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e“This book is well-presented and would suit applied scientists, researchers, graduate students and particularly anyone who uses likelihood and such methods to their studies and applications.”  (\u003ci\u003eISR\u003c\/i\u003e, 2012)\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cb\u003ePreface xiii\u003c\/b\u003e  \u003cp\u003e\u003cb\u003ePart I PRELIMINARIES 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 A taste of likelihood 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 3\u003c\/p\u003e \u003cp\u003e1.2 Motivating example 4\u003c\/p\u003e \u003cp\u003e1.3 Using SAS, R and ADMB 9\u003c\/p\u003e \u003cp\u003e1.4 Implementation of the motivating example 11\u003c\/p\u003e \u003cp\u003e1.5 Exercises 17\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Essential concepts and iid examples 18\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 18\u003c\/p\u003e \u003cp\u003e2.2 Some necessary notation 19\u003c\/p\u003e \u003cp\u003e2.3 Interpretation of likelihood 23\u003c\/p\u003e \u003cp\u003e2.4 IID examples 25\u003c\/p\u003e \u003cp\u003e2.5 Exercises 33\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II PRAGMATICS 37\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Hypothesis tests and confidence intervals or regions 39\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 39\u003c\/p\u003e \u003cp\u003e3.2 Approximate normality of MLEs 40\u003c\/p\u003e \u003cp\u003e3.3 Wald tests, confidence intervals and regions 43\u003c\/p\u003e \u003cp\u003e3.4 Likelihood ratio tests, confidence intervals and regions 49\u003c\/p\u003e \u003cp\u003e3.5 Likelihood ratio examples 54\u003c\/p\u003e \u003cp\u003e3.6 Profile likelihood 57\u003c\/p\u003e \u003cp\u003e3.7 Exercises 59\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 What you really need to know 64\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 64\u003c\/p\u003e \u003cp\u003e4.2 Inference about \u003ci\u003eg\u003c\/i\u003e(\u003cb\u003e\u003ci\u003eθ\u003c\/i\u003e\u003c\/b\u003e) 65\u003c\/p\u003e \u003cp\u003e4.3 Wald statistics – quick and dirty? 75\u003c\/p\u003e \u003cp\u003e4.4 Model selection 79\u003c\/p\u003e \u003cp\u003e4.5 Bootstrapping 81\u003c\/p\u003e \u003cp\u003e4.6 Prediction 91\u003c\/p\u003e \u003cp\u003e4.7 Things that can mess you up 95\u003c\/p\u003e \u003cp\u003e4.8 Exercises 98\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Maximizing the likelihood 101\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 101\u003c\/p\u003e \u003cp\u003e5.2 The Newton-Raphson algorithm 103\u003c\/p\u003e \u003cp\u003e5.3 The EM (Expectation–Maximization) algorithm 104\u003c\/p\u003e \u003cp\u003e5.4 Multi-stage maximization 113\u003c\/p\u003e \u003cp\u003e5.5 Exercises 118\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Some widely used applications of maximum likelihood 121\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 121\u003c\/p\u003e \u003cp\u003e6.2 Box-Cox transformations 122\u003c\/p\u003e \u003cp\u003e6.3 Models for survival-time data 125\u003c\/p\u003e \u003cp\u003e6.4 Mark–recapture models 134\u003c\/p\u003e \u003cp\u003e6.5 Exercises 141\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Generalized linear models and extensions 143\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 143\u003c\/p\u003e \u003cp\u003e7.2 Specification of a GLM 144\u003c\/p\u003e \u003cp\u003e7.3 Likelihood calculations 148\u003c\/p\u003e \u003cp\u003e7.4 Model evaluation 149\u003c\/p\u003e \u003cp\u003e7.5 Case study 1: Logistic regression and inverse prediction in R 154\u003c\/p\u003e \u003cp\u003e7.6 Beyond binomial and Poisson models 161\u003c\/p\u003e \u003cp\u003e7.7 Case study 2: Multiplicative vs additive models of over-dispersed counts in SAS 167\u003c\/p\u003e \u003cp\u003e7.8 Exercises 173\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Quasi-likelihood and generalized estimating equations 175\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 175\u003c\/p\u003e \u003cp\u003e8.2 Wedderburn’s quasi-likelihood 177\u003c\/p\u003e \u003cp\u003e8.3 Generalized estimating equations 181\u003c\/p\u003e \u003cp\u003e8.4 Exercises 187\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 ML inference in the presence of incidental parameters 188\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 188\u003c\/p\u003e \u003cp\u003e9.2 Conditional likelihood 192\u003c\/p\u003e \u003cp\u003e9.3 Integrated likelihood 198\u003c\/p\u003e \u003cp\u003e9.3.1 Justification 199\u003c\/p\u003e \u003cp\u003e9.3.2 Uses of integrated likelihood 200\u003c\/p\u003e \u003cp\u003e9.4 Exercises 201\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Latent variable models 202\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 202\u003c\/p\u003e \u003cp\u003e10.2 Developing the likelihood 203\u003c\/p\u003e \u003cp\u003e10.3 Software 204\u003c\/p\u003e \u003cp\u003e10.4 One-way linear random-effects model 210\u003c\/p\u003e \u003cp\u003e10.5 Nonlinear mixed-effects model 217\u003c\/p\u003e \u003cp\u003e10.6 Generalized linear mixed-effects model 221\u003c\/p\u003e \u003cp\u003e10.7 State-space model for count data 227\u003c\/p\u003e \u003cp\u003e10.8 ADMB template files 228\u003c\/p\u003e \u003cp\u003e10.9 Exercises 232\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III THEORETICAL FOUNDATIONS 233\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Cramer-Rao inequality and Fisher information 235\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 235\u003c\/p\u003e \u003cp\u003e11.2 The Cramer-Rao inequality for \u003ci\u003eθ \u003c\/i\u003e RI 236\u003c\/p\u003e \u003cp\u003e11.3 Cramer-Rao inequality for functions of \u003ci\u003eθ\u003c\/i\u003e 239\u003c\/p\u003e \u003cp\u003e11.4 Alternative formulae for \u003cb\u003e\u003ci\u003eI\u003c\/i\u003e\u003c\/b\u003e (\u003ci\u003eθ\u003c\/i\u003e) 241\u003c\/p\u003e \u003cp\u003e11.5 The iid data case 243\u003c\/p\u003e \u003cp\u003e11.6 The multi-dimensional case, \u003cb\u003e\u003ci\u003eθ \u003c\/i\u003e\u003c\/b\u003e RI \u003ci\u003es\u003c\/i\u003e 243\u003c\/p\u003e \u003cp\u003e11.7 Examples of Fisher information calculation 247\u003c\/p\u003e \u003cp\u003e11.8 Exercises 253\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Asymptotic theory and approximate normality 256\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 256\u003c\/p\u003e \u003cp\u003e12.2 Consistency and asymptotic normality 257\u003c\/p\u003e \u003cp\u003e12.3 Approximate normality 271\u003c\/p\u003e \u003cp\u003e12.4 Wald tests and confidence regions 276\u003c\/p\u003e \u003cp\u003e12.5 Likelihood ratio test statistic 280\u003c\/p\u003e \u003cp\u003e12.6 Rao-score test statistic 281\u003c\/p\u003e \u003cp\u003e12.7 Exercises 283\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Tools of the trade 286\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 286\u003c\/p\u003e \u003cp\u003e13.2 Equivalence of tests and confidence intervals 286\u003c\/p\u003e \u003cp\u003e13.3 Transformation of variables 287\u003c\/p\u003e \u003cp\u003e13.4 Mean and variance conditional identities 288\u003c\/p\u003e \u003cp\u003e13.5 Relevant inequalities 289\u003c\/p\u003e \u003cp\u003e13.6 Asymptotic probability theory 291\u003c\/p\u003e \u003cp\u003e13.7 Exercises 297\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Fundamental paradigms and principles of inference 299\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 299\u003c\/p\u003e \u003cp\u003e14.2 Sufficiency principle 300\u003c\/p\u003e \u003cp\u003e14.3 Conditionality principle 304\u003c\/p\u003e \u003cp\u003e14.4 The likelihood principle 306\u003c\/p\u003e \u003cp\u003e14.5 Statistical significance versus statistical evidence 309\u003c\/p\u003e \u003cp\u003e14.6 Exercises 311\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Miscellanea 313\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Notation 313\u003c\/p\u003e \u003cp\u003e15.2 Acronyms 315\u003c\/p\u003e \u003cp\u003e15.3 Do you think like a frequentist or a Bayesian? 315\u003c\/p\u003e \u003cp\u003e15.4 Some useful distributions 316\u003c\/p\u003e \u003cp\u003e15.5 Software extras 321\u003c\/p\u003e \u003cp\u003e15.6 Automatic differentiation 323\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix: Partial solutions to selected exercises 325\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eBibliography 337\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIndex 345\u003c\/b\u003e\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default 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