# AN INTRODUCTION TO GENERALIZED LINEAR MODELS SECOND ebook 电子书代购

 Contents Preface 1 Introduction 1.1 Background\$ [; m+ ]5 {5 ?& r6 y 1.2 Scope 1.3 Notation, |& d% Q" e3 b. L7 w  Z: i4 A 1.4 Distributions related to the Normal distribution 1.5 Quadratic forms 1.6 Estimation* W0 v. v& b" A 1.7 Exercises 2 Model Fitting% X+ `5 ]( Q" q' Y& M" O 2.1 Introduction2 H  F# N5 G\$ S6 E* Q1 [, X' s- T 2.2 Examples 2.3 Some principles ofstatistica l modelling 2.4 Notation and coding for explanatory variables 2.5 Exercises 3 Exponential Family and Generalized Linear Models8 \7 q1 Q% B\$ V) z" N% L 3.1 Introduction3 _4 K\$ _% u& A\$ M- V 3.2 Exponential family of distributions 3.3 Properties ofdistribution s in the exponential family; F& E% W6 p& C' }8 g  q* W 3.4 Generalized linear models3 v# j2 z) G\$ S' W5 } 3.5 Examples' {* l" f+ e& T- O) Q' i8 _- X 3.6 Exercises 4 Estimation0 U" E3 i4 Z, L: X 4.1 Introduction\$ [; d8 L) {; M+ a3 _# } 4.2 Example: Failure times for pressure vessels 4.3 Maximum likelihood estimation* e  V" R+ l: s+ S\$ k 4.4 Poisson regression example! G2 `, P/ ~9 z- u9 M( G 4.5 Exercises; a* f' q5 E0 ~  E& N  B7 F\$ J 5 Inference 5.1 Introduction 5.2 Sampling distribution for score statistics! ^9 @6 b. c+ ~ ? 2002 by Chapman & Hall/CRC4 z# n  c\$ V, h. S\$ V1 [  T1 Z 5" {' g" q3 V. d3 }\$ \& r 5.3 Taylor series approximations& [  L: |& G  p. m  x/ x0 Z 5.4 Sampling distribution for maximum likelihood estimators 5.5 Log-likelihood ratio statistic 5.6 Sampling distribution for the deviance 5.7 Hypothesis testing+ u0 h1 g% i' `8 S) G" g5 ` 5.8 Exercises 6 Normal Linear Models) @2 H  m8 n; u1 b8 c7 O 6.1 Introduction) L3 a  B9 y, u9 d# ^: `; w( |# N* M 6.2 Basic results 6.3 Multiple linear regression. ~4 x5 b& S, Y( W 6.4 Analysis of variance: f7 f3 c; Y. n/ L8 d8 K 6.5 Analysis ofc ovariance 6.6 General linear models 6.7 Exercises9 F! k( `9 C- n( M2 H8 T 7 Binary Variables and Logistic Regression( [- v; N# T" [( b' @: P 7.1 Probability distributions( p# w8 V7 v6 X+ |* n% m 7.2 Generalized linear models, u% O' s# b  e: N 7.3 Dose response models 7.4 General logistic regression model 7.5 Goodness offi t statistics& f0 H\$ O8 x7 }7 n  P4 q/ s0 e0 Q 7.6 Residuals: e+ Z. t5 N1 s  [' c5 _ 7.7 Other diagnostics 7.8 Example: Senility and WAIS! O3 y. A, J0 R* g4 j( `6 o 7.9 Exercises1 A/ z9 I9 l) j; N 8 Nominal and Ordinal Logistic Regression 8.1 Introduction9 f7 I2 ~2 f. [: u" M8 I/ z8 L' P\$ k 8.2 Multinomial distribution 8.3 Nominal logistic regression 8.4 Ordinal logistic regression/ }8 g% X/ w1 x6 ~7 T3 Q2 d1 t( b! O# j 8.5 General comments 8.6 Exercises\$ m) h& y& m. E, x. A7 A 9 Count Data, Poisson Regression and Log-Linear Models 9.1 Introduction 9.2 Poisson regression9 [7 O9 t9 o) {# C8 p 9.3 Examples ofco ntingency tables4 E2 r: x# g, F! r' ~* n 9.4 Probability models for contingency tables 9.5 Log-linear models  n" i% T% x" ]3 ?* x% |7 u 9.6 Inference for log-linear models; b2 ^+ G2 Y/ ]) \\$ a- O4 f# \ 9.7 Numerical examples 9.8 Remarks 9.9 Exercises ? 2002 by Chapman & Hall/CRC 6! U! x: f/ `+ w8 g  {, J+ c9 _ 10 Survival Analysis 10.1 Introduction 10.2 Survivor functions and hazard functions 10.3 Empirical survivor function1 D8 _\$ |3 s  M: S6 _# B 10.4 Estimation\$ m( o1 j1 [9 q6 h 10.5 Inference6 [' l- P5 e  \! F9 D/ b! b 10.6 Model checking+ R0 u4 L9 P9 u 10.7 Example: remission times 10.8 Exercises4 `* G! T+ _8 i8 a 11 Clustered and Longitudinal Data: i5 s( X& I" Z! c7 n 11.1 Introduction5 q6 P+ w\$ D7 _' f/ E% { 11.2 Example: Recovery from stroke 11.3 Repeated measures models for Normal data 11.4 Repeated measures models for non-Normal data9 d\$ S' t# v0 `( ^" X4 W) a; ] 11.5 Multilevel models) y, [9 B% e; Z3 O: C 11.6 Stroke example continued 11.7 Comments 11.8 Exercises Software References* V) [. g% }: a' W8 Z ? 2002 by Chapman & Hall/CRC 75 x& K- ?: _) d/ A4 r  I Preface+ Q3 b0 n9 Q  R+ l0 T Statistical tools for analyzing data are developing rapidly so that the 1990 edition ofthis book is now out ofdate.) Y+ o# R: U7 L9 M& D# q The original purpose ofthe book was to present a unified theoretical and0 ]) C\$ v# T- y/ U- O& N/ Q conceptual framework for statistical modelling in a way that was accessible2 z5 X" Q) V3 `5 J: _5 ]+ N* w to undergraduate students and researchers in other fields. This new edition  T" \& a  J% _: S" W* d has been expanded to include nominal (or multinomial) and ordinal logistic regression, survival analysis and analysis oflongitudinal and clustered data.1 h2 [7 c4 e0 r8 E: B\$ Q2 l Although these topics do not fall strictly within the definition of generalized% z3 ^) W0 p\$ D- z, Y) a8 s# k linear models, the underlying principles and methods are very similar and3 ?( k* t3 @. w3 O+ _# U. l, ~ their inclusion is consistent with the original purpose ofthe book. The new edition relies on numerical methods more than the previous edition* u7 s7 _0 w% M7 }( ] did. Some ofthe calculations can be performed with a spreadsheet while others\$ U( b9 i/ ]: U* d# [- [  y) |0 { require statistical software. There is an emphasis on graphical methods for* ^, M8 |1 F4 k1 J  e\$ Q exploratory data analysis, visualizing numerical optimization (for example, ofthe likelihood function) and plotting residuals to check the adequacy of+ Q- }! m/ G5 j, _1 p models. Introduction 1.1 Background* G: W8 y8 Y/ J\$ s: x  {/ ` This book is designed to introduce the reader to generalized linear models; these provide a unifying framework for many commonly used statistical techniques. They also illustrate the ideas ofstatistical modelling.8 Z4 H1 E5 t6 W) t' J8 Z! b" H  [1 } The reader is assumed to have some familiarity with statistical principles7 S; b3 l# T- M+ i and methods. In particular, understanding the concepts ofestimation, sampling distributions and hypothesis testing is necessary. Experience in the use oft-tests, analysis ofv ariance, simple linear regression and chi-squared tests of independence for two-dimensional contingency tables is assumed. In addition, some knowledge ofmatrix algebra and calculus is required. The reader will find it necessary to have access to statistical computing facilities. Many statistical programs, languages or packages can now perform3 T5 P3 a# C9 Y* s the analyses discussed in this book. Often, however, they do so with a different2 y% d; v  R2 u. C) m  z! l( I" l6 u program or procedure for each type of analysis so that the unifying structure1 n, D: F' j6 Z is not apparent. Some programs or languages which have procedures consistent with the8 w- Y9 {/ A/ b* k' o approach used in this book are: Stata, S-PLUS, Glim, Genstat and SYSTAT. This list is not comprehensive as appropriate modules are continually being added to other programs.4 _2 |: \1 c0 m5 R  r" _3 W9 j In addition, anyone working through this book may find it helpful to be able+ ?3 |1 a* v6 k* ^ to use mathematical software that can perform matrix algebra, differentiation. P3 W2 N2 |4 R  C, l and iterative calculations. 1.2 Scope) r8 |' x+ y4 W4 w' `1 [ The statistical methods considered in this v威枝, |0 d* a+ \2 @4 [5 ?7 J3 d ( y, h2 s  h: F; s. _\$ D % E& _, J  [0 Y% J" `\$ G  M 联系QQ：526781618- N! k' A* Y0 I 淘宝旺旺：跟朝流走9 |6 w7 f+ h# L8 E- j, [+ N7 X 有需要的欢迎联系！专业代购电子书+ J/ O\$ I9 T/ z\$ t # x9 \0 p0 K0 ^ 5 i- R. q- I+ c9 x ebook 英文电子书代购