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

 Contents5 w2 j: _4 S7 u Preface\$ ^: T  `) V0 S 1 Introduction/ m  A" ]4 @! P2 V/ Y! V( v 1.1 Background; _- I9 R# H  x  z7 {  J0 e# o\$ z 1.2 Scope 1.3 Notation3 T, G\$ ]) N. T  x  p: I 1.4 Distributions related to the Normal distribution 1.5 Quadratic forms 1.6 Estimation 1.7 Exercises4 c9 {- f4 z( I' w1 O) p 2 Model Fitting 2.1 Introduction7 {" J/ _3 n& p 2.2 Examples0 [: V% c5 _/ H\$ E 2.3 Some principles ofstatistica l modelling 2.4 Notation and coding for explanatory variables 2.5 Exercises0 x0 f- |0 U* q 3 Exponential Family and Generalized Linear Models" d\$ I2 w2 Q! q; n 3.1 Introduction 3.2 Exponential family of distributions 3.3 Properties ofdistribution s in the exponential family 3.4 Generalized linear models 3.5 Examples" x' K0 i. k* \8 F" { 3.6 Exercises7 g9 l3 {+ G! @2 L) u- o. l 4 Estimation# q  R3 K8 b) O\$ ]3 O 4.1 Introduction 4.2 Example: Failure times for pressure vessels& N* u\$ W* T  Q4 J# u/ h# t8 @ 4.3 Maximum likelihood estimation 4.4 Poisson regression example; N( L4 c, q2 R- c( s  I 4.5 Exercises 5 Inference1 P7 |- E8 ?+ a. K9 e- _ 5.1 Introduction 5.2 Sampling distribution for score statistics ? 2002 by Chapman & Hall/CRC 5 5.3 Taylor series approximations 5.4 Sampling distribution for maximum likelihood estimators 5.5 Log-likelihood ratio statistic3 t9 e6 s# F' r  ~( l 5.6 Sampling distribution for the deviance  }3 ~# l: G0 `0 B/ ^; w* L 5.7 Hypothesis testing3 l+ w! S/ N8 b5 O6 w% b/ b% |  d 5.8 Exercises& K2 h% P3 T' L' S\$ K2 ^ 6 Normal Linear Models& H1 w' R# h) t! M" \ 6.1 Introduction 6.2 Basic results 6.3 Multiple linear regression 6.4 Analysis of variance6 C  h4 @, B: a8 d0 h( S 6.5 Analysis ofc ovariance. Y* ?( K( g" X* P2 c\$ N( ^9 M 6.6 General linear models9 B( x4 w; _; T5 O. \ 6.7 Exercises- J; ], B+ J: `" t 7 Binary Variables and Logistic Regression 7.1 Probability distributions: z* e7 ~- L" k1 [0 k 7.2 Generalized linear models 7.3 Dose response models 7.4 General logistic regression model 7.5 Goodness offi t statistics 7.6 Residuals 7.7 Other diagnostics 7.8 Example: Senility and WAIS- l- E1 D+ u  w* q4 A 7.9 Exercises* w% ?; x9 S8 e5 z 8 Nominal and Ordinal Logistic Regression! a4 R; A6 x2 X; u: \% E 8.1 Introduction' Y0 Y\$ S% K7 ^% _4 t' E2 ~ 8.2 Multinomial distribution; A. f- W, n: B8 z3 O. ]0 F 8.3 Nominal logistic regression 8.4 Ordinal logistic regression1 ]/ c\$ ~& o\$ r3 v3 H7 A 8.5 General comments 8.6 Exercises- w1 p2 A9 \6 I& } 9 Count Data, Poisson Regression and Log-Linear Models7 I# q% F# |4 V\$ o( s; v4 m8 j 9.1 Introduction! y2 d" j: Q  L  I" u' P0 e 9.2 Poisson regression/ j8 L4 B# ^: w) \- W7 C 9.3 Examples ofco ntingency tables( _- \& b; @! a  K* l 9.4 Probability models for contingency tables 9.5 Log-linear models6 i! k; ^) @% Z! A5 }# o 9.6 Inference for log-linear models 9.7 Numerical examples 9.8 Remarks; ~* Y3 G# w, l# x; I 9.9 Exercises ? 2002 by Chapman & Hall/CRC 6 10 Survival Analysis\$ k3 P8 L6 k1 I0 m& m0 ?) \ 10.1 Introduction% c; A+ i5 r9 O9 Z7 g 10.2 Survivor functions and hazard functions 10.3 Empirical survivor function 10.4 Estimation 10.5 Inference6 S( e9 x+ B* O1 o 10.6 Model checking 10.7 Example: remission times 10.8 Exercises  B\$ _% ^* o; U4 X: L0 ~ 11 Clustered and Longitudinal Data4 c) K3 S( `' y 11.1 Introduction 11.2 Example: Recovery from stroke4 a% e4 L% s1 u+ [/ p 11.3 Repeated measures models for Normal data 11.4 Repeated measures models for non-Normal data/ C( ^2 V( w7 V! ~\$ E5 o  x1 \ 11.5 Multilevel models 11.6 Stroke example continued4 w1 `8 ]# A! x9 W# Q 11.7 Comments 11.8 Exercises Software+ p+ \8 W8 l' v- p References5 S, x% n" a1 P ? 2002 by Chapman & Hall/CRC; C) E) x& D1 ^7 _& g 7: w# H) w# X2 C1 W5 i. X  ]6 i% z( S Preface3 B5 Y) t# n+ y. K- I Statistical tools for analyzing data are developing rapidly so that the 1990/ G% N" M7 x' a, ^; l' v+ D; A edition ofthis book is now out ofdate.' A/ D/ q\$ m  t/ r, t6 v, n The original purpose ofthe book was to present a unified theoretical and conceptual framework for statistical modelling in a way that was accessible( r. b9 j+ J2 q9 u3 U\$ t: k  n to undergraduate students and researchers in other fields. This new edition has been expanded to include nominal (or multinomial) and ordinal logistic/ x) {4 ?% _  o. Q# I; \  O+ q regression, survival analysis and analysis oflongitudinal and clustered data. Although these topics do not fall strictly within the definition of generalized' ~4 f. J% x6 A5 p: n1 \5 E* C linear models, the underlying principles and methods are very similar and; q. A  N! G# \ their inclusion is consistent with the original purpose ofthe book. The new edition relies on numerical methods more than the previous edition did. Some ofthe calculations can be performed with a spreadsheet while others require statistical software. There is an emphasis on graphical methods for exploratory data analysis, visualizing numerical optimization (for example,  c1 G  z9 }" y# W ofthe likelihood function) and plotting residuals to check the adequacy of models.7 b2 A0 m; p# |% F; D% u: K! r + I' z: O2 S" b- c9 ~5 \ Introduction/ J\$ F7 J/ a- s  ^% {( i' z0 z 1.1 Background/ q. J( j) o; i) S8 D 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. The reader is assumed to have some familiarity with statistical principles' V7 H9 m7 E  @0 N6 c& p/ s% s and methods. In particular, understanding the concepts ofestimation, sampling) |3 r1 @0 U3 v  ]6 D. J 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,6 p+ j7 k0 ?# c( \7 d2 e! l some knowledge ofmatrix algebra and calculus is required.. M2 e9 d\$ D; v5 p1 C The reader will find it necessary to have access to statistical computing9 B2 c  O: a& ?. K, ?; g1 h facilities. Many statistical programs, languages or packages can now perform the analyses discussed in this book. Often, however, they do so with a different program or procedure for each type of analysis so that the unifying structure" z: ~% B8 q/ ?* f1 a is not apparent. Some programs or languages which have procedures consistent with the approach used in this book are: Stata, S-PLUS, Glim, Genstat and SYSTAT.; s7 U" u: h" Q; J0 z/ Z; y  i This list is not comprehensive as appropriate modules are continually being added to other programs., [/ X! }0 O+ _# U9 | In addition, anyone working through this book may find it helpful to be able6 c2 ?  I4 Q! [2 b to use mathematical software that can perform matrix algebra, differentiation, k; H- B; U5 ?. O and iterative calculations. 1.2 Scope7 o- I3 s9 x! n2 G8 R\$ ~ The statistical methods considered in this v威枝* U7 m% W; ]* K' O; j, K& C( l 3 T- A7 a0 z3 O 联系QQ：526781618 淘宝旺旺：跟朝流走 ) w! n, d6 M+ i  z 有需要的欢迎联系！专业代购电子书 / N' J, x6 d4 m# M. P. h, B" x1 m 0 N* n& I5 S' p3 M! ?9 j  x ebook 英文电子书代购