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

 Contents Preface 1 Introduction7 C9 i) z. g& Z1 C 1.1 Background 1.2 Scope6 t; w; d/ O. a5 q8 M9 ~1 I 1.3 Notation 1.4 Distributions related to the Normal distribution6 I! \3 Z; v% m+ {9 P 1.5 Quadratic forms 1.6 Estimation 1.7 Exercises" E4 h# Y. S4 E2 n6 g 2 Model Fitting0 L% z- J, Q/ s\$ _ 2.1 Introduction 2.2 Examples 2.3 Some principles ofstatistica l modelling 2.4 Notation and coding for explanatory variables, l& o: i/ s3 x' ]7 W  p\$ b7 i4 _ 2.5 Exercises 3 Exponential Family and Generalized Linear Models/ C+ J) ^2 {. y7 B 3.1 Introduction1 n\$ S5 w6 E- j; w3 K, {+ g 3.2 Exponential family of distributions 3.3 Properties ofdistribution s in the exponential family) y" z% M9 D/ ?+ T: a2 r 3.4 Generalized linear models 3.5 Examples+ i# r+ |. S\$ L% C0 T, |1 c% ` 3.6 Exercises  b3 v, m* G1 Q 4 Estimation 4.1 Introduction 4.2 Example: Failure times for pressure vessels 4.3 Maximum likelihood estimation; G) l( b1 V7 V\$ x0 Q 4.4 Poisson regression example 4.5 Exercises 5 Inference 5.1 Introduction 5.2 Sampling distribution for score statistics ? 2002 by Chapman & Hall/CRC1 a2 |\$ D. m6 S  q, B. s 5 5.3 Taylor series approximations6 g" n7 x( X. {" l6 W; A 5.4 Sampling distribution for maximum likelihood estimators 5.5 Log-likelihood ratio statistic) Y+ f  ?6 S: q" {6 H% z 5.6 Sampling distribution for the deviance3 p# x" ]1 ~" x) Z  M 5.7 Hypothesis testing: l* h9 `! N% s# }8 p 5.8 Exercises 6 Normal Linear Models 6.1 Introduction 6.2 Basic results& e\$ M6 C7 \# o" J1 [9 ]3 h: o9 M 6.3 Multiple linear regression, I# r8 ~8 S- y1 C7 z 6.4 Analysis of variance 6.5 Analysis ofc ovariance# \. u7 F' N\$ N: u7 u) f7 [9 E 6.6 General linear models" A/ j5 n: v& z8 f! ?) [ 6.7 Exercises1 U0 ~% V* D1 Y. z: X3 U5 i 7 Binary Variables and Logistic Regression 7.1 Probability distributions 7.2 Generalized linear models 7.3 Dose response models7 c( G7 K' n2 l1 ~# `. A  E 7.4 General logistic regression model2 Q. B; ~% v, ]' _- q( u 7.5 Goodness offi t statistics 7.6 Residuals1 W# o/ J6 y8 d+ A 7.7 Other diagnostics 7.8 Example: Senility and WAIS 7.9 Exercises( A* e0 D& H3 A6 \& h+ b9 Q' ? 8 Nominal and Ordinal Logistic Regression 8.1 Introduction 8.2 Multinomial distribution- f. H% o, G\$ a, S6 i 8.3 Nominal logistic regression6 \; s9 o- K* B 8.4 Ordinal logistic regression 8.5 General comments8 O% L! I, V' }1 P0 m 8.6 Exercises 9 Count Data, Poisson Regression and Log-Linear Models 9.1 Introduction\$ r4 A- E9 p) V\$ V' G0 ^5 j 9.2 Poisson regression 9.3 Examples ofco ntingency tables8 E) B) K( V3 B6 a# B; e 9.4 Probability models for contingency tables 9.5 Log-linear models, S' S* B. W; } 9.6 Inference for log-linear models 9.7 Numerical examples! b2 F# Z# l% `& F7 N 9.8 Remarks 9.9 Exercises ? 2002 by Chapman & Hall/CRC 6, m- U8 ?8 d! ^6 u0 b% z 10 Survival Analysis 10.1 Introduction# o* y. H1 j- O6 M 10.2 Survivor functions and hazard functions 10.3 Empirical survivor function4 A& j; Q! O" b6 h9 q4 k* ~# |6 H. s 10.4 Estimation 10.5 Inference2 D" v  |, a9 N0 _1 C% e 10.6 Model checking 10.7 Example: remission times 10.8 Exercises 11 Clustered and Longitudinal Data 11.1 Introduction 11.2 Example: Recovery from stroke 11.3 Repeated measures models for Normal data 11.4 Repeated measures models for non-Normal data7 u+ g0 K' m) \" d- J0 }, \ 11.5 Multilevel models0 u3 i/ J- \: ] 11.6 Stroke example continued 11.7 Comments 11.8 Exercises Software References ? 2002 by Chapman & Hall/CRC2 g/ D& ^% ]0 j! ~1 \. X1 x0 u 7 Preface Statistical tools for analyzing data are developing rapidly so that the 1990 edition ofthis book is now out ofdate.% y# Q5 e0 U5 _+ y The original purpose ofthe book was to present a unified theoretical and1 D. u3 v& E9 K& I8 ?% K conceptual framework for statistical modelling in a way that was accessible) I1 C! ^  K: [" h! J\$ e4 P5 b to undergraduate students and researchers in other fields. This new edition! A/ [& F* g2 ?3 G  }8 x has been expanded to include nominal (or multinomial) and ordinal logistic: @* A0 d- `, q* a regression, survival analysis and analysis oflongitudinal and clustered data.! x" I" j8 H) x! U+ ? Although these topics do not fall strictly within the definition of generalized linear models, the underlying principles and methods are very similar and9 w% v# i8 n. [" i\$ ]" P- V0 h9 f their inclusion is consistent with the original purpose ofthe book. The new edition relies on numerical methods more than the previous edition% v\$ b( X; W9 N did. Some ofthe calculations can be performed with a spreadsheet while others4 e) y, z# u' s require statistical software. There is an emphasis on graphical methods for exploratory data analysis, visualizing numerical optimization (for example,/ F, e4 m; G0 d. |( O ofthe likelihood function) and plotting residuals to check the adequacy of! O' o3 g  G9 Y\$ |. S  ^\$ P models. Introduction, [- P4 d* t5 m7 U( f" z 1.1 Background 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 principles9 e/ o! n) N( n, A% f! `9 I! @3 x and methods. In particular, understanding the concepts ofestimation, sampling! ~6 i# f; X; } distributions and hypothesis testing is necessary. Experience in the use oft-tests, analysis ofv ariance, simple linear regression and chi-squared tests of5 o1 ?, ~\$ o4 V2 |7 ?, r% B independence for two-dimensional contingency tables is assumed. In addition,' M\$ }8 B, e6 W. U3 p 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 perform2 |' A% q% i; \9 w\$ H3 m 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! z9 N+ F! X! z! B5 g is not apparent.* ~& @* ?3 f( O" n# V7 U# V1 N8 f8 y Some programs or languages which have procedures consistent with the* Q, G; F* h0 b9 L approach used in this book are: Stata, S-PLUS, Glim, Genstat and SYSTAT.5 V' h( F8 F% h This list is not comprehensive as appropriate modules are continually being added to other programs. In addition, anyone working through this book may find it helpful to be able to use mathematical software that can perform matrix algebra, differentiation) X1 d* B* o+ _7 a and iterative calculations.6 s. h* Z: I" m+ K# w 1.2 Scope% [) ?+ W\$ ?5 [! W* Q. t0 L The statistical methods considered in this v威枝6 V( N1 V. j  J. T& l6 e8 Y + |! w& n* N* a% z2 z; T * I; }& f2 X, s1 G( d8 L\$ C 8 e) ?\$ F/ f6 R - i# [6 Q" H  g  M4 @ % Y+ k% @1 z3 B! N" k8 k1 M 联系QQ：526781618 * |5 Q" ]\$ d0 N( ~. V. G2 J, e 淘宝旺旺：跟朝流走9 v4 b\$ G, F" d5 \\$ K" T 8 ?9 ~& H: ~) a. o 有需要的欢迎联系！专业代购电子书 / a3 c9 Z2 k& {! t( }! _ ebook 英文电子书代购