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

 Contents' P' p; M2 }5 i2 M: C& p Preface- T5 D2 ~: z* a5 R. V 1 Introduction 1.1 Background- T& W\$ Y' r/ M, l0 d8 X  e5 D 1.2 Scope 1.3 Notation 1.4 Distributions related to the Normal distribution2 H( z1 Y: m/ W\$ x0 l/ t4 y 1.5 Quadratic forms 1.6 Estimation, O* B3 \% s6 ^: e2 \. N' C6 d\$ j 1.7 Exercises+ D4 ?" q. g& R8 j\$ j) n 2 Model Fitting 2.1 Introduction 2.2 Examples 2.3 Some principles ofstatistica l modelling 2.4 Notation and coding for explanatory variables* i1 C5 M( P; h- V, @ 2.5 Exercises. E. U4 v# w0 M! j* J 3 Exponential Family and Generalized Linear Models9 L" f\$ a7 [" y  k7 B. h5 a 3.1 Introduction 3.2 Exponential family of distributions6 t  o2 k8 }- Q4 u9 p4 p 3.3 Properties ofdistribution s in the exponential family9 }% W5 f1 F( d5 K: X 3.4 Generalized linear models9 t4 E! @/ Q% J: K" h 3.5 Examples5 C) b5 x% w: J3 Z 3.6 Exercises 4 Estimation 4.1 Introduction) P, h\$ U" p3 x' W. O% N 4.2 Example: Failure times for pressure vessels 4.3 Maximum likelihood estimation 4.4 Poisson regression example& }! D4 A0 k: \& ` 4.5 Exercises2 x& s% f9 D: t/ P\$ L3 w 5 Inference/ y3 b7 j1 D7 H: p! ^: j 5.1 Introduction9 g  {2 b, T7 `" B 5.2 Sampling distribution for score statistics6 D+ w9 u: {4 T ? 2002 by Chapman & Hall/CRC 54 h* ]4 a2 g2 {/ @ 5.3 Taylor series approximations 5.4 Sampling distribution for maximum likelihood estimators7 Q( [4 S& e# {5 H 5.5 Log-likelihood ratio statistic4 I9 p3 M( x/ H* ] 5.6 Sampling distribution for the deviance 5.7 Hypothesis testing1 B. n9 c7 s/ F7 D, x1 p 5.8 Exercises 6 Normal Linear Models 6.1 Introduction 6.2 Basic results 6.3 Multiple linear regression' g\$ R& E  X& ~ 6.4 Analysis of variance 6.5 Analysis ofc ovariance 6.6 General linear models- h' |. K8 x2 \; Z5 i* Y 6.7 Exercises\$ M0 F  ?' Z8 L, i  p 7 Binary Variables and Logistic Regression 7.1 Probability distributions* J7 D4 }1 I1 J4 A7 z 7.2 Generalized linear models2 G: k# L2 x0 C( {2 y 7.3 Dose response models 7.4 General logistic regression model5 ^3 [; u( F* @) p/ U; v5 V8 ?% I6 v* y 7.5 Goodness offi t statistics9 h6 ^9 a9 ~2 h2 R( t4 \ 7.6 Residuals: S  E! u9 L3 }' C- H 7.7 Other diagnostics' ^8 K, V  B/ W 7.8 Example: Senility and WAIS+ \2 Z% r3 |, l/ e 7.9 Exercises- c. X6 A+ X: i& B% B* F 8 Nominal and Ordinal Logistic Regression! Z5 z% a; v; H' [ 8.1 Introduction 8.2 Multinomial distribution 8.3 Nominal logistic regression% v' ^! K8 d3 v) k& K3 V2 K. r 8.4 Ordinal logistic regression7 H% {1 F8 F- p; `2 l. Y" } 8.5 General comments 8.6 Exercises 9 Count Data, Poisson Regression and Log-Linear Models 9.1 Introduction 9.2 Poisson regression9 Z\$ B9 P7 C! c 9.3 Examples ofco ntingency tables 9.4 Probability models for contingency tables, h/ I; T& M* W 9.5 Log-linear models9 H  L: Y( O8 X( Y6 P6 g 9.6 Inference for log-linear models8 J5 o/ V; E, r 9.7 Numerical examples+ e7 }+ o! R* c  g: V3 ] 9.8 Remarks; G6 I) W1 h) b# j) c1 Y5 c. G 9.9 Exercises ? 2002 by Chapman & Hall/CRC 6 10 Survival Analysis 10.1 Introduction1 u5 F+ M0 y0 e\$ S- a 10.2 Survivor functions and hazard functions+ t& e! f4 L9 G8 C, }; s5 l5 y0 J 10.3 Empirical survivor function& j0 O9 [5 U8 ?; H3 a/ X# y! y 10.4 Estimation1 u& S1 q+ w3 @ 10.5 Inference 10.6 Model checking 10.7 Example: remission times' `( D) o+ J0 T. V2 H 10.8 Exercises 11 Clustered and Longitudinal Data5 B" F; p: s7 N0 Q 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 data% X5 `/ R( j' t, J 11.5 Multilevel models* n( J1 ^3 g( t' m; M* d 11.6 Stroke example continued7 S6 g: d( h8 C. y1 Q. m6 [ 11.7 Comments 11.8 Exercises Software; ~0 b! T' j) u\$ D7 e\$ y# V5 Y* ~ References( }: i2 U8 }, ^! T2 l ? 2002 by Chapman & Hall/CRC 7% [( ^3 A! }, _! _0 y Preface Statistical tools for analyzing data are developing rapidly so that the 19908 K+ j7 \) n2 O) ~ edition ofthis book is now out ofdate. The original purpose ofthe book was to present a unified theoretical and3 F8 _* q* H\$ L3 y- N( E conceptual framework for statistical modelling in a way that was accessible to undergraduate students and researchers in other fields. This new edition has been expanded to include nominal (or multinomial) and ordinal logistic regression, survival analysis and analysis oflongitudinal and clustered data.3 o! m& |6 O. v; Z Although these topics do not fall strictly within the definition of generalized linear models, the underlying principles and methods are very similar and( D4 l/ }% v  d7 ] their inclusion is consistent with the original purpose ofthe book. The new edition relies on numerical methods more than the previous edition# s2 N' B9 V/ K, D' X did. Some ofthe calculations can be performed with a spreadsheet while others1 Y- |# q) q4 m require statistical software. There is an emphasis on graphical methods for; b+ N0 G3 w2 z exploratory data analysis, visualizing numerical optimization (for example, ofthe likelihood function) and plotting residuals to check the adequacy of models. Introduction# s1 I) k6 o/ k5 J3 E! H 1.1 Background1 w" t( e( c" J! T. e* A, o This book is designed to introduce the reader to generalized linear models;, R: R! n! X3 ~7 d( w# Y\$ R 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 and methods. In particular, understanding the concepts ofestimation, sampling& n: C0 l\$ g+ Z: A 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,9 u! ]\$ g1 }6 M/ }1 D; z; m some knowledge ofmatrix algebra and calculus is required.1 S- O/ c0 t' H The reader will find it necessary to have access to statistical computing, M) H1 S' V2 Y" I8 @- o6 l facilities. Many statistical programs, languages or packages can now perform. c% m8 a# p' A4 [/ K2 B the analyses discussed in this book. Often, however, they do so with a different\$ h; w' b) {0 v2 m program or procedure for each type of analysis so that the unifying structure is not apparent.2 J% E3 M  }7 j( @9 L9 w, R; P" c Some programs or languages which have procedures consistent with the5 j& L6 ]" T. B( w approach used in this book are: Stata, S-PLUS, Glim, Genstat and SYSTAT. This list is not comprehensive as appropriate modules are continually: e+ f2 F* }4 J1 u4 M6 f  U being added to other programs.0 z) j: T& O- S, W) R In addition, anyone working through this book may find it helpful to be able to use mathematical software that can perform matrix algebra, differentiation and iterative calculations. 1.2 Scope1 t" _% \- u, {+ ` The statistical methods considered in this * a9 C\$ k* i+ \% l- A v威枝 0 v! E: c) T5 J4 p\$ H 2 d1 W- S, ^& _( d) ?* }% ` - N, G* b% l7 O* P 联系QQ：5267816185 D& H0 Y( |9 a  Y\$ O2 D% X6 Z ; a3 ?6 O) Q4 A" }7 A% | 淘宝旺旺：跟朝流走1 C9 `1 i2 v5 h; j6 L! \2 |2 E  Q 有需要的欢迎联系！专业代购电子书 - H' o( V- C* y* k: a* B ( ?( Z, z; w# n 0 q- Y& d# J* H2 f: S+ w, s ebook 英文电子书代购