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

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

Contents
. D9 x$ }8 V' J0 W9 `' SPreface  o0 Q2 q2 m# p, m- N! t
1 Introduction
- a. A& p( F9 M: R" s: g6 C  `" \! J1.1 Background
8 x3 U; d3 v! e" e0 G; j! ^4 @1.2 Scope
' s" s' V: w9 E" [# P1.3 Notation7 g, v  L( {4 {& C+ V" l. _0 c
1.4 Distributions related to the Normal distribution4 \) l6 S' J" I2 h; ?+ a/ ?( Z: V
1.5 Quadratic forms
, F# V5 v: S* t4 Y- a1.6 Estimation
% X8 c7 z+ o  {6 x4 W  j2 f1.7 Exercises
5 p  m* o6 p. R6 B: W2 }1 h5 T2 Model Fitting+ R7 B' S$ E, W
2.1 Introduction
* ]: m0 y7 a+ P  E; N2.2 Examples3 S& u8 l# [% j% W
2.3 Some principles ofstatistica l modelling
, A7 n2 g8 F: s' \# O2.4 Notation and coding for explanatory variables/ }; o/ a+ b5 d  K5 ]
2.5 Exercises
- g* y' ]6 A& R5 E2 b1 t* y! G# L3 Exponential Family and Generalized Linear Models1 P) J# B  L7 }& O  m
3.1 Introduction& B& ?! C2 l, D
3.2 Exponential family of distributions9 l4 l4 G4 a" ^! _4 F$ J+ m
3.3 Properties ofdistribution s in the exponential family1 v1 \5 l* r. M6 e9 C
3.4 Generalized linear models
1 ^3 L# M2 F- F0 d: a3.5 Examples
, G6 Q) y" z6 q8 E$ ]% }3.6 Exercises
; u+ L% N" [4 c/ |& @2 p4 Estimation
+ h& I; K- J7 r4.1 Introduction* v4 |% a8 |1 F; v& x; z) {% ~0 |* j
4.2 Example: Failure times for pressure vessels
# l  F5 s0 N  ^( b% U; F4.3 Maximum likelihood estimation" K* Z2 }3 O. F# ?
4.4 Poisson regression example) V2 Z8 L. p) F; Q! o$ M
4.5 Exercises
7 e6 I4 F7 Y. Y- F  ?5 Inference# l: m' ?: @3 L* A0 d
5.1 Introduction' l& R5 C0 g* [
5.2 Sampling distribution for score statistics  ?2 g# O/ N8 F% }: U& @
? 2002 by Chapman & Hall/CRC
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5.3 Taylor series approximations
/ S1 B) j1 H1 l5.4 Sampling distribution for maximum likelihood estimators
$ t' `2 P; a8 `5.5 Log-likelihood ratio statistic
4 N; j1 b  D$ {5.6 Sampling distribution for the deviance
: A. u7 I8 C& z5.7 Hypothesis testing6 `0 b8 l2 x& h8 o- V8 ?
5.8 Exercises& X; H4 M7 M6 O: E7 V
6 Normal Linear Models
2 r( ~4 U, P. A! p3 @$ g6.1 Introduction% K. M8 s& R; {# b* @' t
6.2 Basic results
% U* ^% e6 z( X2 h- \- [% G6.3 Multiple linear regression
1 X) n6 f; c6 L5 A6.4 Analysis of variance4 G$ r* ?# m0 m( [* \
6.5 Analysis ofc ovariance2 X; v( e/ @5 w- ~0 ^  L! l- {5 Y
6.6 General linear models9 N5 B. n) Q& ^* y9 `
6.7 Exercises
" B" y$ [3 ?" y- x8 h/ t% o& v7 Binary Variables and Logistic Regression% m& y/ _% p/ x4 {  v- D
7.1 Probability distributions" b$ M6 v* E: b2 d& D' W5 E$ y2 j
7.2 Generalized linear models/ b5 I8 L1 y4 Z: j9 @( g/ L
7.3 Dose response models
, T! [" O8 d8 B. D& u; d, Y' E7.4 General logistic regression model
: X+ A/ O* B/ b8 Z. H7.5 Goodness offi t statistics
% i8 I3 Z# b+ j; r) d7.6 Residuals
1 O5 e+ q) U; Y3 E7.7 Other diagnostics
0 r: s& Y( V% ^0 @# `/ `7.8 Example: Senility and WAIS# c5 |0 O. Q9 a. u
7.9 Exercises3 m2 [2 `9 B7 u: e8 _
8 Nominal and Ordinal Logistic Regression/ m+ O* [9 w2 G. g. b
8.1 Introduction$ L; V/ O, c. x  {
8.2 Multinomial distribution# `) v* t6 T4 }+ N- p+ ?
8.3 Nominal logistic regression
" A3 t$ u- s) J7 R! Z8 \8.4 Ordinal logistic regression8 F" J1 t+ l8 `9 r
8.5 General comments
+ v' H' w- S" C) S8.6 Exercises( @% s# M9 U& L. l
9 Count Data, Poisson Regression and Log-Linear Models
3 ^4 B& T: _! T3 Z) q2 }2 Y4 T2 {9.1 Introduction6 u. W; u  B3 g1 r# l
9.2 Poisson regression9 d; I& T% J) U  N
9.3 Examples ofco ntingency tables6 p. t2 S( n6 r2 L* y
9.4 Probability models for contingency tables0 T8 }) h& M$ W; D% F( L1 A
9.5 Log-linear models
2 i; G) i: G2 h. J0 K" y! }9.6 Inference for log-linear models9 @* {! E# P, c$ P4 h
9.7 Numerical examples
$ t6 b% p, Q5 W/ Q1 T# J( z9.8 Remarks! ?1 U) @5 b# }1 J: Q9 u8 E
9.9 Exercises
8 c+ X* g- F0 t* D; V? 2002 by Chapman & Hall/CRC- C) j4 D) Q1 r. S. Y1 m5 P% e# c
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10 Survival Analysis
4 D7 f$ v! ]/ A- a4 P. r6 k$ M: q, {$ @10.1 Introduction0 u0 i) E& F9 F2 ^8 L6 u7 m4 g
10.2 Survivor functions and hazard functions
9 V% |$ E3 L; ?) c! R2 `10.3 Empirical survivor function3 Z6 C' C+ t& I0 c. e* _
10.4 Estimation  d0 b( g6 d' E7 i% s+ `% |
10.5 Inference0 u* F* L- Z, v) |5 L+ K4 }# {
10.6 Model checking
- @" y, G9 W( `" S3 R3 o* v; ^10.7 Example: remission times0 P. I3 {" t; A& ]& a( }
10.8 Exercises
0 \/ o- W$ S' b# z11 Clustered and Longitudinal Data
, w% R' L9 o8 c3 v' y% i2 r, b11.1 Introduction5 V, _) v# A  c( G0 T
11.2 Example: Recovery from stroke& g5 s+ u* ~" M* `6 @
11.3 Repeated measures models for Normal data# G, N& H8 b: y5 r+ @. a0 Q# L- B
11.4 Repeated measures models for non-Normal data
, ^/ I' t; `  C: p: C11.5 Multilevel models' m' K) P" g  f) k
11.6 Stroke example continued
$ O4 Q$ [( B7 I11.7 Comments
5 z& V7 {) ^; }7 o0 m$ d5 X* p6 ^11.8 Exercises
3 ]* c% H  `7 c; h) D3 ESoftware
, P# y4 q, ]) u) \3 |8 {, e+ u9 wReferences, j: x6 ~, P; n/ J) H
? 2002 by Chapman & Hall/CRC
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% R" A3 A0 y# lPreface
% W4 k/ \; h, f0 zStatistical tools for analyzing data are developing rapidly so that the 1990
, b7 _% R' c* B& n- H8 Z$ Gedition ofthis book is now out ofdate." P+ k; O" @1 r9 S
The original purpose ofthe book was to present a unified theoretical and
- H; V* C7 w) |2 P0 s2 V" \conceptual framework for statistical modelling in a way that was accessible
0 ?5 t9 ~" \% z2 z. B( K4 E' w; v+ Sto undergraduate students and researchers in other fields. This new edition
& d7 d- ^) @5 L) x9 `; l9 Ohas been expanded to include nominal (or multinomial) and ordinal logistic
" {$ k& V# a# |1 K  c/ mregression, survival analysis and analysis oflongitudinal and clustered data.3 C+ u7 J# B: T+ `. ~8 i, [# @8 d
Although these topics do not fall strictly within the definition of generalized; _( \$ z) H+ F2 y
linear models, the underlying principles and methods are very similar and2 j9 E# x' e' p. w' K/ _( D8 U
their inclusion is consistent with the original purpose ofthe book.2 w3 ^1 J& l' V* t$ ?: r$ K+ B+ A7 m
The new edition relies on numerical methods more than the previous edition
$ r! \: b  c! @0 \6 W$ q0 H4 Q, Ddid. Some ofthe calculations can be performed with a spreadsheet while others4 A7 O: R# |! b% O$ T+ A/ e- _- G% u) v
require statistical software. There is an emphasis on graphical methods for
6 n$ |( t8 t) K" j, ^% p' Texploratory data analysis, visualizing numerical optimization (for example,7 q: Y; W" C: J2 u" ]
ofthe likelihood function) and plotting residuals to check the adequacy of
1 ~1 [2 H; w+ h4 J3 _models.9 O* e5 }/ t) E- y

: [0 W5 A9 v  W1 {- hIntroduction9 B% o7 t) R0 q& `, D
1.1 Background
# i9 O+ [; s$ F8 Q! bThis book is designed to introduce the reader to generalized linear models;
. q9 p: a8 }; z5 E, p+ F6 jthese provide a unifying framework for many commonly used statistical techniques.
1 L' B& n* @" E2 p, PThey also illustrate the ideas ofstatistical modelling.
7 O& c9 X7 C9 _2 B8 hThe reader is assumed to have some familiarity with statistical principles4 `" J" a3 ?2 W7 G0 b
and methods. In particular, understanding the concepts ofestimation, sampling$ O3 x, w" G4 M  M1 q
distributions and hypothesis testing is necessary. Experience in the use8 m( x' z& i/ g$ I2 I6 r
oft-tests, analysis ofv ariance, simple linear regression and chi-squared tests of6 o; U1 R! p' ^. W0 \4 E  w" e
independence for two-dimensional contingency tables is assumed. In addition,
1 s( W, [: X, w( s1 _, csome knowledge ofmatrix algebra and calculus is required.
+ P. c; W# m5 R# ]8 k( sThe reader will find it necessary to have access to statistical computing4 u3 E4 b0 [. F2 V. `
facilities. Many statistical programs, languages or packages can now perform4 T' ?4 M1 k! f6 d$ |
the analyses discussed in this book. Often, however, they do so with a different
' H  y% U/ j9 Y9 Y$ w) N, yprogram or procedure for each type of analysis so that the unifying structure
3 y( k2 T0 o, Ais not apparent.
1 u# V: [% Z8 YSome programs or languages which have procedures consistent with the2 ^, [7 t& B  Q  W
approach used in this book are: Stata, S-PLUS, Glim, Genstat and SYSTAT.1 R, A6 [6 `, E7 M
This list is not comprehensive as appropriate modules are continually
( c' G& r/ N# P' @being added to other programs.' j) |1 }+ y& o* Y
In addition, anyone working through this book may find it helpful to be able% a4 E/ B( I% U, J: o+ l. J
to use mathematical software that can perform matrix algebra, differentiation2 r% j- i0 a, w' t! j! n, Z
and iterative calculations.
6 \& H# T5 l9 O# j% X' L4 J! D* B1.2 Scope
0 `* {; m/ S6 G- ZThe statistical methods considered in this
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) b1 u7 W$ l- M4 Dv威枝& G" {. {& e% ?. q, A+ Z8 |
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