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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
) j* K0 [& d/ K  p+ X% ~Preface
3 R) H% y  y0 w+ d' Q# _1 Introduction" O7 b) R9 `+ H, }* ?' ~1 F
1.1 Background
" q0 V2 ~% f6 R5 b  _5 M+ j1.2 Scope+ t! S+ B1 e- X
1.3 Notation9 [1 J2 w2 P2 L. @9 u! l) M
1.4 Distributions related to the Normal distribution& L5 A8 [( e9 N+ c3 `8 N
1.5 Quadratic forms
( T9 X" K4 K$ F' i1.6 Estimation
0 B* K! ]4 D! c2 V* g$ j5 v1.7 Exercises8 o" @3 a% N9 h4 D2 `; f5 `
2 Model Fitting
8 _. v$ O# Z  n1 b3 a9 c! K/ C2.1 Introduction
: }% X4 z" T: R2 l0 F7 I2.2 Examples
* m$ z4 d9 T& }+ u& m/ B) J6 H2.3 Some principles ofstatistica l modelling
% H$ L: V% b: D! Q% g0 l. s2.4 Notation and coding for explanatory variables
8 u; ]& P$ D- M2.5 Exercises  ]9 q  }5 D/ Z/ B! w, @
3 Exponential Family and Generalized Linear Models
' ^) G' u+ \: k3 H( a  B3.1 Introduction# \# B& E' L. j" F
3.2 Exponential family of distributions
2 [6 T) @+ ?/ Y3.3 Properties ofdistribution s in the exponential family3 Y* \- d" ^1 P! t4 n3 G2 j
3.4 Generalized linear models
$ h. e4 b$ h+ C& m2 o' s3.5 Examples
- a4 S" A+ ?4 P* R! z7 c* q3.6 Exercises$ h- S5 W% z1 n' j/ z: B. ^3 y# n
4 Estimation
+ [9 a2 Q3 _  k3 S4.1 Introduction% L4 q5 M5 w0 Z- _4 q! u: c, t* ?
4.2 Example: Failure times for pressure vessels: s+ ]# c. s' O1 c
4.3 Maximum likelihood estimation
4 {" w: }& p' c" g  V# p% `; g4.4 Poisson regression example( l/ L5 a* e3 Y1 R! M
4.5 Exercises
- j- g3 l: a3 v( |1 w5 Inference
: N* c' y$ ^. V1 a( F' d5.1 Introduction
/ C) I3 T+ F. c; @  P5.2 Sampling distribution for score statistics6 h+ R8 M$ {- C% R. L9 O$ ?
? 2002 by Chapman & Hall/CRC
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3 {5 D% ]4 D7 Z9 c9 S3 D5.3 Taylor series approximations: K9 @4 i4 R2 K
5.4 Sampling distribution for maximum likelihood estimators
  K. y) I2 L& e2 U: c- B5.5 Log-likelihood ratio statistic8 e0 ^' a, y" h$ c0 u! }- S) P% e
5.6 Sampling distribution for the deviance$ ?, ~# O" ^. V* `8 S
5.7 Hypothesis testing8 j" O$ j3 i# s' ]$ i2 C7 x
5.8 Exercises# l5 @  ^+ {% H+ R2 i0 {. X
6 Normal Linear Models  \: ^2 u$ j$ M" d8 C0 E
6.1 Introduction! Q, \1 K- O) [( d6 i0 t, k
6.2 Basic results$ `8 T+ z- r% E5 J6 {. G" ]" W: f
6.3 Multiple linear regression8 G- r: k9 u7 z  D3 Q$ n- d
6.4 Analysis of variance
9 B( o% U& K% g' g" n6.5 Analysis ofc ovariance
0 z3 }( O* u" h7 ~' Y6.6 General linear models3 y! O) n5 b2 i$ c; k
6.7 Exercises
: A& p9 |* o( T; U. w8 Q3 m: X7 Binary Variables and Logistic Regression& f6 ^6 p( L9 P4 D+ m
7.1 Probability distributions  H8 D) ^, o# ?5 J/ [2 v0 X" Z
7.2 Generalized linear models
0 \  @: Z: p4 d7.3 Dose response models
8 ?0 @: D2 c6 y9 s7 h7.4 General logistic regression model
' H, `+ e; w$ ~7 h- J7.5 Goodness offi t statistics, }  Z9 N7 g. C; J+ @% q2 B" \
7.6 Residuals
6 }- d+ j/ y% U" ?7.7 Other diagnostics/ z5 n/ a4 U3 G: ^- s3 w
7.8 Example: Senility and WAIS
8 i/ ^% V3 j6 N8 t+ O! ?7.9 Exercises
" L1 x7 |) j% |# b8 Nominal and Ordinal Logistic Regression
8 R% ]+ E( P9 H9 }# F8.1 Introduction
) V( ~5 J0 t3 n8.2 Multinomial distribution# R) X! e3 d2 {
8.3 Nominal logistic regression- s/ |% d6 d1 ]" ?' ?. p% ]
8.4 Ordinal logistic regression% e8 f! `6 n4 t# D8 Z3 W: @" {
8.5 General comments
" V# H: S7 T* w8 g& F" u8.6 Exercises- r3 o5 r6 o! E2 _& O) h# T4 a: p
9 Count Data, Poisson Regression and Log-Linear Models' ?3 l5 a0 ]7 W0 y' i
9.1 Introduction1 {+ f: Z6 g, P5 h9 o
9.2 Poisson regression& [) G) B7 r: L
9.3 Examples ofco ntingency tables0 P# h: w, t* B( C- \
9.4 Probability models for contingency tables
& D4 p6 S# m) D9.5 Log-linear models
9 j& Q' y4 C4 E: u3 u1 Y2 n9.6 Inference for log-linear models. \* m7 j/ J, g1 D# S- K
9.7 Numerical examples
# G5 l4 l; h8 q7 n/ B' V; u1 m) O9.8 Remarks
8 F) W3 k$ C5 E3 e9.9 Exercises
5 _; M# N. h1 w, f! Y7 E, k* V? 2002 by Chapman & Hall/CRC
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8 l* H0 j4 s( g( _10 Survival Analysis
  n1 {+ L" Y' Z' P% b, R, S: l10.1 Introduction, v. S1 u) p6 w9 E1 \
10.2 Survivor functions and hazard functions' L- N2 x6 c( E8 j9 a) e
10.3 Empirical survivor function
( m8 W" l: ]; K  q/ w10.4 Estimation8 t: n" e$ a2 `. ?9 E$ x$ f
10.5 Inference
% B  s, w5 Z" y- W0 i10.6 Model checking
/ ^/ S, G- s$ V# t9 p10.7 Example: remission times
) C) ?( }; |9 T4 ?10.8 Exercises0 z/ U1 l, ^1 V$ ~8 {' k3 g
11 Clustered and Longitudinal Data
5 p  q1 |  p) b2 F2 N. E11.1 Introduction
4 f- i+ b( \, g: r11.2 Example: Recovery from stroke
4 e2 h- h5 p- |& D- C11.3 Repeated measures models for Normal data
& N6 |+ `. }/ D11.4 Repeated measures models for non-Normal data
  h' u6 l* j( Q" H11.5 Multilevel models
. }! d) E9 m! E  A/ l$ W11.6 Stroke example continued
8 \% v& H) s; q' u- s% Z11.7 Comments8 }; w$ F$ d4 Z: m
11.8 Exercises
5 X6 i2 M( l0 ~" SSoftware
) {! \4 n5 Y: U- q& n. N5 A! mReferences1 H, p4 k' E6 O3 t2 e) L
? 2002 by Chapman & Hall/CRC
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Preface
5 ~3 y$ r: U# ~- T" G) ~Statistical tools for analyzing data are developing rapidly so that the 1990- c) E( c" Q4 i, \3 ?+ b$ y
edition ofthis book is now out ofdate.
4 N" h  u. K; }( QThe original purpose ofthe book was to present a unified theoretical and
0 p( _: U. E7 S. H, Fconceptual framework for statistical modelling in a way that was accessible$ z8 [# L7 U, M7 Y! N
to undergraduate students and researchers in other fields. This new edition1 R$ J2 d& [( }' e+ ]
has been expanded to include nominal (or multinomial) and ordinal logistic: i! }2 s- X6 D2 A' C
regression, survival analysis and analysis oflongitudinal and clustered data.8 M. \5 G( H* _; d# L! C/ y
Although these topics do not fall strictly within the definition of generalized) A/ g' S3 ^/ H$ A: D
linear models, the underlying principles and methods are very similar and+ _% J: p0 b$ I, J3 N
their inclusion is consistent with the original purpose ofthe book.$ E8 E. [. L8 q& T: i& |" q6 D
The new edition relies on numerical methods more than the previous edition  b4 R* }* b) O: c% k7 Y
did. Some ofthe calculations can be performed with a spreadsheet while others8 b. J$ X$ l; `1 h! T& I
require statistical software. There is an emphasis on graphical methods for
% z* n4 R1 n9 K5 g8 N/ Mexploratory data analysis, visualizing numerical optimization (for example,
! M7 ~. I# r2 `* ]$ F+ i1 p: Q) Uofthe likelihood function) and plotting residuals to check the adequacy of
- q) |3 J( y; Z( K# ]6 U! o# O1 cmodels.
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Introduction
6 i1 n5 ]3 p% d) b: ^4 q: e' m1.1 Background' S  f- r9 \* z- Y1 s3 ]  N
This book is designed to introduce the reader to generalized linear models;
' {* G) s7 H5 U  g3 `these provide a unifying framework for many commonly used statistical techniques.( Y$ v' \5 l0 r4 X* @
They also illustrate the ideas ofstatistical modelling.
# @0 [& j( c; \  AThe reader is assumed to have some familiarity with statistical principles
6 u- v. E0 L% _( U5 wand methods. In particular, understanding the concepts ofestimation, sampling
5 O5 g* E! W& T3 idistributions and hypothesis testing is necessary. Experience in the use
; B# }% g1 _+ p# L) qoft-tests, analysis ofv ariance, simple linear regression and chi-squared tests of
( t( B6 C) k' n0 |# _; Q3 [( Nindependence for two-dimensional contingency tables is assumed. In addition,5 C" q9 s$ i$ P" e
some knowledge ofmatrix algebra and calculus is required./ {, b4 ?+ Z- [+ [5 E+ T- M* Q+ D
The reader will find it necessary to have access to statistical computing/ ]# W7 D6 c4 [
facilities. Many statistical programs, languages or packages can now perform
3 |5 J* g5 I" {6 m  E! H' w* h1 bthe analyses discussed in this book. Often, however, they do so with a different3 h+ K/ }1 b* |! a5 h* m! w/ D: [
program or procedure for each type of analysis so that the unifying structure
$ S1 D5 X+ ]2 n, K& _is not apparent.
# G) S+ ]$ m5 M9 ~: eSome programs or languages which have procedures consistent with the
# U0 a  ^8 p, \& I6 bapproach used in this book are: Stata, S-PLUS, Glim, Genstat and SYSTAT.7 R1 q9 e) \/ C+ h# m
This list is not comprehensive as appropriate modules are continually
8 m" v( E$ S' n1 t1 p: G: S" B$ fbeing added to other programs.
" A9 l* F' P& n7 C+ u/ g& L+ J: mIn addition, anyone working through this book may find it helpful to be able0 b7 v' @2 Z5 c! k+ @! |2 y* p
to use mathematical software that can perform matrix algebra, differentiation5 P9 O" d1 r2 N* u4 K4 {8 m7 \
and iterative calculations.. }3 W3 j* z: L# `
1.2 Scope( t" {* l9 n! F0 G; i3 Y3 \
The statistical methods considered in this
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