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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
/ \  S& l! }, A# yPreface
6 s6 V0 \, ?7 a, [+ p9 ]1 Introduction
! b' S7 _2 _" [+ N7 E: R1.1 Background$ [; m+ ]5 {5 ?& r6 y
1.2 Scope
0 H. U2 D4 r. m" }1.3 Notation, |& d% Q" e3 b. L7 w  Z: i4 A
1.4 Distributions related to the Normal distribution
" G5 A7 c1 x) N3 h& x1.5 Quadratic forms
, n/ e5 q6 q' D: V1.6 Estimation* W0 v. v& b" A
1.7 Exercises
  q' K4 l0 A4 ?- u5 W- `0 P2 Model Fitting% X+ `5 ]( Q" q' Y& M" O
2.1 Introduction2 H  F# N5 G$ S6 E* Q1 [, X' s- T
2.2 Examples
, T% S# ^5 h  B4 H. T2.3 Some principles ofstatistica l modelling
7 ]# }8 I+ w$ K' ~! q% \% a9 I2.4 Notation and coding for explanatory variables
- s- q* V4 P7 U3 E% Z2.5 Exercises
! h4 a3 V7 `  @3 Exponential Family and Generalized Linear Models8 \7 q1 Q% B$ V) z" N% L
3.1 Introduction3 _4 K$ _% u& A$ M- V
3.2 Exponential family of distributions
' z: A& N, b" s  B% E3.3 Properties ofdistribution s in the exponential family; F& E% W6 p& C' }8 g  q* W
3.4 Generalized linear models3 v# j2 z) G$ S' W5 }
3.5 Examples' {* l" f+ e& T- O) Q' i8 _- X
3.6 Exercises
3 m9 g- P5 M* o6 Y& X4 Estimation0 U" E3 i4 Z, L: X
4.1 Introduction$ [; d8 L) {; M+ a3 _# }
4.2 Example: Failure times for pressure vessels
/ c1 y* ~8 ]/ f9 \0 V4.3 Maximum likelihood estimation* e  V" R+ l: s+ S$ k
4.4 Poisson regression example! G2 `, P/ ~9 z- u9 M( G
4.5 Exercises; a* f' q5 E0 ~  E& N  B7 F$ J
5 Inference
7 L! E/ u% _6 O5.1 Introduction
$ P- _" I4 U: \- s3 y& I5 ^& u5.2 Sampling distribution for score statistics! ^9 @6 b. c+ ~
? 2002 by Chapman & Hall/CRC4 z# n  c$ V, h. S$ V1 [  T1 Z
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5.3 Taylor series approximations& [  L: |& G  p. m  x/ x0 Z
5.4 Sampling distribution for maximum likelihood estimators
! y- s; A; S: P, D4 B& h5.5 Log-likelihood ratio statistic
) N9 D: e! O# ?/ v7 x( F4 y( C5.6 Sampling distribution for the deviance
, n0 w( Q& Q. S: P/ d% e( r8 q5.7 Hypothesis testing+ u0 h1 g% i' `8 S) G" g5 `
5.8 Exercises
5 L- O' N& q  t( ?, h6 Normal Linear Models) @2 H  m8 n; u1 b8 c7 O
6.1 Introduction) L3 a  B9 y, u9 d# ^: `; w( |# N* M
6.2 Basic results
6 V& v$ w% t- |8 F& y6.3 Multiple linear regression. ~4 x5 b& S, Y( W
6.4 Analysis of variance: f7 f3 c; Y. n/ L8 d8 K
6.5 Analysis ofc ovariance
. @5 P. B# T- I. r* ?3 O6.6 General linear models
3 P, ~4 e. _& o7 m" Z, y* ]9 Z6.7 Exercises9 F! k( `9 C- n( M2 H8 T
7 Binary Variables and Logistic Regression( [- v; N# T" [( b' @: P
7.1 Probability distributions( p# w8 V7 v6 X+ |* n% m
7.2 Generalized linear models, u% O' s# b  e: N
7.3 Dose response models
; d. d- A! |6 \* f, V0 T2 x& C7.4 General logistic regression model
( i, X0 {+ J( g+ c, j* O7.5 Goodness offi t statistics& f0 H$ O8 x7 }7 n  P4 q/ s0 e0 Q
7.6 Residuals: e+ Z. t5 N1 s  [' c5 _
7.7 Other diagnostics
9 D* ]; ]( G2 U4 T& M1 q' ~: ]7.8 Example: Senility and WAIS! O3 y. A, J0 R* g4 j( `6 o
7.9 Exercises1 A/ z9 I9 l) j; N
8 Nominal and Ordinal Logistic Regression
/ s  |; u7 z6 p8.1 Introduction9 f7 I2 ~2 f. [: u" M8 I/ z8 L' P$ k
8.2 Multinomial distribution
2 T3 K' _3 C+ r( w) d8.3 Nominal logistic regression
: q7 O' k3 n  u- ]) D$ u# E1 Q8.4 Ordinal logistic regression/ }8 g% X/ w1 x6 ~7 T3 Q2 d1 t( b! O# j
8.5 General comments
: v1 t8 w8 P% M; J# ?* Z6 u8.6 Exercises$ m) h& y& m. E, x. A7 A
9 Count Data, Poisson Regression and Log-Linear Models
+ N% X( l0 e, C: ~: v8 i4 A9.1 Introduction
, l3 n5 ?+ ?! O" s5 m; N9.2 Poisson regression9 [7 O9 t9 o) {# C8 p
9.3 Examples ofco ntingency tables4 E2 r: x# g, F! r' ~* n
9.4 Probability models for contingency tables
, R# f7 r5 \4 j; y; L9.5 Log-linear models  n" i% T% x" ]3 ?* x% |7 u
9.6 Inference for log-linear models; b2 ^+ G2 Y/ ]) \$ a- O4 f# \
9.7 Numerical examples
: f" z2 X7 T9 f9.8 Remarks
0 r2 b- h3 K2 F$ U( G1 M8 k5 R/ t9.9 Exercises
* ?$ S6 {2 W" X4 f? 2002 by Chapman & Hall/CRC
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10 Survival Analysis
9 t/ T+ o. y( Z7 B: H8 F# a10.1 Introduction
" B# {4 w& P6 S; Y$ A10.2 Survivor functions and hazard functions
( g& R5 }, p6 ]0 I. S4 W& V10.3 Empirical survivor function1 D8 _$ |3 s  M: S6 _# B
10.4 Estimation$ m( o1 j1 [9 q6 h
10.5 Inference6 [' l- P5 e  \! F9 D/ b! b
10.6 Model checking+ R0 u4 L9 P9 u
10.7 Example: remission times
/ C7 h! C3 N" ]10.8 Exercises4 `* G! T+ _8 i8 a
11 Clustered and Longitudinal Data: i5 s( X& I" Z! c7 n
11.1 Introduction5 q6 P+ w$ D7 _' f/ E% {
11.2 Example: Recovery from stroke
; e; \! H3 L$ S+ ~# ]- M5 G11.3 Repeated measures models for Normal data
: j* S; e$ c) Z; `8 A; e6 U11.4 Repeated measures models for non-Normal data9 d$ S' t# v0 `( ^" X4 W) a; ]
11.5 Multilevel models) y, [9 B% e; Z3 O: C
11.6 Stroke example continued
$ h) }8 o8 M3 ~2 E: \' G9 x11.7 Comments
. I4 g! `) U/ [11.8 Exercises
% E6 T( g: ]: G4 v7 KSoftware
+ w" v0 C0 Q4 T& G) e* i# jReferences* V) [. g% }: a' W8 Z
? 2002 by Chapman & Hall/CRC
+ u! i; _( J" K( e3 ?75 x& K- ?: _) d/ A4 r  I
Preface+ Q3 b0 n9 Q  R+ l0 T
Statistical tools for analyzing data are developing rapidly so that the 1990
  {6 [5 D/ T( _/ H" medition ofthis book is now out ofdate.) Y+ o# R: U7 L9 M& D# q
The original purpose ofthe book was to present a unified theoretical and0 ]) C$ v# T- y/ U- O& N/ Q
conceptual framework for statistical modelling in a way that was accessible2 z5 X" Q) V3 `5 J: _5 ]+ N* w
to undergraduate students and researchers in other fields. This new edition  T" \& a  J% _: S" W* d
has been expanded to include nominal (or multinomial) and ordinal logistic
( U4 w3 H. p# x8 zregression, survival analysis and analysis oflongitudinal and clustered data.1 h2 [7 c4 e0 r8 E: B$ Q2 l
Although these topics do not fall strictly within the definition of generalized% z3 ^) W0 p$ D- z, Y) a8 s# k
linear models, the underlying principles and methods are very similar and3 ?( k* t3 @. w3 O+ _# U. l, ~
their inclusion is consistent with the original purpose ofthe book.
4 C/ ]1 r3 L4 I6 h* x' R: \The new edition relies on numerical methods more than the previous edition* u7 s7 _0 w% M7 }( ]
did. Some ofthe calculations can be performed with a spreadsheet while others$ U( b9 i/ ]: U* d# [- [  y) |0 {
require statistical software. There is an emphasis on graphical methods for* ^, M8 |1 F4 k1 J  e$ Q
exploratory data analysis, visualizing numerical optimization (for example,
* A: B9 A+ ^7 Y4 K7 n7 Qofthe likelihood function) and plotting residuals to check the adequacy of+ Q- }! m/ G5 j, _1 p
models.
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- Z4 i* I; a! B/ R' O$ e! l8 ?9 cIntroduction
2 v, B1 `# g2 p5 h% o" e5 P$ i1.1 Background* G: W8 y8 Y/ J$ s: x  {/ `
This book is designed to introduce the reader to generalized linear models;
0 E5 T  {" z, \( p& }1 wthese provide a unifying framework for many commonly used statistical techniques.
# {% U$ g5 ?9 G/ x+ t9 V) z5 FThey also illustrate the ideas ofstatistical modelling.8 Z4 H1 E5 t6 W) t' J8 Z! b" H  [1 }
The reader is assumed to have some familiarity with statistical principles7 S; b3 l# T- M+ i
and methods. In particular, understanding the concepts ofestimation, sampling
! b9 b1 [8 D$ S; d4 }* n8 qdistributions and hypothesis testing is necessary. Experience in the use
0 Q7 E/ }  B4 S9 O7 l. ~oft-tests, analysis ofv ariance, simple linear regression and chi-squared tests of
. u9 H/ {. v4 G; Aindependence for two-dimensional contingency tables is assumed. In addition,
, {, K, b/ A! F) v5 O! O0 i5 usome knowledge ofmatrix algebra and calculus is required.
, Q% S0 Z4 @) YThe reader will find it necessary to have access to statistical computing
2 g) A) @) X4 X( bfacilities. Many statistical programs, languages or packages can now perform3 T5 P3 a# C9 Y* s
the analyses discussed in this book. Often, however, they do so with a different2 y% d; v  R2 u. C) m  z! l( I" l6 u
program or procedure for each type of analysis so that the unifying structure1 n, D: F' j6 Z
is not apparent.
* w0 v( J$ Q0 ^# `9 x. v9 d5 cSome programs or languages which have procedures consistent with the8 w- Y9 {/ A/ b* k' o
approach used in this book are: Stata, S-PLUS, Glim, Genstat and SYSTAT.
4 E$ e" l; o7 IThis list is not comprehensive as appropriate modules are continually
5 K" }% x& X; H. D7 q! W1 xbeing added to other programs.4 _2 |: \1 c0 m5 R  r" _3 W9 j
In addition, anyone working through this book may find it helpful to be able+ ?3 |1 a* v6 k* ^
to use mathematical software that can perform matrix algebra, differentiation. P3 W2 N2 |4 R  C, l
and iterative calculations.
( D  x7 D' _8 G6 s1.2 Scope) r8 |' x+ y4 W4 w' `1 [
The statistical methods considered in this
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