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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' U5 c2 d+ V! o. S8 j
Preface
8 o  C7 J; v* [5 r  F7 t# Q% T" s1 Introduction4 F7 F: N' Z4 ]8 b7 L7 f' X9 X2 y# Y
1.1 Background
: U: _4 f# k& O* c+ h: K% w1.2 Scope
, L9 R2 c* f/ ^2 s: l/ _1.3 Notation
& n% v, m# u; h1 S& k& B5 N1.4 Distributions related to the Normal distribution
+ _) a1 H% b5 F1.5 Quadratic forms. V) h  Y+ o! ]. L2 a
1.6 Estimation
1 B- B' l9 H4 r6 o4 r1.7 Exercises  S+ F/ }- t# l) V
2 Model Fitting
1 |3 \6 c& Z" b+ P9 M2.1 Introduction
2 Q: @! G$ [) J7 L2.2 Examples$ w0 l4 T% U8 [
2.3 Some principles ofstatistica l modelling
0 b) f- @; F5 q3 _5 _, U& u5 B2.4 Notation and coding for explanatory variables+ D, s9 W5 o/ H* T% Z
2.5 Exercises
  i0 {0 z$ A9 g9 G3 W# g3 }3 Exponential Family and Generalized Linear Models0 l: ]+ h( h2 U
3.1 Introduction
3 @' r9 S- V" e. @6 C# E9 [3.2 Exponential family of distributions2 x8 z; J- q" Y7 H2 ^( l
3.3 Properties ofdistribution s in the exponential family" \3 @' j! |$ `4 [( d
3.4 Generalized linear models
1 R* m/ M2 j% L+ f" y! L7 |3.5 Examples: K- o! K9 n& m+ a
3.6 Exercises" m. b* M; i6 ^. E$ Y
4 Estimation
( y7 Q+ |# ~8 `' J% ^% c8 z2 F( s4.1 Introduction9 N; S  L) f, y
4.2 Example: Failure times for pressure vessels# b- b; A" a% [# h$ J( L: U4 p5 u
4.3 Maximum likelihood estimation) {: l: L" S( L+ u
4.4 Poisson regression example0 h! J1 H& j8 e0 t6 |$ P
4.5 Exercises- o4 r) v; M' Z! U% C6 F2 x
5 Inference
! M- z: W" ^3 a% H" j! r5.1 Introduction
% y/ U/ w8 o2 r" Y5 K2 P4 H' J1 k+ g5.2 Sampling distribution for score statistics$ O' }& k" S& Z. m
? 2002 by Chapman & Hall/CRC
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( }5 X8 @0 H% L# e/ m5 Q5.3 Taylor series approximations
/ X7 ~+ P) g8 Z5 ]* A# l2 r, d# p5.4 Sampling distribution for maximum likelihood estimators( i* \1 E! \/ k& L+ E( D1 _
5.5 Log-likelihood ratio statistic$ h9 y1 t: H" ~2 w  l7 P
5.6 Sampling distribution for the deviance- C0 m+ ~/ x1 J; e
5.7 Hypothesis testing
) c" G* u* b* v4 a9 g/ R, {5.8 Exercises
5 p$ t; V+ k& a+ {6 Normal Linear Models2 `9 f7 k0 o% }- T% [
6.1 Introduction, B, T" t$ M' \/ D. J
6.2 Basic results
" d+ [7 N4 t8 U" z. r* b1 ?$ ^; f0 \6.3 Multiple linear regression
0 [$ D9 H% K% d7 |% s$ U6.4 Analysis of variance9 E6 j) z8 e8 I
6.5 Analysis ofc ovariance! ]9 y( w+ q& B; k
6.6 General linear models
! @. a& B3 P) }6.7 Exercises
  `" i4 M/ C. E" M3 R7 Binary Variables and Logistic Regression# z7 P5 M: [0 B0 y! G+ ~% K- G, S
7.1 Probability distributions
9 f3 I# h0 s5 c1 B- n7.2 Generalized linear models9 o# i! \: C. M5 W4 W) a# U; s
7.3 Dose response models
- |. Y. ]1 F1 B3 F; V- X, m7.4 General logistic regression model7 w  \" v& p/ |4 C8 ^3 U
7.5 Goodness offi t statistics$ Y, e# `2 o( c5 y2 F9 x
7.6 Residuals- Y! G7 L" o1 Q# H! k" _: I8 d6 q
7.7 Other diagnostics4 k/ a% x% n3 j0 C4 }# P( R
7.8 Example: Senility and WAIS
8 ~: E( W/ M- q) V( G- z% H7.9 Exercises, ~2 U1 `8 }2 A3 M5 d& _& c
8 Nominal and Ordinal Logistic Regression
6 X1 ?& `) f4 B  V* ?+ T8.1 Introduction1 m# T5 I5 ^- A6 w
8.2 Multinomial distribution4 v" G1 U2 K5 G5 j
8.3 Nominal logistic regression0 X" T# }3 r  |- a; R
8.4 Ordinal logistic regression7 G5 W; b  f' h! Z" V; H1 _
8.5 General comments
* M9 Z! A  \' Q# M) Z9 Y8.6 Exercises
+ Q; Q  g6 {7 C7 O8 ?; ~! o2 `1 @9 Count Data, Poisson Regression and Log-Linear Models
6 @  f% w' Q" N9.1 Introduction/ H1 r# E- l+ [) Z
9.2 Poisson regression- i4 p. m, v* c3 ?0 P( A4 o/ q! x
9.3 Examples ofco ntingency tables
: p& k% f8 ]* C. f- S0 @0 B- Z9.4 Probability models for contingency tables. L9 y8 T# I- }* H* x
9.5 Log-linear models0 g. W9 g* ^0 t4 a. |5 r) J
9.6 Inference for log-linear models& }* |/ p% G4 a2 |
9.7 Numerical examples) E3 G0 {& _" E- X1 \4 p
9.8 Remarks
9 C2 T* S* j# K) E7 ]* ~9.9 Exercises& H+ M. g* N  S% v' N
? 2002 by Chapman & Hall/CRC5 N4 z7 ~* m: p* Y7 d
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10 Survival Analysis
) w! H8 {4 }  L5 A10.1 Introduction2 I7 f5 T7 x! X# u; a% \2 o
10.2 Survivor functions and hazard functions
$ Q) _+ C* m6 f6 q. t10.3 Empirical survivor function
% E) m( X  S! c7 ^; B* f0 P10.4 Estimation4 w6 z) ]6 v1 W& \' @2 r" M
10.5 Inference* |$ D) f$ @9 w+ S
10.6 Model checking0 }  I5 H- ^9 P
10.7 Example: remission times
5 i1 ^! K) \% N3 d10.8 Exercises
' w, z* a: M1 h5 A/ y; E* A11 Clustered and Longitudinal Data& l0 \# _4 b8 W5 Z% O+ Z  i3 P
11.1 Introduction
( N4 v& e0 M; O- x1 q11.2 Example: Recovery from stroke4 E2 ?' V. [6 O0 L7 O. f
11.3 Repeated measures models for Normal data6 r6 n- N0 {  R7 p: c$ Z( B
11.4 Repeated measures models for non-Normal data" a0 h! n6 ?6 V8 l" T: q" e8 g
11.5 Multilevel models
  s, R  u3 B! j8 N' p0 k11.6 Stroke example continued
' m. c' U+ i" d, Y+ ?: l11.7 Comments
+ P, ?9 c% T1 F' P11.8 Exercises
- ~# I5 k4 f; P  F: ]  R5 gSoftware! G- n% n+ S) G* f- u
References" R8 V& C$ ~! P( o
? 2002 by Chapman & Hall/CRC
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Preface
* [( j6 Z0 \: ]9 _- s5 UStatistical tools for analyzing data are developing rapidly so that the 1990
2 l/ ]& n* j4 G, v+ Ledition ofthis book is now out ofdate.
) G# `& l! d7 J9 O4 O4 EThe original purpose ofthe book was to present a unified theoretical and5 z. F9 a5 j3 h( k4 p% v
conceptual framework for statistical modelling in a way that was accessible
0 `  H1 h* B/ c5 H) [to undergraduate students and researchers in other fields. This new edition$ U1 Z+ D" f1 Q' l
has been expanded to include nominal (or multinomial) and ordinal logistic
9 g0 ?& L+ x- _# P6 @2 n* P3 yregression, survival analysis and analysis oflongitudinal and clustered data.
" u7 e8 F2 I# A! }, WAlthough these topics do not fall strictly within the definition of generalized
7 G6 Q, w  R  ?3 Z: olinear models, the underlying principles and methods are very similar and' J( O+ N0 K- H2 h7 Q4 h
their inclusion is consistent with the original purpose ofthe book.
9 n  k9 }! F" A+ h& dThe new edition relies on numerical methods more than the previous edition" h6 X5 ]( y  u
did. Some ofthe calculations can be performed with a spreadsheet while others, u  j$ V7 B0 |" p7 @0 g
require statistical software. There is an emphasis on graphical methods for
8 a# ?# u' k# M) {& G7 s; y! f$ kexploratory data analysis, visualizing numerical optimization (for example,2 t% E$ Q7 P1 F, S8 E' e
ofthe likelihood function) and plotting residuals to check the adequacy of
2 h9 r2 A5 d& ~; E: smodels.* r7 y1 e' t4 ^  g: L

7 J5 U% n: w( Y5 KIntroduction
6 |$ J6 w* ~6 S) V8 I' _* B! b1.1 Background
! D: A/ U/ t3 {6 s9 oThis book is designed to introduce the reader to generalized linear models;
8 C3 \# J8 L6 ?7 }. |% ~# Rthese provide a unifying framework for many commonly used statistical techniques." w) A. k2 Z* Q! `
They also illustrate the ideas ofstatistical modelling.
6 K8 e, b! }9 t8 Y& H" F; `( xThe reader is assumed to have some familiarity with statistical principles
$ ~  \0 [2 m; c, N/ F# B- Pand methods. In particular, understanding the concepts ofestimation, sampling
% f; p; H2 I) N# H- u* A8 J4 ddistributions and hypothesis testing is necessary. Experience in the use2 e; p9 Y9 ?- k
oft-tests, analysis ofv ariance, simple linear regression and chi-squared tests of
6 r4 \2 C+ C1 q6 H) l! E" Gindependence for two-dimensional contingency tables is assumed. In addition,6 n) q! z' Z# R+ G$ Z4 B- i
some knowledge ofmatrix algebra and calculus is required.
2 l8 U# ~! W- v! K! SThe reader will find it necessary to have access to statistical computing0 q: c( O( s- w% u. b
facilities. Many statistical programs, languages or packages can now perform/ [, _  `/ y9 s' T- Q" H
the analyses discussed in this book. Often, however, they do so with a different
5 f* o) k, e% _5 ?% Dprogram or procedure for each type of analysis so that the unifying structure
9 C1 L) a0 P6 R8 g7 `; ois not apparent.5 p( p+ G8 ]# ?) v2 ~" [6 a
Some programs or languages which have procedures consistent with the9 x8 b1 o# w. z. v  f! L, L
approach used in this book are: Stata, S-PLUS, Glim, Genstat and SYSTAT.# y5 ?; G5 s$ M1 P: r
This list is not comprehensive as appropriate modules are continually
/ Z) y1 Q; a( c# H- }/ Ebeing added to other programs.
0 y7 y9 ^$ G2 Z, C! E  b6 N% D# ]In addition, anyone working through this book may find it helpful to be able' F3 p( q3 F6 {8 j) T
to use mathematical software that can perform matrix algebra, differentiation
6 u2 ^; L7 m) M5 ^8 f+ ^! gand iterative calculations.
$ j0 o) x1 m4 u+ V- }1.2 Scope. _. P, E& D/ J& ]4 z
The statistical methods considered in this
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