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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
% d6 ?  ]" r: \/ h8 HPreface! K  y- ^0 {% f5 R; ?' j3 G
1 Introduction( |2 ^$ f, m( ]$ i. f- a
1.1 Background5 Z3 X5 M# X. ?: Q
1.2 Scope  _/ F# V5 U' q; S1 _; I
1.3 Notation$ S) y/ \2 e5 M7 q8 F) M" V
1.4 Distributions related to the Normal distribution3 u7 b7 g7 T0 R
1.5 Quadratic forms
3 f7 P/ @. Q4 i7 M; i3 d4 a8 |$ D2 A1.6 Estimation
  H, _) b1 h& N7 U  V. H' \1.7 Exercises
# n3 y- h8 y8 N4 f. Z" l2 R2 Model Fitting
; r9 @& `% N+ }  c& t2.1 Introduction
) i) f% V0 B1 N2.2 Examples0 x! L- Y$ P# z. j. n! r8 K
2.3 Some principles ofstatistica l modelling( j1 d# t4 q) i! @3 e
2.4 Notation and coding for explanatory variables
" W5 S3 X9 g0 m* j4 S1 E( z4 Z( g3 k2.5 Exercises
( A6 S2 N, {, v3 ?3 Exponential Family and Generalized Linear Models: U8 o; l/ r- P; z1 [, S' K
3.1 Introduction& i) O$ ~4 I( _" I  z
3.2 Exponential family of distributions) t6 ~% p' J- ^7 I/ U1 i
3.3 Properties ofdistribution s in the exponential family8 g" K5 C# i8 ^$ Y/ l
3.4 Generalized linear models
/ ?+ z$ A3 [6 @/ r$ m3.5 Examples5 d: u1 o4 `2 M6 b  c2 I: r0 h
3.6 Exercises
; D( N0 G5 R9 K! t. n; G1 M3 k4 Estimation( Z# \! M5 C8 D' G- C
4.1 Introduction: {, W) J, u/ i! N
4.2 Example: Failure times for pressure vessels
& z2 t( ?  p- S7 l! a- |. T+ y; J4.3 Maximum likelihood estimation' j# l* w3 ^/ C
4.4 Poisson regression example
% l. U! x* p1 B4.5 Exercises
; B" u, G2 h0 S) z3 {/ e5 Inference
* `  ]" q7 v- P; {& ?5.1 Introduction
8 v0 j+ f6 z/ w+ m; N# I5.2 Sampling distribution for score statistics
) @# g( D: [4 ^1 c? 2002 by Chapman & Hall/CRC
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5.3 Taylor series approximations
/ o/ Y# I! @+ E; N+ v+ u5.4 Sampling distribution for maximum likelihood estimators
) x) Z3 T# ^' e! A  n5.5 Log-likelihood ratio statistic2 V% {. @" e$ ?; V# @
5.6 Sampling distribution for the deviance/ T. m2 Z) J4 l! K
5.7 Hypothesis testing
) M* M  [4 Z) N0 S- N0 j9 _5.8 Exercises# _- o  h, ~, q0 L# O
6 Normal Linear Models
" Y  V: r: L" n( N6.1 Introduction1 |8 n* G4 T& T/ i: d8 a
6.2 Basic results
: Y& o. T+ Q* `" @  H# G6 {6.3 Multiple linear regression
, w& e: r) `6 X6.4 Analysis of variance7 y9 i6 A9 N- b" R/ Z. W" M9 u
6.5 Analysis ofc ovariance2 W1 g) o3 O  d' A
6.6 General linear models: a& s/ N. j7 o. m/ ?
6.7 Exercises
4 R) r6 V+ ]( i7 Binary Variables and Logistic Regression/ Q9 q1 s! W. G
7.1 Probability distributions
& l2 m% q5 C4 S2 X; c7.2 Generalized linear models
4 d( Z! I6 P7 K% G3 s7.3 Dose response models
  f' g# i4 n/ v7 a+ F# B7.4 General logistic regression model
$ B- u, l/ N) i' V) [+ z7 i& {7.5 Goodness offi t statistics
1 u; w# }9 z  E; \/ R. C7.6 Residuals
2 I# k/ I, O" w& l7.7 Other diagnostics% m( `0 P- h4 R
7.8 Example: Senility and WAIS
$ U7 m. t; \! m3 H% J$ @7.9 Exercises, C. |' d" g$ {: M
8 Nominal and Ordinal Logistic Regression
) r1 G: O- R' H2 z8.1 Introduction
9 s9 Y4 z8 _/ v8.2 Multinomial distribution" x! n4 N8 y7 `6 d- @
8.3 Nominal logistic regression+ C5 i$ h; d( v8 |
8.4 Ordinal logistic regression
/ p2 k2 l; |( ?8 Y+ R8.5 General comments- l% Y! P* \  J% h
8.6 Exercises  R: m1 P2 Z, u3 Q! O6 I. O
9 Count Data, Poisson Regression and Log-Linear Models
: Y0 f, h# Z; ?( d9 h9.1 Introduction. _( h+ s6 n" [2 o1 I
9.2 Poisson regression
- E! M( O) [/ I# T$ A9.3 Examples ofco ntingency tables  d$ S4 z. }- f' Z: s2 q. _
9.4 Probability models for contingency tables
* g4 E6 w( E" Y- {9 Y9.5 Log-linear models
3 h% h" A! L, l1 F9.6 Inference for log-linear models1 c+ P% e, j, v9 M
9.7 Numerical examples, m, y1 d: v& J7 Y, A
9.8 Remarks
' q( R' W* y5 E$ U$ j9 I9.9 Exercises1 I- r! _" R" Q, X2 f$ N, K
? 2002 by Chapman & Hall/CRC
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: M  Z- n% [0 ]. X% \6 C, o10 Survival Analysis
# K. W) c6 C) n( ?& f( P10.1 Introduction
5 H. ?3 c. M  L, u/ @# _1 o10.2 Survivor functions and hazard functions4 ?+ M$ i5 u) H! k" O! x
10.3 Empirical survivor function
) L" }" h3 `# S; l10.4 Estimation
0 [! A2 J4 k0 h; U2 j* Z* S10.5 Inference
+ A/ @4 @, e0 O9 H+ c10.6 Model checking5 P7 y$ _1 W. H6 y. K/ u
10.7 Example: remission times9 @5 o. b! p5 r9 R( Q& k
10.8 Exercises. h9 S" [* X8 @( o! ]) a4 ~$ s
11 Clustered and Longitudinal Data
- Z9 M6 \) a2 V& N4 \11.1 Introduction( h4 E+ s  [7 i/ Q
11.2 Example: Recovery from stroke! w) @+ Y: G* S* k0 C) Z- `6 Q) `
11.3 Repeated measures models for Normal data" f  l3 x! I1 s9 j
11.4 Repeated measures models for non-Normal data4 D3 u% b) w. [( S- B) {
11.5 Multilevel models
* t* j/ M7 m4 K11.6 Stroke example continued0 \5 p# C( J/ H4 ^+ b: [
11.7 Comments
! k1 C- O( S. n1 l11.8 Exercises
4 W6 x. L' Z2 e7 v! WSoftware: q4 o( ~9 m: s) r; n0 s2 _9 N
References
9 e& j  d/ Q. Q3 H? 2002 by Chapman & Hall/CRC5 g6 d, w% U& k1 Q+ W
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Preface
7 {2 C- _1 O# }! a1 T& MStatistical tools for analyzing data are developing rapidly so that the 1990# U0 U0 x9 ]! S  l0 ?. y. ^9 K
edition ofthis book is now out ofdate.. j6 R6 \' q& D6 K
The original purpose ofthe book was to present a unified theoretical and
$ ?( j" _( G# y! I" ?2 C8 F3 D0 Econceptual framework for statistical modelling in a way that was accessible
  q* N' ~% ~0 a1 s- C) O# R  Oto undergraduate students and researchers in other fields. This new edition/ k; u: \" \2 n  q/ h) }  W
has been expanded to include nominal (or multinomial) and ordinal logistic
: Z, s5 H7 Y% P7 e. Vregression, survival analysis and analysis oflongitudinal and clustered data.2 A6 U7 s' x  l0 N
Although these topics do not fall strictly within the definition of generalized
6 E! G! M8 ~' U( Wlinear models, the underlying principles and methods are very similar and
* e9 G; K  r5 h/ K# D! ^/ }their inclusion is consistent with the original purpose ofthe book.5 Y6 G8 R1 m5 c/ i
The new edition relies on numerical methods more than the previous edition* j" ~5 G  m5 L' S; e- K" s  v6 `0 i: L* f
did. Some ofthe calculations can be performed with a spreadsheet while others
9 F+ a( d' V* n8 p0 `2 s6 S/ Wrequire statistical software. There is an emphasis on graphical methods for
$ o0 t! ~! V6 w0 m) Q3 O9 Yexploratory data analysis, visualizing numerical optimization (for example,+ q; w1 P# |! \5 ~! i9 k8 ^
ofthe likelihood function) and plotting residuals to check the adequacy of
; J! |+ T7 S; d; G/ ^models.
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Introduction# w! G9 ?$ t% A( |( l
1.1 Background
7 Z; w" C8 N, ^" |This book is designed to introduce the reader to generalized linear models;
5 |; X! {( L, B# z" g1 e/ z5 q% {these provide a unifying framework for many commonly used statistical techniques.+ X3 q' y0 X" @/ j3 L
They also illustrate the ideas ofstatistical modelling.
: f2 A8 u' v+ qThe reader is assumed to have some familiarity with statistical principles
! [4 q8 J* k8 ?! `8 Y$ _and methods. In particular, understanding the concepts ofestimation, sampling/ |$ m( S2 V) {4 Q+ N9 z, G. S
distributions and hypothesis testing is necessary. Experience in the use9 A5 r3 E8 [4 E+ E/ t& i# i
oft-tests, analysis ofv ariance, simple linear regression and chi-squared tests of
$ p+ r4 N6 D' f  v" Y  h* V( ^independence for two-dimensional contingency tables is assumed. In addition,- j3 Z: k2 ~, e. Q
some knowledge ofmatrix algebra and calculus is required.. ~# ?+ ]3 b7 A1 E( H
The reader will find it necessary to have access to statistical computing* Q+ j% X1 o9 m
facilities. Many statistical programs, languages or packages can now perform: V# ?/ A1 f  W: n0 [. D
the analyses discussed in this book. Often, however, they do so with a different1 r+ P. x0 J; p% v) e/ E& J  L9 B
program or procedure for each type of analysis so that the unifying structure2 t" o) D/ y5 i+ n$ e* S5 U) M2 Q5 n
is not apparent.
6 J* b2 z8 }7 M9 T4 U9 mSome programs or languages which have procedures consistent with the
, T4 H  e- p3 Xapproach used in this book are: Stata, S-PLUS, Glim, Genstat and SYSTAT.' @' i' k, c! s  {5 V+ V1 b- h
This list is not comprehensive as appropriate modules are continually  e8 ]  {0 U2 T0 u
being added to other programs.: c! T! t5 T( {8 E9 |
In addition, anyone working through this book may find it helpful to be able
, R3 e5 W- `7 dto use mathematical software that can perform matrix algebra, differentiation
$ [/ Z# X, b+ F2 C5 vand iterative calculations.
% H% a! a+ f+ h6 ^- A1.2 Scope
$ I9 y. ^9 }( nThe statistical methods considered in this
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