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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) [4 q# h/ C; E: w. I! B, e
Preface" Z: s  |9 W7 E
1 Introduction) H9 A/ {. A; A2 j9 r% q
1.1 Background
, e: t- H* S% e- R1.2 Scope
& S( N& a: A* o# O. `6 |. d) l# d1.3 Notation
: X' B+ O$ n4 A. U" o) e! J1.4 Distributions related to the Normal distribution. e& ?, I" t7 Q  d# p
1.5 Quadratic forms
: N( D) j# E6 Q" @+ c1.6 Estimation
( n! ^# d3 p% J' f1.7 Exercises0 o- T  A# ]) b% F  i
2 Model Fitting7 q% e3 z6 z1 `0 b" q
2.1 Introduction
7 }& J% s9 a4 w/ K2.2 Examples: T7 P, i$ _) D& b
2.3 Some principles ofstatistica l modelling
  X$ X3 D" s, [) G' r9 j# v* s2.4 Notation and coding for explanatory variables( O+ ^# {3 e5 h1 U3 I7 G- _
2.5 Exercises: _9 W' P0 i. p9 g
3 Exponential Family and Generalized Linear Models# m  Z, g5 U( a
3.1 Introduction
, w1 U: t9 Q+ ?) `+ N$ G! l3.2 Exponential family of distributions
$ V# [8 t$ A0 L# I; V+ t3.3 Properties ofdistribution s in the exponential family: H2 l: e- a2 i" v8 A6 N
3.4 Generalized linear models
3 Z) L" S2 x; f8 d: g  }3.5 Examples8 P: w6 |3 P* b
3.6 Exercises0 T7 Z& ^3 {9 E& ~. o/ S
4 Estimation
0 R. l' [) E# E% t& `  T4.1 Introduction# b7 e# W. m4 l9 N# {* l- A! e# c
4.2 Example: Failure times for pressure vessels
" n$ C, k; ^8 X$ A7 c& c! U. i4.3 Maximum likelihood estimation# N! t: f- x( d# T
4.4 Poisson regression example2 Q  u6 s$ @% `1 e+ L
4.5 Exercises' M2 Y) v2 E0 ]# h/ G$ o# O* T
5 Inference
; a2 M0 w+ `" u; {" I+ `$ ~& v5.1 Introduction. Y7 R) S4 `# B, W' _$ e$ t4 ~$ r
5.2 Sampling distribution for score statistics
+ h1 z7 b3 c1 M0 n1 v. |+ W9 B? 2002 by Chapman & Hall/CRC
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5 N1 z; O5 c1 x# ]( [5.3 Taylor series approximations& D2 s8 S. m0 h6 L; y4 q: s: v
5.4 Sampling distribution for maximum likelihood estimators
' r2 k; c) u" b! p, `, U8 t5.5 Log-likelihood ratio statistic6 A; [/ e9 {$ W  ]* J0 V/ R6 P
5.6 Sampling distribution for the deviance
0 p2 S4 X. V7 G; ~5 V8 j$ z: t  n1 `5.7 Hypothesis testing
, [2 p( j% s( R/ P) h, p, ]0 j5.8 Exercises# G4 H% {# ^- Z, C/ X; N" E9 J
6 Normal Linear Models
. Z) N+ J; M% X: f- [6.1 Introduction8 p. g! u5 g3 q$ k
6.2 Basic results
/ f0 Q2 g: t+ m( k  w& |1 T6.3 Multiple linear regression0 ?  C, z7 Q( s' G( o: I5 U
6.4 Analysis of variance& T& A; g1 E1 ^" m8 x1 H
6.5 Analysis ofc ovariance
# e* I) b2 _8 R9 y6.6 General linear models; m, z* A$ Q+ I) p
6.7 Exercises
8 O* O5 \! Z" I) L5 [2 A7 Binary Variables and Logistic Regression
9 L( A6 X+ V! O7.1 Probability distributions" U. R+ m& s# M+ W5 I  I% |4 Q# _
7.2 Generalized linear models; [* L# J/ ^3 f
7.3 Dose response models
$ o: U; B1 u  R* o* T# L5 E* O( H" P7.4 General logistic regression model
3 i4 D  Z. U, A) T6 |7.5 Goodness offi t statistics
1 w; ]- H1 Y9 x8 p7.6 Residuals8 P6 G2 I1 b, f
7.7 Other diagnostics" L4 b9 Q9 w0 r/ u( [) s3 C
7.8 Example: Senility and WAIS
6 L3 F6 A& E- s5 a8 F- X& p7.9 Exercises7 Y+ g0 Z8 [, I5 O
8 Nominal and Ordinal Logistic Regression
/ M% }6 y. k' d/ G0 A; ~! ?$ ?8.1 Introduction
- f2 I4 @& E9 y4 c% [, d- c: B8.2 Multinomial distribution( x6 O* s% G, E6 s! p) y$ x- a2 Z
8.3 Nominal logistic regression$ Y6 Z. W# J% q3 b" F; I! S6 @
8.4 Ordinal logistic regression
2 o$ f* m- F% @8.5 General comments9 R* Y2 H( M' N7 Y: {  h  l
8.6 Exercises
# M. p/ v7 u/ b9 E" E, S) _9 Count Data, Poisson Regression and Log-Linear Models
: a: X& E; L8 ~' @6 g2 Z9.1 Introduction
/ q1 z8 U5 p- ~$ {% f5 _( w7 P9.2 Poisson regression, O+ j7 k5 m. u# p/ q# c
9.3 Examples ofco ntingency tables
$ J  d% M5 r& j6 j2 N! b2 x9.4 Probability models for contingency tables- }2 a6 q& ]/ ]+ T% w
9.5 Log-linear models
- o- E2 v  w4 N7 Y2 G# S& N9 U9.6 Inference for log-linear models& k# M  N! l: ]3 s2 e
9.7 Numerical examples
& z+ J) H8 f1 Q4 s& X' s9.8 Remarks
# _: F) |3 l3 M% q9.9 Exercises; O, r  O4 b9 L
? 2002 by Chapman & Hall/CRC
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$ L3 D9 d5 R" n" a9 j, q10 Survival Analysis4 I% u( Z% O8 |. H. e) }
10.1 Introduction' N' G1 z, a; ]/ l6 u3 q
10.2 Survivor functions and hazard functions
# X1 V/ m- U* \' o6 G10.3 Empirical survivor function
+ L0 K3 \& o% e  v$ u" E9 M# `10.4 Estimation) D' r/ O" Y/ v) b/ u9 |# r' R8 d6 O
10.5 Inference
2 W: R, F9 R8 Q7 `. I- r10.6 Model checking
9 K9 ~/ M- O* _10.7 Example: remission times+ W' [7 w0 |7 J# j
10.8 Exercises/ W2 n& G4 w; a4 t' N
11 Clustered and Longitudinal Data1 ?5 f- \# A2 _: S
11.1 Introduction$ ?2 q; c& q6 N' C( m
11.2 Example: Recovery from stroke. K/ _; u  W, o* ^
11.3 Repeated measures models for Normal data' m. j) |- q3 ~. D, K" i
11.4 Repeated measures models for non-Normal data
+ N* k6 t7 I; A9 _% K11.5 Multilevel models! r3 B: y" ?7 h) H$ n& I4 X
11.6 Stroke example continued
8 [% ]6 F7 q5 w* w11.7 Comments( W! A0 f0 }& O7 t9 r+ f8 p+ V, C
11.8 Exercises. H6 a5 \/ y- g6 R9 c2 C5 O4 n! t
Software3 B! P- z/ C5 e1 A
References: o6 T1 Z: u. ~/ f
? 2002 by Chapman & Hall/CRC6 W$ G! {' h+ X
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Preface9 J# _. U5 a! t9 L' ?
Statistical tools for analyzing data are developing rapidly so that the 1990
/ ]' D& Z% [9 V( l- p0 Pedition ofthis book is now out ofdate.
  B; `7 S) H$ R$ VThe original purpose ofthe book was to present a unified theoretical and
: a- m3 S+ h8 `0 h% Xconceptual framework for statistical modelling in a way that was accessible& O% K6 a5 F( F
to undergraduate students and researchers in other fields. This new edition) U' Z$ k- A" P& ~
has been expanded to include nominal (or multinomial) and ordinal logistic
; y2 P( H& a+ Y  Pregression, survival analysis and analysis oflongitudinal and clustered data.
" ~4 h! ]# h9 e& ]' |. x7 g- GAlthough these topics do not fall strictly within the definition of generalized% b+ e  B1 V  f2 U/ l
linear models, the underlying principles and methods are very similar and
( u1 C3 h; M7 S6 g% ~: ltheir inclusion is consistent with the original purpose ofthe book.0 S6 K2 ]  A* u* N4 S9 K
The new edition relies on numerical methods more than the previous edition5 k# }; H0 j& l& F  S' ^
did. Some ofthe calculations can be performed with a spreadsheet while others
( e1 a( {: D: h0 Yrequire statistical software. There is an emphasis on graphical methods for
; ]/ p0 v/ d- M' gexploratory data analysis, visualizing numerical optimization (for example,$ o8 I8 Q, R3 a/ M
ofthe likelihood function) and plotting residuals to check the adequacy of
, y9 Y7 I9 a  c& f$ Ymodels.
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$ l: s: [, {! t5 RIntroduction5 v  C! y4 V, s" K' s3 ?6 R8 N
1.1 Background
2 U* W5 b8 h9 f. q& g' E$ sThis book is designed to introduce the reader to generalized linear models;
; ?4 C* K2 n& |+ J& _+ Z* Nthese provide a unifying framework for many commonly used statistical techniques.
- }4 ?( w0 n3 m. \They also illustrate the ideas ofstatistical modelling.
$ }: \0 P1 ^1 Z0 b* A/ @) s- wThe reader is assumed to have some familiarity with statistical principles
/ M3 {! R  m5 K% f1 |% Land methods. In particular, understanding the concepts ofestimation, sampling
% H) Y$ f# @& W3 C8 Y) s  p4 s$ B% hdistributions and hypothesis testing is necessary. Experience in the use
. y# M/ ]4 [1 s4 r; u6 J$ \) H) Uoft-tests, analysis ofv ariance, simple linear regression and chi-squared tests of# w8 m8 k/ }& X( x9 [/ X
independence for two-dimensional contingency tables is assumed. In addition,
% f( r# ]/ v3 t4 W0 psome knowledge ofmatrix algebra and calculus is required.1 s9 N. [" Z& c' j
The reader will find it necessary to have access to statistical computing- T! h0 m# |7 V- x
facilities. Many statistical programs, languages or packages can now perform# ^8 q; M  L- _+ p; n; f' e. _
the analyses discussed in this book. Often, however, they do so with a different
0 u  H, \0 d6 q) {9 g$ \6 X( Lprogram or procedure for each type of analysis so that the unifying structure
4 \: {" |9 A+ gis not apparent.
% H: h- e4 n+ Q+ C  ESome programs or languages which have procedures consistent with the
0 v3 e+ @. E, f3 M# H$ q$ oapproach used in this book are: Stata, S-PLUS, Glim, Genstat and SYSTAT.4 q& w9 A# p6 B8 ~3 }2 L- q
This list is not comprehensive as appropriate modules are continually
- T8 z" e3 b# o, O- qbeing added to other programs.1 R+ t5 R4 m* V  J( A
In addition, anyone working through this book may find it helpful to be able  x+ @# M$ ?* E- ~
to use mathematical software that can perform matrix algebra, differentiation
6 J2 t8 j0 \0 Y! |and iterative calculations.
" I2 e0 I) ~1 J2 t1 B1.2 Scope+ B( ^. z3 t  q* U
The statistical methods considered in this
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