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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& s' H5 T: B2 B: S& C) ?. P+ P0 Y
Preface
3 ], B# L  l" l: q" N! w* V1 Introduction9 G% \+ a4 [- U" F0 X8 s6 r1 n
1.1 Background. z- X! O& a% q6 x* `, @
1.2 Scope
7 ]  q: K% h! @& \% W1.3 Notation
! k% V9 @' d) }5 I! ], e2 ]! ?) i( T1.4 Distributions related to the Normal distribution" ^% i! I" R2 f" d) v
1.5 Quadratic forms
& y2 M2 o; o4 T; _9 X1.6 Estimation9 X" u8 N, r; E# r' ?. Z0 S
1.7 Exercises
, ~7 r2 H' k- J$ Z. I9 k2 l) J2 Model Fitting
$ o! D6 R4 D2 d4 o' z$ d2.1 Introduction
* n3 o. \0 j2 x# J2.2 Examples: G" u/ h# |+ d0 t/ [
2.3 Some principles ofstatistica l modelling
6 }7 P0 t( B- F/ K! E1 v9 n2.4 Notation and coding for explanatory variables3 b+ W7 K  A+ ^2 N- _4 b
2.5 Exercises
6 d- i3 Y6 a: z2 p; C1 z* _3 Exponential Family and Generalized Linear Models
3 a1 O3 @7 ~9 X" r3.1 Introduction. \! M7 p# f! @) ]( D% u
3.2 Exponential family of distributions- F; k* R0 V5 q1 S1 C
3.3 Properties ofdistribution s in the exponential family
6 j& C' _1 H8 }, G3.4 Generalized linear models
$ J: i; w" f3 i+ W7 P3.5 Examples/ v8 v. ?. g; m
3.6 Exercises1 ^- d# ?$ h% z' @4 C
4 Estimation
+ Z1 w) r8 {% t. K& q4 z- z4.1 Introduction5 K/ Q0 E4 Q2 _9 t: d1 E, U& O
4.2 Example: Failure times for pressure vessels( B, m/ j& f5 A( w: s
4.3 Maximum likelihood estimation& E+ B. Y% i/ i8 X3 b
4.4 Poisson regression example
7 R7 B. \, T. y; r' B. I3 j* t" X4.5 Exercises$ `" I) a2 H; r
5 Inference
3 Y* u) i! n" j5.1 Introduction
9 t( }1 T: T* Q/ k6 _( a: `( i5.2 Sampling distribution for score statistics" ~+ |2 s: B. u& C' c
? 2002 by Chapman & Hall/CRC2 H( G/ w; ?5 O, T+ h7 r. ?! o
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5.3 Taylor series approximations( ?' V- r/ j: _) w8 W- Z) \: ]
5.4 Sampling distribution for maximum likelihood estimators5 K% S5 o; v8 O+ m" S: S
5.5 Log-likelihood ratio statistic
! x$ O4 x5 q! w; m5.6 Sampling distribution for the deviance0 l# t- s& O3 ~1 I3 i
5.7 Hypothesis testing
3 `6 P9 h% v6 `$ _8 y5.8 Exercises  g; Q4 l0 m7 y8 V. }* i
6 Normal Linear Models' |/ h* K2 \6 q
6.1 Introduction
4 _" ~# ?$ q. d: d- U6 P6.2 Basic results
- v& I- Z+ ^9 @2 I, B6.3 Multiple linear regression6 @, y& I9 q2 k% \. E
6.4 Analysis of variance
/ e. f2 f2 j; S8 t" Y: Z; x6.5 Analysis ofc ovariance
9 V; j. e9 L8 a% ~8 c( R6.6 General linear models
; V, k$ b0 m$ I6 |3 v. Q9 ]6.7 Exercises' I9 a& F5 D7 C
7 Binary Variables and Logistic Regression
: l1 h' j; |; P0 K3 v0 I1 K7.1 Probability distributions
+ F4 n) I, `  z: Q7.2 Generalized linear models
+ P1 T- K4 I& L7.3 Dose response models
1 M; P. o- `& o4 h; ]7.4 General logistic regression model
6 u& v+ V* M. @& m+ G: c/ q7.5 Goodness offi t statistics
6 i. c* c5 r6 ]6 N0 x1 O7.6 Residuals9 L0 X4 f3 s# @- Y5 d3 }! H
7.7 Other diagnostics
7 D8 M0 f5 U  e+ b8 m, t7.8 Example: Senility and WAIS; Y! x2 G8 O; ~7 L, k8 r6 j+ b2 o6 t
7.9 Exercises# k! }, `. w( S" Z! |; L+ X
8 Nominal and Ordinal Logistic Regression; f7 m+ @0 @% n) Z* c3 L% \& R6 J0 [
8.1 Introduction
, d- k- _- D! O/ i* J5 \8.2 Multinomial distribution
7 }4 o9 d4 h9 t6 T, J6 N8.3 Nominal logistic regression
7 U( ]' T7 A, g. R8.4 Ordinal logistic regression  f4 x* H1 B/ i/ w1 E
8.5 General comments5 X! D% c- T0 Z# i$ v, l: P9 t" R
8.6 Exercises3 S' Y6 Z5 Q' {5 ^6 P! t7 F2 x
9 Count Data, Poisson Regression and Log-Linear Models, D. I! s' ]5 G" h  m. w6 [
9.1 Introduction
4 n3 V( {  B) M8 B9.2 Poisson regression
: w3 m# n/ z( Z, o( F9.3 Examples ofco ntingency tables4 X$ T7 Y8 ?6 R1 B$ G7 x* }& g
9.4 Probability models for contingency tables0 M4 q. m8 b* ?$ l8 ^0 K
9.5 Log-linear models3 F/ i% B+ Z8 G+ x
9.6 Inference for log-linear models" m+ G% t& x% i% Z
9.7 Numerical examples
9 I/ k$ f$ g: _3 X( o2 R% e9.8 Remarks( H* U  u. f6 K4 b0 A, [. U* Q
9.9 Exercises+ |+ Z- C4 Z- c( F! I6 f& N
? 2002 by Chapman & Hall/CRC, Z0 I1 }7 q0 U7 C0 w$ p
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+ W% o3 k& o  s! U& |10 Survival Analysis
1 t8 G2 l2 o- E: y10.1 Introduction  a4 r2 d8 {3 `& r/ m5 ~- ]
10.2 Survivor functions and hazard functions  `4 U1 P, {- u8 Q
10.3 Empirical survivor function3 ~1 p6 c1 ?, q+ E+ E: c4 @* T5 a4 N
10.4 Estimation4 H0 t8 C$ |; ]- H9 m" V
10.5 Inference# V) j# N) l" V2 T$ c! W4 J
10.6 Model checking0 e: y% T+ d- S( l/ m
10.7 Example: remission times) ~+ a! P9 j, N& _
10.8 Exercises
" P3 K  R+ `  J11 Clustered and Longitudinal Data
- i/ C4 ]2 L5 I: m0 D# G; f11.1 Introduction
: z; W, u) c. _5 M) |- E, I. U' k11.2 Example: Recovery from stroke
$ R  d3 [  R" C% ~11.3 Repeated measures models for Normal data
) U% Q; a2 G9 ~% }1 U3 r' O11.4 Repeated measures models for non-Normal data
) g- n) c* V- p0 I11.5 Multilevel models- k0 {6 q$ k: _' h
11.6 Stroke example continued5 P) H2 e' ^# l6 l, T8 p6 G2 l' ?% h
11.7 Comments
5 q4 N1 e# G9 c# ^4 M( m11.8 Exercises
6 \+ V+ N- p) t7 z3 H, U" P; S; ySoftware
& y7 [- L( Y6 A: m/ t. e: TReferences
" ~2 e9 I5 F6 X3 T* J0 `5 t- v( f% X? 2002 by Chapman & Hall/CRC9 L: a0 g1 \- O! e9 C
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- F) s& o+ ~  ~% Y, Y1 hPreface
2 i* N# N! |1 g! ~7 v1 o( pStatistical tools for analyzing data are developing rapidly so that the 1990
. I/ E# G0 R* \$ jedition ofthis book is now out ofdate.
  F+ e: L; F' |- L. e% r# d2 r$ }The original purpose ofthe book was to present a unified theoretical and. ^0 u8 i1 ?9 D3 S5 a
conceptual framework for statistical modelling in a way that was accessible0 ~, T9 l2 }, B
to undergraduate students and researchers in other fields. This new edition7 p5 |! ]; w& Q. `7 p" p3 R
has been expanded to include nominal (or multinomial) and ordinal logistic% q+ F/ G. Y9 T; ?
regression, survival analysis and analysis oflongitudinal and clustered data.
$ d! J1 R, n: Q! p( H! c- xAlthough these topics do not fall strictly within the definition of generalized
9 a7 ~% w- ?( o0 Y: z/ B" zlinear models, the underlying principles and methods are very similar and5 F8 s- K- \' h8 s$ O/ L8 w
their inclusion is consistent with the original purpose ofthe book.
# Z; G; b: }& L. S  q& q7 C% G& @- gThe new edition relies on numerical methods more than the previous edition- h0 O- T- m" k: G$ R6 _
did. Some ofthe calculations can be performed with a spreadsheet while others# s7 I1 T' `: g" i: \+ c
require statistical software. There is an emphasis on graphical methods for
/ {2 L# {9 h7 n3 Z8 n; N# p4 r  @exploratory data analysis, visualizing numerical optimization (for example,% w* n( F  @$ |6 W/ a- _
ofthe likelihood function) and plotting residuals to check the adequacy of* K" {( f/ A/ w" O9 J% @3 o# j* l
models.
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0 D7 D2 c# b% N2 YIntroduction
1 j6 X8 Y. q4 g% a1 z! h! c1.1 Background* z- E- s3 B: _4 f, {6 v# h
This book is designed to introduce the reader to generalized linear models;% k7 a! l- X" I1 n8 o/ w& t8 a
these provide a unifying framework for many commonly used statistical techniques.
" c* E, G* H# {7 xThey also illustrate the ideas ofstatistical modelling., O' v: `. m% Q+ U6 Q+ T
The reader is assumed to have some familiarity with statistical principles/ d2 Z( E, t  K
and methods. In particular, understanding the concepts ofestimation, sampling
5 b( P& y5 N+ Hdistributions and hypothesis testing is necessary. Experience in the use
5 u4 U! L. e4 ]oft-tests, analysis ofv ariance, simple linear regression and chi-squared tests of
" e3 z0 t2 \  O! Oindependence for two-dimensional contingency tables is assumed. In addition,
3 N( w: n& _* d  w' U& r- dsome knowledge ofmatrix algebra and calculus is required." u' `5 [+ m4 A* t+ x
The reader will find it necessary to have access to statistical computing) M; L5 R' x4 Y) T& z! `! c% i
facilities. Many statistical programs, languages or packages can now perform; z* |7 ?8 C: _; F7 Q/ h, X' z7 s
the analyses discussed in this book. Often, however, they do so with a different' z0 s6 L; a' I7 ]3 f; B
program or procedure for each type of analysis so that the unifying structure
! k8 Z4 a4 Z5 G& x* T' uis not apparent.
% x1 }5 t! a9 e  zSome programs or languages which have procedures consistent with the3 u& U5 O1 k, P7 P3 N
approach used in this book are: Stata, S-PLUS, Glim, Genstat and SYSTAT.
. Z( V* t+ M" P, [/ @This list is not comprehensive as appropriate modules are continually. ^% D, [$ P$ Z- y, V& B8 W+ j, e
being added to other programs.
$ I+ j) J' d- L  [  N6 o. l- @In addition, anyone working through this book may find it helpful to be able
" p0 C( O; l; f9 A5 v5 d( v# V7 Jto use mathematical software that can perform matrix algebra, differentiation, G" o6 t- j; {- R% \7 d  t
and iterative calculations.9 l1 _2 V$ R2 S3 j6 A* F( j3 \
1.2 Scope4 Z5 I( w& h% D) d5 N4 b: K
The statistical methods considered in this
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