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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/ w% V' ~9 f# o7 wPreface' |( l( `3 v# c% c- H5 g
1 Introduction! W) L! R# Y! C0 h) P2 [  h
1.1 Background
/ w4 ]4 I& _" q1.2 Scope
# Z. t3 P# U4 Q$ E& V8 l1.3 Notation
: M; T' S, r4 s# G9 B- n1.4 Distributions related to the Normal distribution% w/ h/ n6 H5 t  w4 s4 i* t/ \9 z
1.5 Quadratic forms
. Y  C, K' p6 y. X, U1.6 Estimation1 d! h2 ~- k1 r
1.7 Exercises% p; O* S+ r+ ]2 V/ q8 [
2 Model Fitting7 ~! o- B! {, V, m9 z
2.1 Introduction: n! B, c' e/ a0 x& N, r2 W
2.2 Examples
8 |. c+ H" b  h- D1 N2.3 Some principles ofstatistica l modelling1 ^5 E$ \. j# n$ b1 v
2.4 Notation and coding for explanatory variables4 X6 {9 H) ?. `
2.5 Exercises. |* L: K& @# ~! \: Q' ?3 ~
3 Exponential Family and Generalized Linear Models
) g2 _7 h# @* c/ T" g3.1 Introduction3 b- Q3 J* Q: F1 }7 W* r; j
3.2 Exponential family of distributions" q0 }9 l1 M+ a
3.3 Properties ofdistribution s in the exponential family
) o$ y+ M) M1 ^4 i" p+ r3.4 Generalized linear models1 Z$ b9 f3 d2 Z4 f& R4 T4 d
3.5 Examples$ x' I% D3 o1 z) Z
3.6 Exercises, l& l  }" ]$ F2 V
4 Estimation# H. r# F7 a# I# s
4.1 Introduction3 G8 x( v2 q0 Q: @, R
4.2 Example: Failure times for pressure vessels4 u$ r; U; m7 Z; P; ^! L& Y1 b4 f
4.3 Maximum likelihood estimation
7 ^; a: c4 u) D4.4 Poisson regression example
4 O  Z1 f6 h& B/ U( f. ^( Y4.5 Exercises4 Q1 H& O# |) P
5 Inference! M$ }- M) x1 u5 R, W! B9 Q
5.1 Introduction
7 F2 C7 @8 @* @4 n5.2 Sampling distribution for score statistics5 M/ T8 n7 P+ m
? 2002 by Chapman & Hall/CRC8 ~. d- n: j, x( s( c2 O; B' }; h
59 D4 \6 u+ O0 m3 b
5.3 Taylor series approximations4 O' o3 `1 G2 C% B; z  U7 K( T& A
5.4 Sampling distribution for maximum likelihood estimators5 u+ g3 k: g  i  p1 g( q$ U
5.5 Log-likelihood ratio statistic
3 q, o/ P- c/ i/ P4 {8 V' H% k5.6 Sampling distribution for the deviance
1 c! V2 T; ~1 Z1 W, ^5.7 Hypothesis testing  t( s2 W3 O/ U: i4 z
5.8 Exercises
3 Y2 s; a7 U! \! y- @2 {6 Normal Linear Models- Q5 W: `9 J3 O
6.1 Introduction
. e3 A& g9 {4 J0 _1 ]6.2 Basic results
# c1 a) f$ g- A+ n6 j6.3 Multiple linear regression
7 D* e- B" f5 H6.4 Analysis of variance
( v) N& l! s  U2 v9 n9 O0 E6.5 Analysis ofc ovariance1 C3 ?; ?/ Q9 Z0 R
6.6 General linear models- Q& k" {, G' \5 q. ]
6.7 Exercises
, l7 u/ D/ f4 @, `7 Binary Variables and Logistic Regression; R2 A* I0 x# d" l
7.1 Probability distributions: o! E' W0 K* [! f0 g
7.2 Generalized linear models: T/ K: T) c# [$ [& i
7.3 Dose response models' h- V5 U; f0 ^6 ~4 T% w8 g) F9 L
7.4 General logistic regression model  X- o* O. K3 `2 |8 U1 ^# l- J8 i$ _/ @# t
7.5 Goodness offi t statistics1 v" y) V# X  U% {6 ^2 E8 I, d
7.6 Residuals
: |7 l6 b6 G0 l- r7.7 Other diagnostics
# i4 J7 Q" {7 H) i* \1 s8 r7.8 Example: Senility and WAIS* L; K0 Q( R1 ^% _
7.9 Exercises
- g! O! h5 N# t  R" }8 Nominal and Ordinal Logistic Regression( R2 i: [# h' t
8.1 Introduction, V4 d  K8 R* q! B9 T
8.2 Multinomial distribution
3 q, m) N9 `( R. N4 Q# X+ Y8.3 Nominal logistic regression) r. U7 g* `0 u3 @3 B: W' w
8.4 Ordinal logistic regression8 q% X* j  k- Q5 p) }0 p% e4 Z, j4 m, u0 X
8.5 General comments
* v+ A+ I  s/ H0 C& {5 Y8.6 Exercises
: G: ?) ~6 p* L" M9 Count Data, Poisson Regression and Log-Linear Models
; v; x% R. _/ V" V% g9.1 Introduction& M8 Y. W9 `' P! v7 T2 u8 z& o1 k
9.2 Poisson regression
' `) f- B7 t1 y( ?9 Z9.3 Examples ofco ntingency tables  }0 t1 O3 F# x* S7 r; |
9.4 Probability models for contingency tables
2 [$ Q6 A) c; _9.5 Log-linear models
6 W; A' p# ], n& o6 H+ ?9.6 Inference for log-linear models
& B5 R' X. z- l$ K/ _9.7 Numerical examples
- N% N7 ?2 j1 i0 F2 `- N9.8 Remarks
, R: @7 C* T+ b; L" ^% b3 q9.9 Exercises. l: n* G% G. q/ X, a! T* W( R" u
? 2002 by Chapman & Hall/CRC
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10 Survival Analysis
" P# g+ }# r4 [10.1 Introduction( w. F, P( K6 O+ Q
10.2 Survivor functions and hazard functions) X. R+ }3 t* T# o
10.3 Empirical survivor function
' O' g% E  H3 u  a3 s10.4 Estimation
) e$ b. X( _" c, f  ]6 x# |10.5 Inference
1 r( Z3 W6 s' f# b10.6 Model checking' P7 {' }, ~$ H  @, b* Z
10.7 Example: remission times
* V, y! Z9 \/ ?, ]9 A10.8 Exercises2 c3 G; P" o/ s8 ?! _
11 Clustered and Longitudinal Data% b# s' V: T$ {9 i! A' T( l8 f
11.1 Introduction7 g: H. S3 M2 w8 [
11.2 Example: Recovery from stroke5 A: ^/ H/ E2 f" w% g( a7 T
11.3 Repeated measures models for Normal data# ?* K5 P- ^8 O7 ?% w$ L  p8 d" [
11.4 Repeated measures models for non-Normal data6 _% x# h7 `  ?& R1 [
11.5 Multilevel models( Z8 ^! u6 ]- K: n' F) i- W& E
11.6 Stroke example continued" V8 k5 I# b' y* Q( J
11.7 Comments. N+ ~# c* \$ x
11.8 Exercises. I$ o) E$ z$ V4 k2 [* f) Z
Software
. `; c; A2 c  k; RReferences
% X. N7 L) Y' ?$ y" d8 k: {6 k? 2002 by Chapman & Hall/CRC; ^. p" Z- _& s4 R8 m
7
. D  E8 |  P" B: V- X9 m7 |Preface
* d5 C+ m3 v( z8 a' ?; IStatistical tools for analyzing data are developing rapidly so that the 1990
' z! ~$ l2 \/ m/ T5 F3 A5 Nedition ofthis book is now out ofdate.
  l/ @+ x) G! M2 O' J2 N( mThe original purpose ofthe book was to present a unified theoretical and
" t. ]; s" j& X. B" M; qconceptual framework for statistical modelling in a way that was accessible
- x/ _$ g+ Y( f; Wto undergraduate students and researchers in other fields. This new edition7 A1 C+ s" ]: n( Z
has been expanded to include nominal (or multinomial) and ordinal logistic
& w! h# y0 h4 V1 gregression, survival analysis and analysis oflongitudinal and clustered data.: d& i. \, H& e
Although these topics do not fall strictly within the definition of generalized
6 L8 Q6 n6 W5 K+ Glinear models, the underlying principles and methods are very similar and8 u- @' M9 k9 L2 V9 \% Q+ H- q
their inclusion is consistent with the original purpose ofthe book.
4 J4 b; Z0 l9 w" UThe new edition relies on numerical methods more than the previous edition
" C; m9 {' }1 c! M, X5 ?3 K$ Ldid. Some ofthe calculations can be performed with a spreadsheet while others
# m; w. \5 R2 g1 N- o- _% x! Erequire statistical software. There is an emphasis on graphical methods for* r( x/ o( t$ h) G
exploratory data analysis, visualizing numerical optimization (for example,
9 F2 _8 e& J0 |5 `( ?ofthe likelihood function) and plotting residuals to check the adequacy of
# U' O  }' _: W. U- J) Kmodels., ]; G/ A' V9 S, a3 `
& ]$ o1 x6 }$ H5 C7 U
Introduction4 o# r. G9 ~& \+ y
1.1 Background
4 {) j6 e& y: Q+ W; C/ LThis book is designed to introduce the reader to generalized linear models;
( n4 T# T, _" K& |- Q/ wthese provide a unifying framework for many commonly used statistical techniques.
. ]9 d- h2 X' h) q( }, ]- ZThey also illustrate the ideas ofstatistical modelling.% R7 }, s/ d9 i. l* t6 E
The reader is assumed to have some familiarity with statistical principles
0 C8 H1 c6 O7 p" S1 c, p9 r) pand methods. In particular, understanding the concepts ofestimation, sampling3 a+ q. N$ n; Q7 ^
distributions and hypothesis testing is necessary. Experience in the use
. i; W" k' `9 w' \: eoft-tests, analysis ofv ariance, simple linear regression and chi-squared tests of
' }6 |6 G6 @6 R3 y6 Lindependence for two-dimensional contingency tables is assumed. In addition,! p4 }- Y& E% o, X
some knowledge ofmatrix algebra and calculus is required.
2 a6 k. k2 v  b3 c  p  G7 K" BThe reader will find it necessary to have access to statistical computing
$ Z" v: T& S) e4 R9 {& J" q9 |1 zfacilities. Many statistical programs, languages or packages can now perform
* e! P# J! R1 Z. @5 }/ t# @" a2 Sthe analyses discussed in this book. Often, however, they do so with a different
1 g/ |* N( \% R: a. yprogram or procedure for each type of analysis so that the unifying structure
$ |9 i* p9 q5 F1 ~/ ?6 M+ Cis not apparent.
# Q3 I* {3 i' Z8 qSome programs or languages which have procedures consistent with the8 p: g5 f6 }# P+ o6 g, c) Z
approach used in this book are: Stata, S-PLUS, Glim, Genstat and SYSTAT.
# k* k; ~* X) j2 _" p! G6 t$ y) @4 {This list is not comprehensive as appropriate modules are continually
8 a# J. T# v' K7 m, w) M; \4 Gbeing added to other programs.) o% ?! L1 k4 D" B
In addition, anyone working through this book may find it helpful to be able9 S8 F% x) Q8 I2 C! s
to use mathematical software that can perform matrix algebra, differentiation8 F( h# J& y! D+ F8 K5 g4 ]
and iterative calculations.1 s/ z  N/ C: V+ K, q7 J
1.2 Scope
% W  ]8 N3 X# L' GThe statistical methods considered in this6 t; X/ Z# T+ X" U4 k

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