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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
2 @3 I+ C6 p) L9 Q- i" |Preface  K. C2 F1 z* f3 j! k: K
1 Introduction8 ^/ f9 N4 U0 j+ t- [: T
1.1 Background7 z' {& K2 @  B4 b3 n
1.2 Scope
5 _) `! t, @' p3 E' h' K2 ^0 _1.3 Notation
! D( v) y8 L/ s- @$ e, I9 R1.4 Distributions related to the Normal distribution
# l! K2 Y: [6 ~( f- M; t1 W1.5 Quadratic forms. y  f$ Z) m# Q
1.6 Estimation" Z; a! t" X9 c1 w
1.7 Exercises$ F$ a; Y5 r3 y- C3 r
2 Model Fitting
5 N- S3 p: n0 A" H4 Z2.1 Introduction
. g& s# |2 I3 n/ u( B  D7 ~2.2 Examples9 P9 i: Q) G+ Q9 T) j- M' X$ O
2.3 Some principles ofstatistica l modelling0 G9 f! |# \% G
2.4 Notation and coding for explanatory variables
( e) w! ^" W- G) e+ k1 a2.5 Exercises- `) Z7 Y$ P9 s( y3 r
3 Exponential Family and Generalized Linear Models$ H5 ?: x& B6 ?( i- V
3.1 Introduction* T7 b6 i* [  a* u1 F4 k
3.2 Exponential family of distributions1 v: @4 I* h/ B! }% N$ c
3.3 Properties ofdistribution s in the exponential family' U) v9 }# s8 _, m9 y; O$ i
3.4 Generalized linear models
2 s9 x6 f: `+ I6 o# Z3.5 Examples
4 E* X. P7 t/ J1 T+ a7 ?; Q* c3.6 Exercises( O& g! B) `8 E3 w0 U, b
4 Estimation* B9 E+ Z. x- y3 p
4.1 Introduction/ p1 Q0 z) q7 d# g
4.2 Example: Failure times for pressure vessels; Q" x; Q/ F0 x! I
4.3 Maximum likelihood estimation
( s4 f  ~! c' |0 _; p6 X; S, c: b4.4 Poisson regression example& E9 a  q$ [$ m* b
4.5 Exercises2 l1 v" P1 \& C' o
5 Inference" y7 j( v! @9 x7 L9 u5 K
5.1 Introduction+ j5 J: t4 w3 M
5.2 Sampling distribution for score statistics% D1 `7 A  |7 V! K" N
? 2002 by Chapman & Hall/CRC
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5.3 Taylor series approximations1 b3 o3 ]  O" A. I1 Q4 G  M
5.4 Sampling distribution for maximum likelihood estimators- T) D5 e1 K& \8 \8 m9 m
5.5 Log-likelihood ratio statistic; F4 \4 W8 m/ q! [( K
5.6 Sampling distribution for the deviance1 O: E4 _' \% W8 [
5.7 Hypothesis testing
( Q* L. l4 x/ V# X! X4 m5.8 Exercises
4 l4 s3 @" @" R- T2 h' c6 Normal Linear Models4 U5 ~$ }# N& q, Z3 ~5 P
6.1 Introduction+ w, j6 B" D* H4 y0 a
6.2 Basic results& p# C( z" ~5 G$ }3 \( I
6.3 Multiple linear regression
6 K7 i3 y9 R6 R6.4 Analysis of variance
  H7 I2 i" f) v+ u% T; `/ @% c) ^6.5 Analysis ofc ovariance
+ d* _0 a$ f4 H7 e+ p6.6 General linear models
" ^' o! ~. Q# X5 Y" i6 O5 m6.7 Exercises6 u& ]/ U* }+ i8 s, u
7 Binary Variables and Logistic Regression% C. i8 X: l' r
7.1 Probability distributions) s" P' Y( g- f0 c
7.2 Generalized linear models1 |0 z( y, h6 L- H8 R3 P# m
7.3 Dose response models
8 Y" I5 H0 l/ u, X: L. a: m7.4 General logistic regression model
2 P+ h! G- L) @8 S  D# H% F% ?7.5 Goodness offi t statistics5 d. @* e# x% Z3 ~
7.6 Residuals
4 b8 o2 E8 e4 P) `3 y5 N4 E7.7 Other diagnostics
% k: l  v# U; ?6 p" s$ {; i7.8 Example: Senility and WAIS2 ]* h2 E- [* O2 J4 [
7.9 Exercises
  ]8 {6 E4 x+ l9 z8 Nominal and Ordinal Logistic Regression
" r: R" r; N7 u; g7 f4 [! F0 v8.1 Introduction- d; @4 F2 @" z
8.2 Multinomial distribution+ B% p' S4 m% P, w2 j  T
8.3 Nominal logistic regression
- [4 ], C1 \; v" O8.4 Ordinal logistic regression  M" c$ n$ `* G( }0 k( U# N
8.5 General comments
; a' H' x% I# T- m" h8.6 Exercises
, ]' e+ l4 P" s# Y$ y9 Count Data, Poisson Regression and Log-Linear Models
/ o; `) E4 K: Q' D9.1 Introduction
: _) n4 H: p9 A# ~. S* r" G- X9.2 Poisson regression) E. H7 C) B/ R# B
9.3 Examples ofco ntingency tables" G3 u7 A4 l- A4 L# z
9.4 Probability models for contingency tables
& F3 o# h* \; k9.5 Log-linear models4 i: L: m! l8 `" @5 n
9.6 Inference for log-linear models6 [7 h8 j! l1 Q* g- _3 I7 V
9.7 Numerical examples
+ u' {' D, [4 |9.8 Remarks  A* o3 g) Q0 _! E+ J
9.9 Exercises9 ?  {5 I  ]) W0 u' j$ b
? 2002 by Chapman & Hall/CRC2 O- f4 ]8 Y9 V; d
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/ u8 v3 m' Q, z# v$ X; R/ B10 Survival Analysis
6 w& i/ o: [! A6 y7 N10.1 Introduction
# U2 C, y/ I& v$ d1 u10.2 Survivor functions and hazard functions- t+ q! K' Y6 z- Z7 H
10.3 Empirical survivor function* b' e& L4 r9 ~& k9 E. b! C% e, W: o
10.4 Estimation, I, t5 K8 |% P5 F/ u& O
10.5 Inference" y  h5 D3 `1 }# j
10.6 Model checking
. u: {8 @/ g: E10.7 Example: remission times6 k$ Z( d: E6 W3 ~; X
10.8 Exercises
/ C+ _# ]% p! k11 Clustered and Longitudinal Data3 ^6 o1 {, |8 v) Y
11.1 Introduction
' b% Q2 D" k! y- b11.2 Example: Recovery from stroke
+ Z, G# ]1 y8 I7 K) \11.3 Repeated measures models for Normal data
8 \3 R' y, Z' S2 b- ]% u11.4 Repeated measures models for non-Normal data. B% g- a4 @$ |( Y9 h
11.5 Multilevel models
! ^: N; v& U7 S, q- A5 p11.6 Stroke example continued
7 q* m3 M4 x2 q/ Y% q11.7 Comments
, [/ j- g2 r( b6 ?# D. p. N! o11.8 Exercises
3 t, G) a$ ~7 _$ X" L: e7 C* WSoftware# @0 @5 c- N( n) ?' ]
References$ J* Q: g4 I2 \1 G5 V7 _7 {
? 2002 by Chapman & Hall/CRC  n! ^9 W5 ~# C; u' f0 T
7: v, V+ S5 q' x
Preface/ c2 v8 [' v& ?9 n: Z
Statistical tools for analyzing data are developing rapidly so that the 1990* B8 c# ?6 q  V4 B+ x( X6 x
edition ofthis book is now out ofdate.
8 Z3 X. Q& _+ xThe original purpose ofthe book was to present a unified theoretical and4 S" F: r4 {4 e4 ]. o2 w9 n
conceptual framework for statistical modelling in a way that was accessible
. Z" O- _9 g5 w) O$ r$ Z+ Lto undergraduate students and researchers in other fields. This new edition# _+ b, K* ?2 T
has been expanded to include nominal (or multinomial) and ordinal logistic
, V; x# M5 F# M) n1 j, t  Bregression, survival analysis and analysis oflongitudinal and clustered data.
% O4 X9 ?  F1 \) v0 ?( V- R) RAlthough these topics do not fall strictly within the definition of generalized; L7 |# w! `; v3 e" u
linear models, the underlying principles and methods are very similar and
1 k: t3 n6 l6 [6 i( ptheir inclusion is consistent with the original purpose ofthe book.
% d" x+ F. q  n# r2 F5 f4 H6 TThe new edition relies on numerical methods more than the previous edition0 V6 ^6 d6 p- t: f9 g2 ^
did. Some ofthe calculations can be performed with a spreadsheet while others
6 w" J" ~( T6 S  frequire statistical software. There is an emphasis on graphical methods for! N. x0 {+ k' X% y2 L9 {7 ^7 ~( n
exploratory data analysis, visualizing numerical optimization (for example,
5 b/ ?0 U% z+ e0 p7 ~, Cofthe likelihood function) and plotting residuals to check the adequacy of& Y6 t( g/ g5 d+ ]5 n9 z
models.
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1 ]) X$ r% {" e3 jIntroduction* D7 W& M% i, o5 ~+ ~( m  z- [/ d: s
1.1 Background$ o; h4 k; K# t" f. s: `
This book is designed to introduce the reader to generalized linear models;
' Q  d2 z: g2 a4 D6 |7 @9 x, \these provide a unifying framework for many commonly used statistical techniques.4 H. @4 |* |+ T, w: F# \
They also illustrate the ideas ofstatistical modelling.6 H7 A$ S" J, H4 P: k  a" ?1 N
The reader is assumed to have some familiarity with statistical principles- F( n: Z+ f9 p/ C4 c( n! C
and methods. In particular, understanding the concepts ofestimation, sampling! [% h" i2 y  I' W8 @+ s
distributions and hypothesis testing is necessary. Experience in the use& M- o4 d, q7 W+ @' r% K8 f
oft-tests, analysis ofv ariance, simple linear regression and chi-squared tests of2 D) p" D) Q" C5 ?
independence for two-dimensional contingency tables is assumed. In addition,+ q/ g- L1 A  A+ M( n1 C
some knowledge ofmatrix algebra and calculus is required.8 b5 J% }) |* e$ k+ K; A! \
The reader will find it necessary to have access to statistical computing1 Y* A. C# t8 C1 F$ y3 k/ l
facilities. Many statistical programs, languages or packages can now perform6 k$ M, [. l: s- \" j
the analyses discussed in this book. Often, however, they do so with a different7 v' J1 B" w% l' `
program or procedure for each type of analysis so that the unifying structure# K4 x' \  Y+ E8 J# ~* k
is not apparent.
, s3 ^1 H! b3 V! t# jSome programs or languages which have procedures consistent with the6 P+ \- y+ ]7 `7 I+ H
approach used in this book are: Stata, S-PLUS, Glim, Genstat and SYSTAT.
) [% J) z/ Z* j- L+ q( mThis list is not comprehensive as appropriate modules are continually
0 `2 K' c* `$ R! X" w4 d3 Fbeing added to other programs.
) h2 g# Y% |3 _9 S9 J: A  u; U* q& KIn addition, anyone working through this book may find it helpful to be able
. j" O% U/ }, @. Z  k& ^% cto use mathematical software that can perform matrix algebra, differentiation/ |6 l4 t6 v. E
and iterative calculations.
9 A, ?9 Q( i) A" y/ ?3 F& z, }1.2 Scope
* t) J+ v$ I* x( b7 HThe statistical methods considered in this
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v威枝
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