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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' P' p; M2 }5 i2 M: C& p
Preface- T5 D2 ~: z* a5 R. V
1 Introduction
! [1 A! k6 g* o3 |1.1 Background- T& W$ Y' r/ M, l0 d8 X  e5 D
1.2 Scope
  ?/ I- M5 \$ h# m6 g% F/ V' Q2 |' @1.3 Notation
: N5 U! k" x  F+ d5 n% B1.4 Distributions related to the Normal distribution2 H( z1 Y: m/ W$ x0 l/ t4 y
1.5 Quadratic forms
0 P3 B3 K% z& f  m" y1.6 Estimation, O* B3 \% s6 ^: e2 \. N' C6 d$ j
1.7 Exercises+ D4 ?" q. g& R8 j$ j) n
2 Model Fitting
  m6 [# `6 F" N- k2.1 Introduction
. E- M6 X; p2 C% t1 m! |2.2 Examples
. T) w3 Q# A6 L1 N* r4 _2.3 Some principles ofstatistica l modelling
" V" R2 a+ j" E  X! h, k2.4 Notation and coding for explanatory variables* i1 C5 M( P; h- V, @
2.5 Exercises. E. U4 v# w0 M! j* J
3 Exponential Family and Generalized Linear Models9 L" f$ a7 [" y  k7 B. h5 a
3.1 Introduction
# y0 s! h' m) L0 _7 C3.2 Exponential family of distributions6 t  o2 k8 }- Q4 u9 p4 p
3.3 Properties ofdistribution s in the exponential family9 }% W5 f1 F( d5 K: X
3.4 Generalized linear models9 t4 E! @/ Q% J: K" h
3.5 Examples5 C) b5 x% w: J3 Z
3.6 Exercises
( Q. A# t0 l7 k9 v* `% m& p4 Estimation
, l$ G0 H4 W0 _% e" G4.1 Introduction) P, h$ U" p3 x' W. O% N
4.2 Example: Failure times for pressure vessels
- [6 F$ s1 X9 R" L- R2 g4.3 Maximum likelihood estimation
0 M& H5 W- L" m' O0 A4.4 Poisson regression example& }! D4 A0 k: \& `
4.5 Exercises2 x& s% f9 D: t/ P$ L3 w
5 Inference/ y3 b7 j1 D7 H: p! ^: j
5.1 Introduction9 g  {2 b, T7 `" B
5.2 Sampling distribution for score statistics6 D+ w9 u: {4 T
? 2002 by Chapman & Hall/CRC
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5.3 Taylor series approximations
2 i* U, [+ u$ X) U, M( k5.4 Sampling distribution for maximum likelihood estimators7 Q( [4 S& e# {5 H
5.5 Log-likelihood ratio statistic4 I9 p3 M( x/ H* ]
5.6 Sampling distribution for the deviance
5 B% G0 n" `8 U0 S0 h# C) i1 q5.7 Hypothesis testing1 B. n9 c7 s/ F7 D, x1 p
5.8 Exercises
" V5 `" A# |- ?: Y, }4 y: X8 `6 Normal Linear Models
7 v2 ~4 ~" U4 N/ A6.1 Introduction
# G; R+ c7 l/ r1 u  ~6.2 Basic results
5 e3 W1 p1 d# y' R4 W. n3 |6.3 Multiple linear regression' g$ R& E  X& ~
6.4 Analysis of variance
7 l- w0 Y7 R; I/ h# C3 f6.5 Analysis ofc ovariance
' m/ b. W2 }/ `' B6.6 General linear models- h' |. K8 x2 \; Z5 i* Y
6.7 Exercises$ M0 F  ?' Z8 L, i  p
7 Binary Variables and Logistic Regression
# @  e  U5 ]7 F2 d" P- X: J7.1 Probability distributions* J7 D4 }1 I1 J4 A7 z
7.2 Generalized linear models2 G: k# L2 x0 C( {2 y
7.3 Dose response models
8 C, y, f- z6 i; \( p7.4 General logistic regression model5 ^3 [; u( F* @) p/ U; v5 V8 ?% I6 v* y
7.5 Goodness offi t statistics9 h6 ^9 a9 ~2 h2 R( t4 \
7.6 Residuals: S  E! u9 L3 }' C- H
7.7 Other diagnostics' ^8 K, V  B/ W
7.8 Example: Senility and WAIS+ \2 Z% r3 |, l/ e
7.9 Exercises- c. X6 A+ X: i& B% B* F
8 Nominal and Ordinal Logistic Regression! Z5 z% a; v; H' [
8.1 Introduction
4 z0 D% U5 M& e2 U8.2 Multinomial distribution
7 \. T* [8 ?  L5 ]! a8.3 Nominal logistic regression% v' ^! K8 d3 v) k& K3 V2 K. r
8.4 Ordinal logistic regression7 H% {1 F8 F- p; `2 l. Y" }
8.5 General comments
% T& @) d+ `4 H7 J0 E& L8.6 Exercises
! n8 k: _  e. F8 Q; q9 Count Data, Poisson Regression and Log-Linear Models
" y# N9 s3 A* H, e9.1 Introduction
4 _. \. k& ~$ g1 U/ G9.2 Poisson regression9 Z$ B9 P7 C! c
9.3 Examples ofco ntingency tables
/ i+ d1 z4 w' H3 E+ i7 I: F9.4 Probability models for contingency tables, h/ I; T& M* W
9.5 Log-linear models9 H  L: Y( O8 X( Y6 P6 g
9.6 Inference for log-linear models8 J5 o/ V; E, r
9.7 Numerical examples+ e7 }+ o! R* c  g: V3 ]
9.8 Remarks; G6 I) W1 h) b# j) c1 Y5 c. G
9.9 Exercises
8 S" t" k0 j; v. a- @? 2002 by Chapman & Hall/CRC
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4 S* O1 U$ L, o, g6 N10 Survival Analysis
  x- E1 l. r% W10.1 Introduction1 u5 F+ M0 y0 e$ S- a
10.2 Survivor functions and hazard functions+ t& e! f4 L9 G8 C, }; s5 l5 y0 J
10.3 Empirical survivor function& j0 O9 [5 U8 ?; H3 a/ X# y! y
10.4 Estimation1 u& S1 q+ w3 @
10.5 Inference
5 A. ^! S! p( j8 r9 d10.6 Model checking
. W) Z( K$ ]( h" p10.7 Example: remission times' `( D) o+ J0 T. V2 H
10.8 Exercises
& N( g4 O  e9 U% w' h1 K11 Clustered and Longitudinal Data5 B" F; p: s7 N0 Q
11.1 Introduction
: s8 W( O+ }2 _11.2 Example: Recovery from stroke
9 o9 L: \) J% ]3 g11.3 Repeated measures models for Normal data
7 o% _! p% C: {) h6 j  e9 k% i, a11.4 Repeated measures models for non-Normal data% X5 `/ R( j' t, J
11.5 Multilevel models* n( J1 ^3 g( t' m; M* d
11.6 Stroke example continued7 S6 g: d( h8 C. y1 Q. m6 [
11.7 Comments
( [9 r0 ^+ S% F6 w11.8 Exercises
, y6 ]$ x5 \6 M2 HSoftware; ~0 b! T' j) u$ D7 e$ y# V5 Y* ~
References( }: i2 U8 }, ^! T2 l
? 2002 by Chapman & Hall/CRC
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Preface
4 P; P$ l* G" S+ V* kStatistical tools for analyzing data are developing rapidly so that the 19908 K+ j7 \) n2 O) ~
edition ofthis book is now out ofdate.
* B8 x: U+ c3 `7 ?# R! u1 B3 zThe original purpose ofthe book was to present a unified theoretical and3 F8 _* q* H$ L3 y- N( E
conceptual framework for statistical modelling in a way that was accessible
/ j  [" _2 q6 ~( Vto undergraduate students and researchers in other fields. This new edition
" g5 B, T% F8 W- u% mhas been expanded to include nominal (or multinomial) and ordinal logistic
1 M! @) T1 a) ]% L) Jregression, survival analysis and analysis oflongitudinal and clustered data.3 o! m& |6 O. v; Z
Although these topics do not fall strictly within the definition of generalized
, T9 D2 S! q0 W) |# Y; J6 slinear models, the underlying principles and methods are very similar and( D4 l/ }% v  d7 ]
their inclusion is consistent with the original purpose ofthe book.
  V" B9 s4 @+ rThe new edition relies on numerical methods more than the previous edition# s2 N' B9 V/ K, D' X
did. Some ofthe calculations can be performed with a spreadsheet while others1 Y- |# q) q4 m
require statistical software. There is an emphasis on graphical methods for; b+ N0 G3 w2 z
exploratory data analysis, visualizing numerical optimization (for example,
: B+ m# Z0 X& a* x/ K3 H5 S/ cofthe likelihood function) and plotting residuals to check the adequacy of
- R) m2 Q( s' Q5 ?3 \. J% ~, Ymodels.
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  g& N, [: v2 I2 z% f5 Z$ kIntroduction# s1 I) k6 o/ k5 J3 E! H
1.1 Background1 w" t( e( c" J! T. e* A, o
This book is designed to introduce the reader to generalized linear models;, R: R! n! X3 ~7 d( w# Y$ R
these provide a unifying framework for many commonly used statistical techniques.
1 B' N/ O0 y1 G6 I. j9 @$ b( d% qThey also illustrate the ideas ofstatistical modelling.
' X* }. E$ P5 I2 fThe reader is assumed to have some familiarity with statistical principles
- l+ a5 k) U+ oand methods. In particular, understanding the concepts ofestimation, sampling& n: C0 l$ g+ Z: A
distributions and hypothesis testing is necessary. Experience in the use
+ h6 Y- Z+ @/ C+ r7 n, noft-tests, analysis ofv ariance, simple linear regression and chi-squared tests of
3 `# Q. i' O, @+ _! ]: ~independence for two-dimensional contingency tables is assumed. In addition,9 u! ]$ g1 }6 M/ }1 D; z; m
some knowledge ofmatrix algebra and calculus is required.1 S- O/ c0 t' H
The reader will find it necessary to have access to statistical computing, M) H1 S' V2 Y" I8 @- o6 l
facilities. Many statistical programs, languages or packages can now perform. c% m8 a# p' A4 [/ K2 B
the analyses discussed in this book. Often, however, they do so with a different$ h; w' b) {0 v2 m
program or procedure for each type of analysis so that the unifying structure
- S) y3 c$ @4 C2 p8 @- `is not apparent.2 J% E3 M  }7 j( @9 L9 w, R; P" c
Some programs or languages which have procedures consistent with the5 j& L6 ]" T. B( w
approach used in this book are: Stata, S-PLUS, Glim, Genstat and SYSTAT.
+ c# W+ H  s( ~3 ^; b+ w% T: iThis list is not comprehensive as appropriate modules are continually: e+ f2 F* }4 J1 u4 M6 f  U
being added to other programs.0 z) j: T& O- S, W) R
In addition, anyone working through this book may find it helpful to be able
; j  L2 k4 I" Y7 _5 _0 I% Xto use mathematical software that can perform matrix algebra, differentiation
: e  @. i4 |% D1 w( ?& e) @( f1 ~8 Kand iterative calculations.
6 O+ T# R( e' E, _8 m) t, F1.2 Scope1 t" _% \- u, {+ `
The statistical methods considered in this
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v威枝
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