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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
+ ]4 W8 }( y. T0 ^$ T9 uPreface
, j& C: H9 @/ q5 q& h# |. L( Y1 Introduction) v$ _7 Q+ o' O: O! W' n
1.1 Background
+ D3 a' q' V+ r7 I/ x. ~9 B1.2 Scope
4 ^  [- c& v6 a* T0 b% y) U$ u; T' l1.3 Notation
7 B! c8 f, S1 p. m1.4 Distributions related to the Normal distribution# a1 u+ W( o& I! z8 P4 H# {; r
1.5 Quadratic forms* ^/ p( {2 ?1 U4 g4 Q% p
1.6 Estimation2 \; g6 U7 X0 i: y! z
1.7 Exercises- J  f! \8 u/ |, Y& b( b
2 Model Fitting4 s6 B+ J' l1 W& w7 |; T
2.1 Introduction4 y" v( E6 N0 n/ d
2.2 Examples
7 v6 N& F# j7 @4 S2.3 Some principles ofstatistica l modelling
* D, t! o& K# R/ L3 s. T/ P2.4 Notation and coding for explanatory variables% B) q" C0 ?( P! r0 l9 ]
2.5 Exercises: Q: {- \; W& I' K  ~  i& ]
3 Exponential Family and Generalized Linear Models
) \. ]4 R2 J( f6 b: {/ k$ e3.1 Introduction
# q; Q/ E: @6 E3 M% ]+ F% i! ]3.2 Exponential family of distributions
0 n2 {" J; c5 D7 D" j1 }3.3 Properties ofdistribution s in the exponential family
9 l2 e2 S' z: k0 B3.4 Generalized linear models
! b) @' b: g7 _' y( e3.5 Examples
8 y6 L6 |! }/ M# Y3.6 Exercises6 S  k) X) k4 _2 f8 n3 P( `
4 Estimation
2 n, H2 o  W$ J, `1 p; @$ z6 f4.1 Introduction
: l/ K' u; R, H! N8 D4.2 Example: Failure times for pressure vessels& d6 u0 ]7 m% i  {3 T% ?$ _, g, x
4.3 Maximum likelihood estimation
5 |8 _! O" i9 w& d# o4.4 Poisson regression example
( q' n& M" T# q, {0 x+ }4.5 Exercises
3 {7 e3 R+ z, @. X$ d2 j: I5 Inference8 d7 P! v3 r5 c4 R- \
5.1 Introduction6 e% l$ O  `& c; `
5.2 Sampling distribution for score statistics4 ?! y4 K/ C5 F4 C
? 2002 by Chapman & Hall/CRC
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- I2 F0 O: W( r# A) G5.3 Taylor series approximations
8 W6 o3 r- n. @: m. q% U5.4 Sampling distribution for maximum likelihood estimators
& |8 H) O# I) w# X9 m9 ~+ r( f5.5 Log-likelihood ratio statistic; j" e2 O/ l5 X" h/ w; g: M( F# k
5.6 Sampling distribution for the deviance! f) b$ j$ u' Q$ x% d& h+ G7 ~! y
5.7 Hypothesis testing( B  b2 C7 M% u; X7 w- `
5.8 Exercises. m  }; f! R/ J/ V( G; n
6 Normal Linear Models) M1 Q6 k* S! `  ~
6.1 Introduction8 y$ y  |3 Z6 {/ o
6.2 Basic results
- u( A" L9 z4 W! d6.3 Multiple linear regression
2 |6 Q8 I1 _* M* L! |. x6.4 Analysis of variance* Y9 w- K# `4 y/ k
6.5 Analysis ofc ovariance& T9 t; I3 C5 w
6.6 General linear models
  B. e2 j  f3 q6.7 Exercises
; F; n$ M+ c1 A7 Binary Variables and Logistic Regression2 s4 B$ m# ^1 x$ Y6 c) z: {! ?
7.1 Probability distributions
+ Z: ?1 L$ `: {& z7.2 Generalized linear models9 C4 z1 [& i) \5 P" W' B
7.3 Dose response models) W3 m6 g& ]# r* W- ?' D
7.4 General logistic regression model
3 G/ [# F* Q0 T4 @7.5 Goodness offi t statistics6 ]7 z/ U2 i& s* j* }
7.6 Residuals
0 G" O6 v5 y. y) _* H7.7 Other diagnostics. V- R, j8 z) `. W0 ]
7.8 Example: Senility and WAIS$ C) Q  [4 Z) z% l5 Y
7.9 Exercises
# J, s, D' g* h2 M' ?8 Nominal and Ordinal Logistic Regression
0 V& T! ~4 }  e2 Y+ S$ x- F8.1 Introduction
, s5 |2 ~/ i+ k5 b8 W+ ]6 H1 e8.2 Multinomial distribution
7 O2 K1 J. s3 G8 Y7 I8.3 Nominal logistic regression
/ p" I! Q9 a/ R8.4 Ordinal logistic regression0 y+ Q2 w6 J& W* J7 o. C: {# I1 h
8.5 General comments
, Y& I+ g: O2 o, M+ X8 i0 q+ s8.6 Exercises1 f6 }" l1 o6 q2 C0 ~' \' t; [4 t% f
9 Count Data, Poisson Regression and Log-Linear Models
' ?! u6 d) v3 m$ |7 @9.1 Introduction
5 u/ I% l+ j& k' a9.2 Poisson regression/ s! l- l  J( x8 \: d' B( Z
9.3 Examples ofco ntingency tables' T* {4 J3 g7 m
9.4 Probability models for contingency tables6 M6 C% f9 T, [# \. q+ y* {
9.5 Log-linear models
( ^3 o1 J' g) @/ e8 e  {' _: z9.6 Inference for log-linear models
, F+ z" \0 j' {6 q8 Y  ^# G, U9.7 Numerical examples
. f6 R/ y- _# t1 F) W; e: i9.8 Remarks! e0 n! }. @8 n9 X; ^& A2 h+ b. N
9.9 Exercises$ d- _  E9 k% h
? 2002 by Chapman & Hall/CRC
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10 Survival Analysis
' i. _1 I1 o4 }( ^4 y# [* M10.1 Introduction
9 Q) _$ F; X! v! ~5 [' |5 _( m5 S10.2 Survivor functions and hazard functions; m; f, y5 x5 [+ q* Z- M
10.3 Empirical survivor function! |* _- ~' G+ z% J
10.4 Estimation
0 z' e2 T  T( \8 @! \# t- h10.5 Inference
8 s& k3 _# w- t$ a% j, a10.6 Model checking0 f2 p# r/ ~( u. t
10.7 Example: remission times
" \2 |/ \; J! @8 d- ]: \9 ~/ o10.8 Exercises% a8 C; A% n& n" K) z  g) a
11 Clustered and Longitudinal Data
3 H: l$ A4 ]  W11.1 Introduction( M) G: _& V3 C( U9 u, U# O
11.2 Example: Recovery from stroke5 f% Z; A: [2 j! G5 `3 u5 L
11.3 Repeated measures models for Normal data$ |7 c9 e, [, \1 ]
11.4 Repeated measures models for non-Normal data$ \3 c" Y& v9 d
11.5 Multilevel models7 g8 u7 h6 E' G% s! Q* w1 o5 K* @
11.6 Stroke example continued4 C: N" C9 Q, U
11.7 Comments
$ `1 c( z, ]) F# V! k# L11.8 Exercises
* m4 i# c1 W, U' o+ i5 l6 {Software- u/ G) U- H6 @
References- J9 L6 }: }6 \: K7 k) m' B
? 2002 by Chapman & Hall/CRC
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+ s3 o5 Z2 o/ q5 W4 M& uPreface5 N" F) u7 g3 ?2 ?- l! |' K6 Y
Statistical tools for analyzing data are developing rapidly so that the 1990, W- K4 A! {4 I$ V1 Z) [0 v6 y) w
edition ofthis book is now out ofdate.+ n2 k' E/ G& k9 [; s. @
The original purpose ofthe book was to present a unified theoretical and4 n, a& ^. S: R) E. n
conceptual framework for statistical modelling in a way that was accessible- F  k/ _7 ^+ L- }
to undergraduate students and researchers in other fields. This new edition
9 \" b& t0 g8 p: Y/ Ahas been expanded to include nominal (or multinomial) and ordinal logistic9 \1 A% W) Z- Z% z
regression, survival analysis and analysis oflongitudinal and clustered data.# z+ U1 k/ K& t% k2 _$ P
Although these topics do not fall strictly within the definition of generalized
- i1 [/ E5 j) H% T, flinear models, the underlying principles and methods are very similar and
, O: L- X# C! k# E; ^" v  }their inclusion is consistent with the original purpose ofthe book.
8 a* I1 b% H$ u5 vThe new edition relies on numerical methods more than the previous edition& Z2 P' G. ~* `
did. Some ofthe calculations can be performed with a spreadsheet while others
+ s' F3 _2 f/ B) C% |' Grequire statistical software. There is an emphasis on graphical methods for( m8 v7 v' i* ^6 }" f
exploratory data analysis, visualizing numerical optimization (for example,0 M- B$ e# w: s: A9 m+ p
ofthe likelihood function) and plotting residuals to check the adequacy of
0 e5 x0 ~" S8 u" I) F4 f. `models.
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( g8 P+ r7 G. r8 Z0 LIntroduction0 {- ^( x! p" m: g8 O
1.1 Background6 J3 ^0 h  P+ K2 h
This book is designed to introduce the reader to generalized linear models;3 t4 q" J; K' h: ~: @" Q. H1 ^6 a
these provide a unifying framework for many commonly used statistical techniques.  F3 p9 u1 s7 G3 D8 p4 t
They also illustrate the ideas ofstatistical modelling./ A" H: m! n  G$ N
The reader is assumed to have some familiarity with statistical principles
) q" G% Z: _+ _and methods. In particular, understanding the concepts ofestimation, sampling  ~! k4 s: A, K' V# w
distributions and hypothesis testing is necessary. Experience in the use
- S" V, D8 _7 o3 @9 Qoft-tests, analysis ofv ariance, simple linear regression and chi-squared tests of# T! T' m  g6 w
independence for two-dimensional contingency tables is assumed. In addition,
6 Z/ g4 y" f' s6 Q! g1 Msome knowledge ofmatrix algebra and calculus is required.
% ^9 ^4 i+ o, ?( qThe reader will find it necessary to have access to statistical computing. I* r6 C' K1 C; ^5 z) H2 J( A5 w6 ?% U
facilities. Many statistical programs, languages or packages can now perform. ~/ @2 f" Y# h4 r( Q8 ~- u' c
the analyses discussed in this book. Often, however, they do so with a different
4 t% d+ C9 N: f% |6 }" N# r& rprogram or procedure for each type of analysis so that the unifying structure
" |- q6 t0 W! ~# q- H2 B+ Z4 yis not apparent.0 G; B) J/ S8 I
Some programs or languages which have procedures consistent with the6 j9 J* r" \2 P: e* E, |5 C! K
approach used in this book are: Stata, S-PLUS, Glim, Genstat and SYSTAT.1 u1 N2 ]. F2 H5 V- ^4 G" M
This list is not comprehensive as appropriate modules are continually% H1 F0 \- w% b* d/ t
being added to other programs.
. Y! |& y, o" i; e6 ]0 ?3 GIn addition, anyone working through this book may find it helpful to be able% i/ Z0 ~" ?% ]/ X3 d6 V
to use mathematical software that can perform matrix algebra, differentiation* Q, q! b" j% n  g, l! y6 b
and iterative calculations.8 T1 }6 f' I+ f$ M
1.2 Scope
; B' C5 o+ I- v+ fThe statistical methods considered in this
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+ Q! D% A5 g3 _8 h5 d$ o. K9 {v威枝4 Q1 G0 L  m7 u9 J. h# ?
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