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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
' o5 A! {- w- p' T' M3 yPreface5 P$ ?; k! O% F
1 Introduction) h' D. Q, y  k9 i7 H7 G
1.1 Background" t% o: n/ }1 ?4 \: j
1.2 Scope9 w* u  z8 C' M& Z+ m5 S' a. }
1.3 Notation; J( X# y. C) v& r
1.4 Distributions related to the Normal distribution  p6 \# h4 n2 E4 S
1.5 Quadratic forms4 ?( O% @* d/ n& Z  k2 M# R8 P
1.6 Estimation
' T) W& J1 a% J# U- u1 u# U1.7 Exercises
4 s0 P' G5 o3 k9 B& Y% u1 c2 Model Fitting1 H- U. v8 i7 Z  j  s
2.1 Introduction6 {4 w3 G. o+ T2 ^
2.2 Examples
- q. u; @9 c4 v5 P2.3 Some principles ofstatistica l modelling
; q# r( h; F" E3 P) e' P2.4 Notation and coding for explanatory variables
* i) {8 P1 n4 D2.5 Exercises1 G- J0 q8 G) X$ j" u5 }6 X0 R
3 Exponential Family and Generalized Linear Models' |& y( g+ _1 q" y" ^! v. S
3.1 Introduction
( O# S2 R5 u( P3.2 Exponential family of distributions" |9 z7 ?; m# J. T. `
3.3 Properties ofdistribution s in the exponential family
2 I1 g5 g$ J8 [, C) S# e3.4 Generalized linear models
! l9 N7 U; \$ i1 o1 c) D3.5 Examples1 W& o* D5 t; q4 g
3.6 Exercises8 _4 I+ b6 w. W- y
4 Estimation
- P9 D" Q0 B& v& t- ~" C4.1 Introduction
" w( v' J* `  q5 u; g1 d$ L4.2 Example: Failure times for pressure vessels  ]- g; g! w3 ~" X7 J9 U+ q2 f* B
4.3 Maximum likelihood estimation5 Z; L4 H( m, t3 T; s
4.4 Poisson regression example
9 w, ^/ w* h: |4.5 Exercises2 J7 g5 ^+ Z8 d, L
5 Inference
+ L, H8 I. l2 N3 g5.1 Introduction
8 f( D% U; F1 b7 `. W5.2 Sampling distribution for score statistics
4 C; U' d7 e- p' C( u& W2 \) L? 2002 by Chapman & Hall/CRC
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5.3 Taylor series approximations
8 H9 M2 \2 K6 M5.4 Sampling distribution for maximum likelihood estimators/ j6 P! T3 ~: Q7 K& A6 v9 V7 K
5.5 Log-likelihood ratio statistic4 S$ b1 Z! h+ A4 C. _8 y" S4 Q
5.6 Sampling distribution for the deviance2 O# B; Z! e" ?2 U$ l, h1 n
5.7 Hypothesis testing- h) W. ?  u+ \* g: l( L
5.8 Exercises$ u& Y' _6 |) x
6 Normal Linear Models0 ]. h+ N( y/ r, s6 ^$ P$ w2 A6 C4 }* |8 z
6.1 Introduction" l) `& ^' u: @) e& ~
6.2 Basic results
. O: w: E* N: ^  X! G2 F6.3 Multiple linear regression( f8 K! N  y8 K9 T# @' W/ J! Q
6.4 Analysis of variance$ A* P& U3 t2 Z
6.5 Analysis ofc ovariance, r) T  Q) A2 ~7 u# z" p3 l
6.6 General linear models, m7 n7 Z& }9 X4 V$ ^
6.7 Exercises
4 z. u$ f% u4 ]9 s- h) l7 Binary Variables and Logistic Regression4 R% r/ V, M: m, J
7.1 Probability distributions
& s; H9 C2 Y+ \2 \+ ~1 N" g$ R( _7.2 Generalized linear models
, w5 @# N$ T: ~+ n5 T1 q! P  O7.3 Dose response models
' Z: k+ `/ I: I% C! u7.4 General logistic regression model# [: E& i  f3 n7 P0 S
7.5 Goodness offi t statistics
$ M" j$ C/ f& l* u; G% g: O7.6 Residuals
- g$ P' d" Z0 ^5 n: k9 J! Z7.7 Other diagnostics3 c' T3 r0 I- C2 L
7.8 Example: Senility and WAIS
( u4 G: f7 O- ], ^* T7.9 Exercises; ?$ e9 z- D  L1 }# b8 g
8 Nominal and Ordinal Logistic Regression
# b2 A' J4 o* a* s8.1 Introduction
) D. _. P' L6 V+ h( p! n8.2 Multinomial distribution
$ X8 [! x: j6 N" [8 w$ k8.3 Nominal logistic regression" h/ S( F- y4 ^
8.4 Ordinal logistic regression
' e( z8 @8 M/ o0 O6 j0 D8.5 General comments% ?' t9 Z2 P+ w1 M0 d
8.6 Exercises1 B0 u! b) d( L$ C* ~
9 Count Data, Poisson Regression and Log-Linear Models- G' @! x/ c  [8 D2 n
9.1 Introduction
" `3 u0 I9 g# I* q7 c9.2 Poisson regression4 u( P: O7 E- B  Q9 T$ X+ b6 Q
9.3 Examples ofco ntingency tables$ Q8 _# A! j. h- M9 Z: G
9.4 Probability models for contingency tables
% v* [. w  K, U/ X1 ~8 u0 H9.5 Log-linear models
) e, _) G5 H$ K0 c  e9.6 Inference for log-linear models
, M- _& p& n6 M& D9.7 Numerical examples8 [: r4 c* V' s
9.8 Remarks
! t3 c- p+ k; ]' N, i2 z/ |9.9 Exercises3 m5 P. f0 w: {1 J+ f/ w7 J
? 2002 by Chapman & Hall/CRC
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10 Survival Analysis/ c9 ]5 S" N" t5 X+ G! e7 w
10.1 Introduction
8 a* e6 r; Y9 r/ ^( m  e' s10.2 Survivor functions and hazard functions
1 K- q0 B) e# D( O: p; v' W10.3 Empirical survivor function
+ \; p. ~. ~" ^5 K2 k- j- m1 `( `10.4 Estimation
' S" x# ]1 h: J5 L10.5 Inference
! N2 ^' Z4 J+ b. l) h10.6 Model checking
# g% ?% f/ e: W/ g10.7 Example: remission times
/ }" _; y7 x' }3 W10.8 Exercises
6 y: }* y; Z3 F11 Clustered and Longitudinal Data) Y6 |- z7 C5 o0 `, C2 K8 z
11.1 Introduction1 `: l0 `6 T4 i
11.2 Example: Recovery from stroke
+ |; k$ R2 m+ Z11.3 Repeated measures models for Normal data
6 X/ U6 b. T8 n; Q- y11.4 Repeated measures models for non-Normal data
& K+ X: Y3 l- h$ e# Z7 m8 M7 o11.5 Multilevel models7 N' e- O0 `" B2 h5 h; W( l
11.6 Stroke example continued& L$ z6 ~0 K8 R7 X4 r9 E0 i3 t4 |
11.7 Comments+ l+ t6 F# |+ h/ c" J
11.8 Exercises
, X) b$ n+ Q9 E1 R1 K7 `Software
; B9 o& R( \4 a$ @+ ZReferences3 y' J$ {+ f. K( h- |8 [/ R
? 2002 by Chapman & Hall/CRC
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7 S' o, O( f1 o9 y2 W8 p- _Preface
* r& _# C- [  vStatistical tools for analyzing data are developing rapidly so that the 19900 Z9 p  q( O! Y
edition ofthis book is now out ofdate.& O; @! S8 l3 \) m. z
The original purpose ofthe book was to present a unified theoretical and
/ }0 V; d1 N+ J' xconceptual framework for statistical modelling in a way that was accessible  s8 P5 ~' L2 D5 D# \
to undergraduate students and researchers in other fields. This new edition
" z7 v. E/ S9 \" y' H3 I7 G' F# m# lhas been expanded to include nominal (or multinomial) and ordinal logistic
5 b( _/ ?& q0 N, E/ i4 zregression, survival analysis and analysis oflongitudinal and clustered data.* ^7 Z) i: s+ v' j( {( [
Although these topics do not fall strictly within the definition of generalized
0 }% @% F% ^& [1 a) c# l1 flinear models, the underlying principles and methods are very similar and' X" g, K- A* r& P: M6 K4 a
their inclusion is consistent with the original purpose ofthe book.2 Z2 Z' H. w* d0 S# G
The new edition relies on numerical methods more than the previous edition  E8 o7 e) p* [. T$ T. P
did. Some ofthe calculations can be performed with a spreadsheet while others
2 F4 F% ^/ W# Q# y  T/ xrequire statistical software. There is an emphasis on graphical methods for' |$ R0 V6 i: g( @/ H! p8 t4 N' H
exploratory data analysis, visualizing numerical optimization (for example,
7 i" s) o, J9 ?! l4 g& Fofthe likelihood function) and plotting residuals to check the adequacy of
3 N* T& Q% R! y9 S$ Ymodels.
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Introduction9 [. q% l; Y8 R) U. D: u
1.1 Background
0 F/ a& i% w; H: t7 WThis book is designed to introduce the reader to generalized linear models;5 Q( j1 t8 i" ?& Y3 o6 g4 j
these provide a unifying framework for many commonly used statistical techniques.
+ L# H) O) H1 W/ T! u$ Y: uThey also illustrate the ideas ofstatistical modelling.
3 |% [$ j1 I- X5 T+ u/ uThe reader is assumed to have some familiarity with statistical principles
( |( ?/ T# T" V  tand methods. In particular, understanding the concepts ofestimation, sampling3 s% {; A4 v4 ^0 h5 D! C& t7 V
distributions and hypothesis testing is necessary. Experience in the use
) V* w& z8 q6 g* B: P2 k3 N: Qoft-tests, analysis ofv ariance, simple linear regression and chi-squared tests of
9 ~" g; z3 ?5 f$ K2 a- t& Pindependence for two-dimensional contingency tables is assumed. In addition,
( Y9 n/ l3 A5 p% T& P0 isome knowledge ofmatrix algebra and calculus is required.
+ \' X' o2 b/ f) gThe reader will find it necessary to have access to statistical computing
( d& y+ {! `0 q/ H# N( R+ Nfacilities. Many statistical programs, languages or packages can now perform
0 F5 K! T+ ^' t" Y9 ?! Xthe analyses discussed in this book. Often, however, they do so with a different
, s  V3 b; r: Z& Zprogram or procedure for each type of analysis so that the unifying structure
9 O4 E" o% y0 c4 _is not apparent.
, B3 h1 k9 Y' z, `7 kSome programs or languages which have procedures consistent with the' p2 n' p9 l) _6 W) D# O
approach used in this book are: Stata, S-PLUS, Glim, Genstat and SYSTAT.
! J& g9 e) U) V: XThis list is not comprehensive as appropriate modules are continually
$ w$ h3 A0 H# M- ?0 a0 M5 G5 ybeing added to other programs.
* Q4 ]) p0 N) {) P1 T- cIn addition, anyone working through this book may find it helpful to be able
. Z0 X% A) ~0 v, ~  Kto use mathematical software that can perform matrix algebra, differentiation
  q/ [( L# l) P# y& N5 b) X) Wand iterative calculations.5 |9 m8 Y: l. x6 r; o
1.2 Scope/ `6 y" N( r1 Y7 t) Y& z
The statistical methods considered in this3 ^; _: O) O  ~; n! D; P
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v威枝
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