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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, c. ~! N* p; s, `& y4 A( D! T
Preface
1 O- Z7 ?" y& ]; h" K3 v. z$ q2 A9 {1 Introduction
+ a# _; A: q% ]8 c# c3 a1.1 Background
6 U% {% y0 K6 y" C8 G6 J$ L- o1.2 Scope5 W8 Y7 _. `& y' i+ m3 C
1.3 Notation8 j! M" X) v! ~4 t# T
1.4 Distributions related to the Normal distribution
( c6 Q) \  V/ n% @8 V( F6 b' C# W! |4 h1.5 Quadratic forms  N- W" K# e5 I4 a
1.6 Estimation  O& a1 ~6 `" w- l$ E$ \+ [4 x
1.7 Exercises$ R8 c9 [) N0 I5 B: u' P
2 Model Fitting
# }  e0 ^3 V- z7 E4 T2.1 Introduction+ H) f; y, Y/ N, ~8 }+ Z+ P8 k
2.2 Examples1 K* U3 l* e$ |! l
2.3 Some principles ofstatistica l modelling3 X9 [5 L- F, L6 @4 K9 H+ h% a
2.4 Notation and coding for explanatory variables) u7 `  c& e$ E0 J5 q$ i
2.5 Exercises
4 `, y' m: S: @1 n" S3 Exponential Family and Generalized Linear Models$ k. I" L7 H1 o2 |8 `1 g! N: K
3.1 Introduction4 z1 e) r3 m" B5 y. X
3.2 Exponential family of distributions
" j% A5 G% j, `/ `3.3 Properties ofdistribution s in the exponential family
; J2 k9 o9 [$ G/ n$ U7 J# |5 ?9 O3.4 Generalized linear models
: T- P' V) o, w6 Q' ]3.5 Examples/ V1 j1 a1 l8 h) U$ Z% H2 ~
3.6 Exercises
* V. H$ f: K; l: [3 s4 Estimation
& h: e1 e. Q7 f, k5 b& L% c. D4.1 Introduction
2 R' U1 l0 w2 Y) b4.2 Example: Failure times for pressure vessels
/ o9 I2 f2 N2 t1 W& `* ^4.3 Maximum likelihood estimation
# E+ R; l! t( N: o$ N" l5 k4.4 Poisson regression example
3 s3 R) D8 T5 D+ q- Y4.5 Exercises
' J$ [# K9 |8 o4 E+ ]5 Inference
: c& l* f; P' H! M& m2 I1 ?+ G/ ?$ T5.1 Introduction8 x$ {# N6 Z, Y: {
5.2 Sampling distribution for score statistics8 x. i2 {- k/ Y! V/ i; f& p( X% w
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5.3 Taylor series approximations
8 c( U- j& W4 D' q% S4 v. v5.4 Sampling distribution for maximum likelihood estimators) x' `7 q* r3 y( }# @' ~
5.5 Log-likelihood ratio statistic% l; O3 Z, a2 s3 g' p
5.6 Sampling distribution for the deviance; j& t" V  j" G9 R
5.7 Hypothesis testing6 f* i8 u" {$ a; D2 r8 ^
5.8 Exercises: |/ I( z: d2 m7 i
6 Normal Linear Models5 f- v  r& k5 F& P" N
6.1 Introduction
# f" a" B# s5 c1 N3 p6.2 Basic results$ L) y0 g6 e0 I+ U3 o
6.3 Multiple linear regression) y5 ]+ B0 z) |1 R9 b
6.4 Analysis of variance
  w% g) ^! N" E  y+ i' A! f. J6.5 Analysis ofc ovariance2 |! l( n$ S' o4 x8 {9 f  a8 ^! e
6.6 General linear models
7 Y7 |8 H1 p8 k' a, L6.7 Exercises
- g2 e' s* n: a) r7 Binary Variables and Logistic Regression' U$ ]  C( I4 O8 `& r! f& Y
7.1 Probability distributions
  j; y+ b; g( y5 c3 y7.2 Generalized linear models
. [2 P( `' G7 e6 y) N. r8 Y/ |7.3 Dose response models
) w4 C- E& g- c! g7.4 General logistic regression model4 v4 d: Q# w0 D' z
7.5 Goodness offi t statistics: w: W' O+ k* [8 ?
7.6 Residuals0 B) U1 V3 v. l6 U$ P5 r5 Z
7.7 Other diagnostics; |/ A( a" j/ w4 K2 f" q- l% l
7.8 Example: Senility and WAIS
. p% r4 x/ c% Q1 i4 p+ Y7.9 Exercises0 M$ W- ~& e2 }4 I' k& Q
8 Nominal and Ordinal Logistic Regression  X; S4 }  Y5 ?: g2 b6 e) s9 e9 D5 h
8.1 Introduction
/ i8 x% T5 u% w9 F% i8 J8.2 Multinomial distribution( A- ^2 o( H' r5 d
8.3 Nominal logistic regression
/ c5 W0 ?7 \& Y) x  ~. X& ]8.4 Ordinal logistic regression
1 w1 H& e% |& M8.5 General comments9 L" j" a* U3 J+ A
8.6 Exercises# Y1 p+ a, d& Q) e9 [7 {7 I8 k$ M+ U
9 Count Data, Poisson Regression and Log-Linear Models" \$ Y' j  E: B. M; x
9.1 Introduction0 v4 Q6 i- N* {' y: }
9.2 Poisson regression( k3 Z8 m2 W: t8 g
9.3 Examples ofco ntingency tables7 f+ f9 L! ^1 L  d
9.4 Probability models for contingency tables
* z$ Z2 E7 B$ g% _9.5 Log-linear models) d6 P0 u2 G. h
9.6 Inference for log-linear models7 k! }: z# _3 g, s
9.7 Numerical examples, k  N6 z6 v4 `* `* D$ t* O; P
9.8 Remarks( x8 I) c; U' ^- u* P: O0 U
9.9 Exercises: v) Z% L/ O6 M4 ^. I
? 2002 by Chapman & Hall/CRC
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6 I5 t1 c5 F, E2 \. y; q& Q; N  r10 Survival Analysis
1 q+ e; s: k0 D6 W& s! ]0 m! ?10.1 Introduction* f+ _& h- W& ]3 u& v* G1 c
10.2 Survivor functions and hazard functions
$ b+ {9 q0 s# E* F9 U3 b1 k$ i" v10.3 Empirical survivor function
4 |0 ]9 `+ p; L' ]6 C10.4 Estimation" w' L: s  t+ q( m8 |4 j( i! @
10.5 Inference; E' Q5 j. z; U2 L2 f3 F
10.6 Model checking0 e$ l+ w1 D$ a! n. j
10.7 Example: remission times5 r4 A6 }4 a8 `0 V  V* \
10.8 Exercises. P4 w& N4 O9 L* G; z
11 Clustered and Longitudinal Data
$ K: Q/ u3 e+ D( N11.1 Introduction
# Q3 A8 n3 ~% ~/ T8 `11.2 Example: Recovery from stroke& P7 J  \5 G& V4 x- E, S) q
11.3 Repeated measures models for Normal data  s- u( B* y0 Z% @$ T. w% u- S
11.4 Repeated measures models for non-Normal data) N% p% B% T4 b- R* o
11.5 Multilevel models; p3 ~; S( r9 Z% f3 \" B
11.6 Stroke example continued" I0 O! r; k3 s6 p4 d) O6 U
11.7 Comments
' D3 m: x/ I% t% z6 M8 U! v11.8 Exercises
% C$ z; f7 w7 s9 }3 Y) HSoftware0 [; n, Y! r, ~3 ~. L; C1 N
References0 N3 o& k) N& F: i# T. a3 S
? 2002 by Chapman & Hall/CRC
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( _, ~' H4 ]+ A- UPreface% r. i0 Z. t9 ]" s8 V2 Y
Statistical tools for analyzing data are developing rapidly so that the 1990- f  z1 {9 S! ^/ q
edition ofthis book is now out ofdate.
! X& [2 R" t9 k, VThe original purpose ofthe book was to present a unified theoretical and! g; ^' c# O2 n, \
conceptual framework for statistical modelling in a way that was accessible
% ~8 i+ Q% l1 @- B% d3 ~to undergraduate students and researchers in other fields. This new edition: k" t4 j! e1 N9 N1 O# ~% X
has been expanded to include nominal (or multinomial) and ordinal logistic6 L/ j  S7 N6 ]
regression, survival analysis and analysis oflongitudinal and clustered data.2 i, f- p7 }. M& w, a0 f
Although these topics do not fall strictly within the definition of generalized7 A% o) E# ]8 `
linear models, the underlying principles and methods are very similar and( X; a, B9 N1 i
their inclusion is consistent with the original purpose ofthe book.
% K. T9 D" k8 y5 H5 f8 R2 B* gThe new edition relies on numerical methods more than the previous edition% ^/ J) s0 l1 L, B' k7 O
did. Some ofthe calculations can be performed with a spreadsheet while others8 O+ n; U. p3 h  M# Q! }
require statistical software. There is an emphasis on graphical methods for
6 |  {& {; a% C* k2 Pexploratory data analysis, visualizing numerical optimization (for example,- W1 I4 Y" l6 Z: O# a
ofthe likelihood function) and plotting residuals to check the adequacy of
& ]/ @/ ~, h7 X6 B( Rmodels.
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Introduction9 @. V& F4 r4 U
1.1 Background, t3 L& ^+ |$ C! m* N+ N
This book is designed to introduce the reader to generalized linear models;
% |7 V9 s6 Z  ythese provide a unifying framework for many commonly used statistical techniques.& E" F$ G! }( B/ I+ n5 H1 [
They also illustrate the ideas ofstatistical modelling.
$ ]' B9 E$ i1 j$ gThe reader is assumed to have some familiarity with statistical principles8 p- H: G  J' N  j  G
and methods. In particular, understanding the concepts ofestimation, sampling
0 y; x) K) }+ L, m8 U7 Jdistributions and hypothesis testing is necessary. Experience in the use( {6 \) l' I  I/ D! }
oft-tests, analysis ofv ariance, simple linear regression and chi-squared tests of+ o6 \' g, I& b+ b
independence for two-dimensional contingency tables is assumed. In addition,1 J0 Z! q5 u% M2 u
some knowledge ofmatrix algebra and calculus is required./ j  m5 P# X5 ?) p
The reader will find it necessary to have access to statistical computing/ w5 @2 S. B8 Y' h5 `5 U- K- `
facilities. Many statistical programs, languages or packages can now perform
  e, o* w- v; Lthe analyses discussed in this book. Often, however, they do so with a different
+ w# F  \& K8 jprogram or procedure for each type of analysis so that the unifying structure
4 h% w. f0 e! A9 `  {7 Qis not apparent.
( `5 _' J/ t% R6 BSome programs or languages which have procedures consistent with the# j! }( E3 ^0 n! O) r  t6 v0 y
approach used in this book are: Stata, S-PLUS, Glim, Genstat and SYSTAT.  r) n0 c# R( Y/ A' P; {; v
This list is not comprehensive as appropriate modules are continually5 g, `7 L" @7 u" [' c4 _
being added to other programs.
9 m/ e" I: [8 d" ZIn addition, anyone working through this book may find it helpful to be able" T2 ^$ D' p& i" ]' V/ F3 A
to use mathematical software that can perform matrix algebra, differentiation
* _3 l; f; l, X5 }6 a, N* Y% eand iterative calculations.3 Y& X! c/ K2 ^6 B- K6 F4 Z3 \
1.2 Scope
- a# J7 }) P" XThe statistical methods considered in this' z- G) A4 W9 R; |  q, s

& Q) Q7 N7 u8 P. ev威枝
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