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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
; E1 L$ A- Y" l5 A3 y" T: DPreface9 I! x! l  T2 x6 N. L
1 Introduction; b/ v( o& T7 ?
1.1 Background2 s1 G' H5 {3 K9 c7 j* A4 z
1.2 Scope8 Q# e. I4 _3 b5 i1 P0 {4 ~* c% \$ w
1.3 Notation
5 C2 y  O1 I4 z* y7 w$ @1.4 Distributions related to the Normal distribution+ a9 D: `8 R6 r) Q% N* l0 M0 q
1.5 Quadratic forms; N. O  Y' J' \: [2 T
1.6 Estimation# r0 Q( z3 _6 w( F+ g
1.7 Exercises, ^2 u/ E9 ]6 f* Y& M. ]. A
2 Model Fitting
$ Y/ D0 w2 Y9 W/ l3 Z4 [  E6 H2.1 Introduction1 A* u1 o6 S1 I  m
2.2 Examples+ ~& I6 B$ f! r, K- ^
2.3 Some principles ofstatistica l modelling
, E) d! @3 Z: c8 b% s) Y2.4 Notation and coding for explanatory variables+ Q0 X* M8 T- g# j$ s, |
2.5 Exercises( `0 E9 V9 Z( n/ V; H
3 Exponential Family and Generalized Linear Models
' R. H/ y+ p5 h# _/ _5 B( {# [3.1 Introduction
5 R- \& c9 x7 q' O: c3.2 Exponential family of distributions* X! w" [% K. F0 C( w
3.3 Properties ofdistribution s in the exponential family9 S7 D+ l% b; C- W
3.4 Generalized linear models
  s0 i9 |5 M7 T6 h7 K3.5 Examples
& p' A* {( r1 U3.6 Exercises7 Y% t- ?+ d$ i, i
4 Estimation
+ l% G, }6 X% v/ I% Q4.1 Introduction
; Y0 R1 W/ y# \4.2 Example: Failure times for pressure vessels
" x- d. w$ L5 O  u: Q4.3 Maximum likelihood estimation2 `, q2 K. Z' R/ |) m, F
4.4 Poisson regression example
7 I' o! m) l2 f+ E, ]; q- R5 o& n4.5 Exercises
/ l5 r; z: V4 P5 Inference
% W5 j; R* I6 a- ^  ?; u5 {5.1 Introduction, k6 l4 B% S7 o/ K7 Q) h* z
5.2 Sampling distribution for score statistics
/ U7 `/ T8 X7 D? 2002 by Chapman & Hall/CRC, F4 s" F' Z1 n2 d; v
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( A* ?' m& J3 p5 j) y  ^6 Q5.3 Taylor series approximations
4 W+ ]+ L* `8 a# T$ ]5.4 Sampling distribution for maximum likelihood estimators
3 w, [: f# x8 V, m4 v5.5 Log-likelihood ratio statistic7 a* G& X3 b* h2 ?( e3 N) X
5.6 Sampling distribution for the deviance
5 X* n% U% P+ S& J, v. c# J5.7 Hypothesis testing
- B/ L$ f; w1 d5.8 Exercises
( b4 c! C( T' U8 u( R7 g6 Normal Linear Models
" N  `( `* a7 |; m+ |6 U' l1 S6.1 Introduction
7 m7 Z& {3 o4 K/ p6.2 Basic results
" {4 H5 t1 o8 U6 q8 f6.3 Multiple linear regression6 h: a% J2 D8 u& }7 v
6.4 Analysis of variance% X. L3 j5 s! Y. ?1 g( t/ x* s& A: ~+ i
6.5 Analysis ofc ovariance
) S7 }: r2 U; k* T: i6.6 General linear models
3 B. R% ^3 C# j: `6.7 Exercises9 ?/ _; V) X6 A
7 Binary Variables and Logistic Regression
* @  K+ o, D* a/ b5 T7.1 Probability distributions
) t. I/ V  ?/ E7.2 Generalized linear models
# |4 O4 i1 ^( S" y; S# i" d1 D0 L7.3 Dose response models2 P& E2 t6 F$ e6 d$ R% U
7.4 General logistic regression model
: L# [5 Q7 N# ~3 w" Z7.5 Goodness offi t statistics" t! {% R* O' K) z$ P* N
7.6 Residuals3 m* C. }2 k$ g& k: w3 W7 O/ o5 l
7.7 Other diagnostics8 G  _+ e- j8 `9 t- Q
7.8 Example: Senility and WAIS7 L# P3 W7 b& y& F" z2 U3 I
7.9 Exercises9 u% k& }) v' j) j
8 Nominal and Ordinal Logistic Regression
4 |6 z0 ]% ]. o3 Q8.1 Introduction
9 j7 G' ?0 \. ~7 \8.2 Multinomial distribution0 g! g" T1 @) w/ P; ?4 p
8.3 Nominal logistic regression; m' O2 k# A9 |$ u
8.4 Ordinal logistic regression
+ C# x$ H) l) c3 Q: E8.5 General comments
: ]) @: N3 h) M* ?8.6 Exercises
% m5 Y- i; Y* K' c; y5 l0 l9 Count Data, Poisson Regression and Log-Linear Models" V# ~! o$ l# N/ q( w' o1 z
9.1 Introduction$ v7 P: o3 e: A% V8 s. f% n' b
9.2 Poisson regression0 B: ]* y% d) m
9.3 Examples ofco ntingency tables: }* ?2 l1 Y* {, A( K4 h, ?
9.4 Probability models for contingency tables# T8 P9 x6 ]+ J! h0 V. S  y: T0 }. J6 l
9.5 Log-linear models
; F9 f" Z3 ?7 T; b2 U9.6 Inference for log-linear models
$ Q7 m* j7 N6 d, s9.7 Numerical examples
7 g7 i) m4 q2 A9.8 Remarks
1 M; W" e% Y$ O; S9.9 Exercises& Y+ J% y! w% [4 p& y# {- I" M: T/ I
? 2002 by Chapman & Hall/CRC
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10 Survival Analysis
0 e7 u. ?; i1 U4 k8 e10.1 Introduction
9 a( H2 w/ j, B3 A- ?10.2 Survivor functions and hazard functions, {1 E7 a& a8 ^2 [+ W
10.3 Empirical survivor function3 j; x; ~% h4 k! B4 ^
10.4 Estimation! f- f0 R4 U0 b' F& d) x4 s
10.5 Inference
8 K" M6 b6 f) G3 c, P% {0 F$ G10.6 Model checking
% u7 r1 \6 U3 R& q/ d- n10.7 Example: remission times2 O$ W: O- N$ P: N: Q
10.8 Exercises
: \2 d8 L' H. S! G  G11 Clustered and Longitudinal Data
" i) g. e% i5 z11.1 Introduction
$ r% ~3 G' j' O5 N# F) h0 {11.2 Example: Recovery from stroke6 Q( V. j# K4 R# N  n
11.3 Repeated measures models for Normal data
$ w' i7 V3 m2 K9 l7 s11.4 Repeated measures models for non-Normal data( J6 Q0 e4 o( K. l
11.5 Multilevel models) G/ ^6 Y+ D. _# O8 ^4 u
11.6 Stroke example continued
5 B0 M1 k9 J2 S# s/ C  @. r& K11.7 Comments2 ~0 x! ~0 ?- t6 T7 U8 f
11.8 Exercises" S. \- T& o: o% j' Z
Software
4 O. q8 ~. p/ c- p- C, t5 YReferences
4 W; C: t* S: O! a0 q; P2 k) w- a- m1 L? 2002 by Chapman & Hall/CRC
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Preface
! m4 U2 `) v  Q8 fStatistical tools for analyzing data are developing rapidly so that the 1990
' x/ s( ]. z; H* _* \edition ofthis book is now out ofdate.$ o9 Z) n7 M* X3 [$ S- X' e5 G
The original purpose ofthe book was to present a unified theoretical and
4 l& R) s5 ^6 v& g; L4 u, K) qconceptual framework for statistical modelling in a way that was accessible
* }' b) L1 D" j9 r: T9 kto undergraduate students and researchers in other fields. This new edition
- ^$ P. B8 z& f( D9 _& ]* lhas been expanded to include nominal (or multinomial) and ordinal logistic1 B% s$ `- [  K  m
regression, survival analysis and analysis oflongitudinal and clustered data.% q# {6 ~2 G5 S" Q! }
Although these topics do not fall strictly within the definition of generalized
+ l' K5 y* j, x6 \( |linear models, the underlying principles and methods are very similar and
  s1 _' M/ D* U0 I3 \) G& {$ }their inclusion is consistent with the original purpose ofthe book.3 }  ~+ _! [1 P* g; i4 W% |. b) C
The new edition relies on numerical methods more than the previous edition
4 d5 u5 B1 p% ^did. Some ofthe calculations can be performed with a spreadsheet while others: f' _# N$ ~/ U6 d* n% A
require statistical software. There is an emphasis on graphical methods for- f) o' K4 q8 z! h( J
exploratory data analysis, visualizing numerical optimization (for example,
+ j2 s' z- W# r5 B* pofthe likelihood function) and plotting residuals to check the adequacy of
! z1 C3 S) a* a% r9 Fmodels.) |0 Q" ?. d9 F2 Q! ^4 A
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Introduction
3 M/ g$ e3 s7 n8 S. ]1.1 Background( i: A1 {' `% f4 k: `9 @" V0 G
This book is designed to introduce the reader to generalized linear models;  _* q9 E# g3 I' g' W
these provide a unifying framework for many commonly used statistical techniques.2 `5 U+ e6 L& I+ L: O
They also illustrate the ideas ofstatistical modelling.
3 W2 L3 O$ U7 Y) s  m( a2 Q6 @/ IThe reader is assumed to have some familiarity with statistical principles: i! X3 X  K6 B/ W# I3 {
and methods. In particular, understanding the concepts ofestimation, sampling6 Z' b( U* \* n& ^+ s) z+ n
distributions and hypothesis testing is necessary. Experience in the use
/ [0 X4 o7 v, i  B8 yoft-tests, analysis ofv ariance, simple linear regression and chi-squared tests of% @2 C- F( G! [  k
independence for two-dimensional contingency tables is assumed. In addition,2 m) l8 O3 Z- ?( X, }5 G' m* p
some knowledge ofmatrix algebra and calculus is required.
& f% v# {' |' T" nThe reader will find it necessary to have access to statistical computing
  B+ q" |& b7 b& R9 R6 Efacilities. Many statistical programs, languages or packages can now perform3 g3 a, A3 j$ s6 U
the analyses discussed in this book. Often, however, they do so with a different
  A) X6 c) o* ]) ^program or procedure for each type of analysis so that the unifying structure
) q$ }6 @1 \, I7 Z. cis not apparent.
: z. X- p: u- @/ O, g. y( T6 XSome programs or languages which have procedures consistent with the
1 q/ g5 U6 I& @$ v7 Napproach used in this book are: Stata, S-PLUS, Glim, Genstat and SYSTAT.6 s; A# J  A/ A& {' A
This list is not comprehensive as appropriate modules are continually
0 }( T8 r+ N$ Z% L- A: q* b$ Y& ]being added to other programs./ I0 X* ]' S7 l% b3 X- y
In addition, anyone working through this book may find it helpful to be able
# o$ u8 j/ O: ito use mathematical software that can perform matrix algebra, differentiation$ a8 j& E1 R; k# Z
and iterative calculations.
2 I1 S3 }3 y' M1.2 Scope- x' ~% C% L9 y, }
The statistical methods considered in this3 o, @  O. w/ h+ A7 U8 o+ d
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v威枝
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