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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

Contents5 w2 j: _4 S7 u
Preface$ ^: T  `) V0 S
1 Introduction/ m  A" ]4 @! P2 V/ Y! V( v
1.1 Background; _- I9 R# H  x  z7 {  J0 e# o$ z
1.2 Scope
( g% E, F" B0 }2 X. X# C6 d1.3 Notation3 T, G$ ]) N. T  x  p: I
1.4 Distributions related to the Normal distribution
2 n, ~4 ~- v$ y* E. o1 M" T1.5 Quadratic forms
7 S' w7 g5 r: q) E& I7 k1.6 Estimation
5 X9 O0 ~6 Z) X! v: ~. a  U1.7 Exercises4 c9 {- f4 z( I' w1 O) p
2 Model Fitting
( P! \& s( t! O" i. q* o2.1 Introduction7 {" J/ _3 n& p
2.2 Examples0 [: V% c5 _/ H$ E
2.3 Some principles ofstatistica l modelling
4 d) K  g8 l' v: u2.4 Notation and coding for explanatory variables
- T' y# i8 A& c/ I2.5 Exercises0 x0 f- |0 U* q
3 Exponential Family and Generalized Linear Models" d$ I2 w2 Q! q; n
3.1 Introduction
, M5 [, D: W* j9 q" h, z3.2 Exponential family of distributions
& |' |3 D- J% ~  \9 J6 I3.3 Properties ofdistribution s in the exponential family
; D, [5 B. y0 [2 S: O9 X3.4 Generalized linear models
: P7 E( e% X) p3.5 Examples" x' K0 i. k* \8 F" {
3.6 Exercises7 g9 l3 {+ G! @2 L) u- o. l
4 Estimation# q  R3 K8 b) O$ ]3 O
4.1 Introduction
# T! l0 a2 R3 P+ ]% h9 s4.2 Example: Failure times for pressure vessels& N* u$ W* T  Q4 J# u/ h# t8 @
4.3 Maximum likelihood estimation
5 N# j( Z3 A+ p% O1 y7 ~0 ?4.4 Poisson regression example; N( L4 c, q2 R- c( s  I
4.5 Exercises
0 n0 Q2 W' N+ F5 Inference1 P7 |- E8 ?+ a. K9 e- _
5.1 Introduction
9 Y4 z* e! w# b5 p9 P2 p( a" N5.2 Sampling distribution for score statistics
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+ j' i. J0 s% D8 h/ a( L5.3 Taylor series approximations
1 P! A9 R3 g7 ]5.4 Sampling distribution for maximum likelihood estimators
8 z, Q0 t' _) L+ g% W: ]4 H5.5 Log-likelihood ratio statistic3 t9 e6 s# F' r  ~( l
5.6 Sampling distribution for the deviance  }3 ~# l: G0 `0 B/ ^; w* L
5.7 Hypothesis testing3 l+ w! S/ N8 b5 O6 w% b/ b% |  d
5.8 Exercises& K2 h% P3 T' L' S$ K2 ^
6 Normal Linear Models& H1 w' R# h) t! M" \
6.1 Introduction
$ m6 T' ~$ l$ W1 m6.2 Basic results
8 d: M# B4 G/ C5 F6.3 Multiple linear regression
5 m; Q8 J, ~" F# v  w6.4 Analysis of variance6 C  h4 @, B: a8 d0 h( S
6.5 Analysis ofc ovariance. Y* ?( K( g" X* P2 c$ N( ^9 M
6.6 General linear models9 B( x4 w; _; T5 O. \
6.7 Exercises- J; ], B+ J: `" t
7 Binary Variables and Logistic Regression
" m/ B# w& s# {4 k1 O7.1 Probability distributions: z* e7 ~- L" k1 [0 k
7.2 Generalized linear models
. {- m! A4 X2 x7.3 Dose response models
2 {; s+ h5 y$ j2 J8 r7.4 General logistic regression model
) f9 i# r: \- e) I- N7.5 Goodness offi t statistics
6 Z, D% x3 \. L+ \8 e7.6 Residuals
$ J% I" O' n; }& ]) a* t* N7.7 Other diagnostics
  f; E. e4 n* v' [" C1 a7.8 Example: Senility and WAIS- l- E1 D+ u  w* q4 A
7.9 Exercises* w% ?; x9 S8 e5 z
8 Nominal and Ordinal Logistic Regression! a4 R; A6 x2 X; u: \% E
8.1 Introduction' Y0 Y$ S% K7 ^% _4 t' E2 ~
8.2 Multinomial distribution; A. f- W, n: B8 z3 O. ]0 F
8.3 Nominal logistic regression
' x4 @) k: E+ A) T8.4 Ordinal logistic regression1 ]/ c$ ~& o$ r3 v3 H7 A
8.5 General comments
4 `/ h* o/ U- ?& ^8 q9 e* C* y8.6 Exercises- w1 p2 A9 \6 I& }
9 Count Data, Poisson Regression and Log-Linear Models7 I# q% F# |4 V$ o( s; v4 m8 j
9.1 Introduction! y2 d" j: Q  L  I" u' P0 e
9.2 Poisson regression/ j8 L4 B# ^: w) \- W7 C
9.3 Examples ofco ntingency tables( _- \& b; @! a  K* l
9.4 Probability models for contingency tables
  Y: [0 f7 p9 ~% {4 O7 X9.5 Log-linear models6 i! k; ^) @% Z! A5 }# o
9.6 Inference for log-linear models
: x# V' r; P) s! }# R. H9.7 Numerical examples
1 Y7 ^! x" [0 E  y! H+ o$ {9.8 Remarks; ~* Y3 G# w, l# x; I
9.9 Exercises
3 O8 [& x# y! k  b/ T8 e? 2002 by Chapman & Hall/CRC
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: s+ A: r8 a5 A* S# p10 Survival Analysis$ k3 P8 L6 k1 I0 m& m0 ?) \
10.1 Introduction% c; A+ i5 r9 O9 Z7 g
10.2 Survivor functions and hazard functions
- {) X, M0 _" a' y9 K10.3 Empirical survivor function
- d0 N5 U9 J8 v5 t- M. `0 {( j10.4 Estimation
8 H( j1 y, V+ V6 P: L; s3 V" V$ C( y1 z10.5 Inference6 S( e9 x+ B* O1 o
10.6 Model checking
* ]- S, U$ q# W' e- \10.7 Example: remission times
9 |$ |; S7 {' k( v7 a+ q; t) A10.8 Exercises  B$ _% ^* o; U4 X: L0 ~
11 Clustered and Longitudinal Data4 c) K3 S( `' y
11.1 Introduction
' S4 y) }3 c8 M( B11.2 Example: Recovery from stroke4 a% e4 L% s1 u+ [/ p
11.3 Repeated measures models for Normal data
! y. R. {1 p; ]  [11.4 Repeated measures models for non-Normal data/ C( ^2 V( w7 V! ~$ E5 o  x1 \
11.5 Multilevel models
7 _& A2 A# F6 S6 U0 P  C11.6 Stroke example continued4 w1 `8 ]# A! x9 W# Q
11.7 Comments
: l; L/ x8 \6 O% u9 ~6 Q& a$ D0 a4 l11.8 Exercises
8 h- c2 p3 G! g% ~& kSoftware+ p+ \8 W8 l' v- p
References5 S, x% n" a1 P
? 2002 by Chapman & Hall/CRC; C) E) x& D1 ^7 _& g
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Preface3 B5 Y) t# n+ y. K- I
Statistical tools for analyzing data are developing rapidly so that the 1990/ G% N" M7 x' a, ^; l' v+ D; A
edition ofthis book is now out ofdate.' A/ D/ q$ m  t/ r, t6 v, n
The original purpose ofthe book was to present a unified theoretical and
9 g6 P% W. \2 }" J& N8 I6 k& \6 yconceptual framework for statistical modelling in a way that was accessible( r. b9 j+ J2 q9 u3 U$ t: k  n
to undergraduate students and researchers in other fields. This new edition
, C9 T4 q5 j7 Shas been expanded to include nominal (or multinomial) and ordinal logistic/ x) {4 ?% _  o. Q# I; \  O+ q
regression, survival analysis and analysis oflongitudinal and clustered data.
: [$ Y7 Q- r) QAlthough these topics do not fall strictly within the definition of generalized' ~4 f. J% x6 A5 p: n1 \5 E* C
linear models, the underlying principles and methods are very similar and; q. A  N! G# \
their inclusion is consistent with the original purpose ofthe book.
, `9 h$ h  A; `( x! J3 l( \5 KThe new edition relies on numerical methods more than the previous edition
0 D5 y1 B) I& D! i( odid. Some ofthe calculations can be performed with a spreadsheet while others
# j5 O- k9 D, {, D6 c$ B9 h/ D  Nrequire statistical software. There is an emphasis on graphical methods for
/ j) W# {- Y9 O2 O% rexploratory data analysis, visualizing numerical optimization (for example,  c1 G  z9 }" y# W
ofthe likelihood function) and plotting residuals to check the adequacy of
6 I( b8 e( W1 [) v( C4 dmodels.7 b2 A0 m; p# |% F; D% u: K! r
+ I' z: O2 S" b- c9 ~5 \
Introduction/ J$ F7 J/ a- s  ^% {( i' z0 z
1.1 Background/ q. J( j) o; i) S8 D
This book is designed to introduce the reader to generalized linear models;
( _. z4 {* U" ?these provide a unifying framework for many commonly used statistical techniques.
1 Y1 Y8 V% N7 p9 M$ WThey also illustrate the ideas ofstatistical modelling.
- [( H7 _0 f4 g- I/ t8 vThe reader is assumed to have some familiarity with statistical principles' V7 H9 m7 E  @0 N6 c& p/ s% s
and methods. In particular, understanding the concepts ofestimation, sampling) |3 r1 @0 U3 v  ]6 D. J
distributions and hypothesis testing is necessary. Experience in the use
+ |- J5 y! U9 `) [) I# i; qoft-tests, analysis ofv ariance, simple linear regression and chi-squared tests of
' ~; |* U7 O5 g/ oindependence for two-dimensional contingency tables is assumed. In addition,6 p+ j7 k0 ?# c( \7 d2 e! l
some knowledge ofmatrix algebra and calculus is required.. M2 e9 d$ D; v5 p1 C
The reader will find it necessary to have access to statistical computing9 B2 c  O: a& ?. K, ?; g1 h
facilities. Many statistical programs, languages or packages can now perform
; b& S1 n" S1 \4 J( E4 _, p  qthe analyses discussed in this book. Often, however, they do so with a different
$ N" P# d- M0 i/ sprogram or procedure for each type of analysis so that the unifying structure" z: ~% B8 q/ ?* f1 a
is not apparent.
- I( A8 K9 g% NSome programs or languages which have procedures consistent with the
) Z) T+ A  Q* l9 A, }1 \% Yapproach used in this book are: Stata, S-PLUS, Glim, Genstat and SYSTAT.; s7 U" u: h" Q; J0 z/ Z; y  i
This list is not comprehensive as appropriate modules are continually
# \' k4 G7 o( ]being added to other programs., [/ X! }0 O+ _# U9 |
In addition, anyone working through this book may find it helpful to be able6 c2 ?  I4 Q! [2 b
to use mathematical software that can perform matrix algebra, differentiation, k; H- B; U5 ?. O
and iterative calculations.
2 {# d& G1 a% x( O. e: \1.2 Scope7 o- I3 s9 x! n2 G8 R$ ~
The statistical methods considered in this
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