返回列表 发帖

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
: @( F2 S. a  sPreface
' w! @9 p- p1 _1 Introduction7 C9 i) z. g& Z1 C
1.1 Background
) }4 G# T; N/ X" Q% M" Y) O+ v1.2 Scope6 t; w; d/ O. a5 q8 M9 ~1 I
1.3 Notation
( @  }0 R3 O9 H$ Z& G1.4 Distributions related to the Normal distribution6 I! \3 Z; v% m+ {9 P
1.5 Quadratic forms
& V2 {" E7 C9 J: k+ J7 S) w1.6 Estimation
  p, ?# l+ A# }7 b4 G# D1.7 Exercises" E4 h# Y. S4 E2 n6 g
2 Model Fitting0 L% z- J, Q/ s$ _
2.1 Introduction
6 j2 D0 y9 p& U) _3 d- P2.2 Examples
1 q. ^: j' L, R& X. O! U7 m2.3 Some principles ofstatistica l modelling
2 w9 \1 V2 f7 t# z2.4 Notation and coding for explanatory variables, l& o: i/ s3 x' ]7 W  p$ b7 i4 _
2.5 Exercises
( l5 c7 E' ~' N9 S3 Exponential Family and Generalized Linear Models/ C+ J) ^2 {. y7 B
3.1 Introduction1 n$ S5 w6 E- j; w3 K, {+ g
3.2 Exponential family of distributions
8 i$ X# U! \( [% @+ u* F3.3 Properties ofdistribution s in the exponential family) y" z% M9 D/ ?+ T: a2 r
3.4 Generalized linear models
9 @5 L$ L6 s5 _& F3.5 Examples+ i# r+ |. S$ L% C0 T, |1 c% `
3.6 Exercises  b3 v, m* G1 Q
4 Estimation
! @3 L9 g7 X3 s" h7 n4.1 Introduction
3 R, ~% o5 z- w4.2 Example: Failure times for pressure vessels
: F- V) a2 m( [, {* p) f; b0 b( r" M+ i7 I4.3 Maximum likelihood estimation; G) l( b1 V7 V$ x0 Q
4.4 Poisson regression example
# ]+ F- r9 b1 p* I  e4.5 Exercises
4 e/ n  N5 X' ^  M  z5 }5 Inference
$ K/ L% o3 M; k. r4 a. Q+ U5.1 Introduction
% A3 P9 L/ l8 d2 S& G$ p' u5.2 Sampling distribution for score statistics
. A1 L3 O; g2 T# _8 E) V8 u? 2002 by Chapman & Hall/CRC1 a2 |$ D. m6 S  q, B. s
5
+ o5 {" c8 o$ }) v" M- o5.3 Taylor series approximations6 g" n7 x( X. {" l6 W; A
5.4 Sampling distribution for maximum likelihood estimators
4 y# {% Y+ R/ o5.5 Log-likelihood ratio statistic) Y+ f  ?6 S: q" {6 H% z
5.6 Sampling distribution for the deviance3 p# x" ]1 ~" x) Z  M
5.7 Hypothesis testing: l* h9 `! N% s# }8 p
5.8 Exercises
& x7 A: H5 Y8 t3 }6 Normal Linear Models
1 u; e2 q2 J/ i: h2 {* z6.1 Introduction
9 s2 n" o3 G7 a( K& z6.2 Basic results& e$ M6 C7 \# o" J1 [9 ]3 h: o9 M
6.3 Multiple linear regression, I# r8 ~8 S- y1 C7 z
6.4 Analysis of variance
/ `% D$ A4 i& P# U6.5 Analysis ofc ovariance# \. u7 F' N$ N: u7 u) f7 [9 E
6.6 General linear models" A/ j5 n: v& z8 f! ?) [
6.7 Exercises1 U0 ~% V* D1 Y. z: X3 U5 i
7 Binary Variables and Logistic Regression
+ ~2 U. _+ w! y4 v/ y" n# ?/ Q7.1 Probability distributions
: ^' I# U* T' A* f& d7.2 Generalized linear models
3 Z2 _& S% B# K4 b" b) T. A  `7.3 Dose response models7 c( G7 K' n2 l1 ~# `. A  E
7.4 General logistic regression model2 Q. B; ~% v, ]' _- q( u
7.5 Goodness offi t statistics
! ]& q) K( T& P, L" {5 Q7.6 Residuals1 W# o/ J6 y8 d+ A
7.7 Other diagnostics
8 m3 k1 x  }: o3 [. i7.8 Example: Senility and WAIS
& Y; z1 w2 z: C7.9 Exercises( A* e0 D& H3 A6 \& h+ b9 Q' ?
8 Nominal and Ordinal Logistic Regression
0 ]  e: S3 Q6 Z6 \) H8.1 Introduction
7 {3 Z: z& j) X8.2 Multinomial distribution- f. H% o, G$ a, S6 i
8.3 Nominal logistic regression6 \; s9 o- K* B
8.4 Ordinal logistic regression
# b7 C7 `) ~2 n* [: F8.5 General comments8 O% L! I, V' }1 P0 m
8.6 Exercises
8 L& ^, w* ]! ~: T6 q" j1 [! J: S; y9 Count Data, Poisson Regression and Log-Linear Models
4 c3 _1 D; M5 N9.1 Introduction$ r4 A- E9 p) V$ V' G0 ^5 j
9.2 Poisson regression
; q6 o5 ~7 R. a9 i. D9.3 Examples ofco ntingency tables8 E) B) K( V3 B6 a# B; e
9.4 Probability models for contingency tables
* b4 r2 T. ~$ u3 A9.5 Log-linear models, S' S* B. W; }
9.6 Inference for log-linear models
8 F/ q+ U5 d( G; j9.7 Numerical examples! b2 F# Z# l% `& F7 N
9.8 Remarks
8 G. Z2 g7 `! d* e3 \4 J* R# F9.9 Exercises
$ G! B2 W* k6 P+ \? 2002 by Chapman & Hall/CRC
" ], }+ N1 v- \. J6 C6, m- U8 ?8 d! ^6 u0 b% z
10 Survival Analysis
, e( X3 ]: ^/ W( K4 A9 d3 J10.1 Introduction# o* y. H1 j- O6 M
10.2 Survivor functions and hazard functions
; I" w- V, [. k% O& K2 B10.3 Empirical survivor function4 A& j; Q! O" b6 h9 q4 k* ~# |6 H. s
10.4 Estimation
% q4 L4 _% \7 r% C9 Q/ j" G, Z/ D10.5 Inference2 D" v  |, a9 N0 _1 C% e
10.6 Model checking
0 p% F6 a+ o5 F( a5 b- b10.7 Example: remission times
  m4 A0 N4 g2 Z10.8 Exercises
7 Z; N7 n8 p- @! G; ~2 _6 f4 V7 ~11 Clustered and Longitudinal Data
) \  _: d. ^, ?; r9 T: d9 Q8 J. M11.1 Introduction
$ ?" T: X: ?( S) j11.2 Example: Recovery from stroke
) {  ?& F5 C9 D+ x8 O' p0 Z; m9 O6 y11.3 Repeated measures models for Normal data
$ D3 z; k( T  p# P11.4 Repeated measures models for non-Normal data7 u+ g0 K' m) \" d- J0 }, \
11.5 Multilevel models0 u3 i/ J- \: ]
11.6 Stroke example continued
0 z1 ?# f; x9 N" X11.7 Comments
2 g  O$ W3 c( a5 K! M( b- h9 ~) C11.8 Exercises
" ~2 f8 j9 l. V; VSoftware
. O3 U; ]1 T' i  M9 e3 AReferences
! R  G9 p1 [- Z+ r? 2002 by Chapman & Hall/CRC2 g/ D& ^% ]0 j! ~1 \. X1 x0 u
7
; m$ J' X  d' h8 \  E5 N  bPreface
% d0 [% I6 R& T: C3 z1 J4 i* gStatistical tools for analyzing data are developing rapidly so that the 1990
/ G+ L: p5 I! U  l. aedition ofthis book is now out ofdate.% y# Q5 e0 U5 _+ y
The original purpose ofthe book was to present a unified theoretical and1 D. u3 v& E9 K& I8 ?% K
conceptual framework for statistical modelling in a way that was accessible) I1 C! ^  K: [" h! J$ e4 P5 b
to undergraduate students and researchers in other fields. This new edition! A/ [& F* g2 ?3 G  }8 x
has been expanded to include nominal (or multinomial) and ordinal logistic: @* A0 d- `, q* a
regression, survival analysis and analysis oflongitudinal and clustered data.! x" I" j8 H) x! U+ ?
Although these topics do not fall strictly within the definition of generalized
( p+ w9 u+ @+ ^( m8 {8 Jlinear models, the underlying principles and methods are very similar and9 w% v# i8 n. [" i$ ]" P- V0 h9 f
their inclusion is consistent with the original purpose ofthe book.
5 K5 X( w* j3 M: [$ WThe new edition relies on numerical methods more than the previous edition% v$ b( X; W9 N
did. Some ofthe calculations can be performed with a spreadsheet while others4 e) y, z# u' s
require statistical software. There is an emphasis on graphical methods for
# ~- s5 B9 `/ I$ v% Vexploratory data analysis, visualizing numerical optimization (for example,/ F, e4 m; G0 d. |( O
ofthe likelihood function) and plotting residuals to check the adequacy of! O' o3 g  G9 Y$ |. S  ^$ P
models.
- N) _9 }# ^+ K5 O* u
! x/ Y4 V) N+ {, J  nIntroduction, [- P4 d* t5 m7 U( f" z
1.1 Background
- Z" Z  E0 |# k" O8 AThis book is designed to introduce the reader to generalized linear models;
/ ^: @. ]& g8 i) N5 e; l3 D' @these provide a unifying framework for many commonly used statistical techniques.
5 Z" H6 q9 B7 C9 k3 {( w, iThey also illustrate the ideas ofstatistical modelling.
2 D5 ]" Y9 R1 fThe reader is assumed to have some familiarity with statistical principles9 e/ o! n) N( n, A% f! `9 I! @3 x
and methods. In particular, understanding the concepts ofestimation, sampling! ~6 i# f; X; }
distributions and hypothesis testing is necessary. Experience in the use
( r: V( y& Y/ m- T% g+ t. Aoft-tests, analysis ofv ariance, simple linear regression and chi-squared tests of5 o1 ?, ~$ o4 V2 |7 ?, r% B
independence for two-dimensional contingency tables is assumed. In addition,' M$ }8 B, e6 W. U3 p
some knowledge ofmatrix algebra and calculus is required.
3 b- p$ ~6 g3 }, M- D6 ]  x9 pThe reader will find it necessary to have access to statistical computing
, r: ^: [# l9 h, Nfacilities. Many statistical programs, languages or packages can now perform2 |' A% q% i; \9 w$ H3 m
the analyses discussed in this book. Often, however, they do so with a different
  a* V! H4 g# u) K5 k# Vprogram or procedure for each type of analysis so that the unifying structure! z9 N+ F! X! z! B5 g
is not apparent.* ~& @* ?3 f( O" n# V7 U# V1 N8 f8 y
Some programs or languages which have procedures consistent with the* Q, G; F* h0 b9 L
approach used in this book are: Stata, S-PLUS, Glim, Genstat and SYSTAT.5 V' h( F8 F% h
This list is not comprehensive as appropriate modules are continually
" Z% V% N& e: m) a( f. n/ qbeing added to other programs.
; N6 e. o& D: qIn addition, anyone working through this book may find it helpful to be able
% v4 V+ q/ l( g+ zto use mathematical software that can perform matrix algebra, differentiation) X1 d* B* o+ _7 a
and iterative calculations.6 s. h* Z: I" m+ K# w
1.2 Scope% [) ?+ W$ ?5 [! W* Q. t0 L
The statistical methods considered in this
0 C7 c& x1 B& e# g1 W$ p) r
" Q/ t7 k3 _  w) M+ S" y  @v威枝6 V( N1 V. j  J. T& l6 e8 Y
+ |! w& n* N* a% z2 z; T
* I; }& f2 X, s1 G( d8 L$ C
8 e) ?$ F/ f6 R
- i# [6 Q" H  g  M4 @
% Y+ k% @1 z3 B! N" k8 k1 M
联系QQ:526781618
0 P! B: [% D0 L: Y8 [1 H( f$ N* j* |5 Q" ]$ d0 N( ~. V. G2 J, e
淘宝旺旺:跟朝流走9 v4 b$ G, F" d5 \$ K" T
8 ?9 ~& H: ~) a. o
有需要的欢迎联系!专业代购电子书
0 r* u3 c& O/ h/ a3 c9 Z2 k& {! t( }! _

1 A7 ?1 O7 Z  T) S) ^: h
* g+ [, E8 {2 Q, o- W" }4 Bebook 英文电子书代购

返回列表