# AN INTRODUCTION TO GENERALIZED LINEAR MODELS SECOND ebook 电子书代购

 Contents, c. ~! N* p; s, `& y4 A( D! T Preface 1 Introduction 1.1 Background 1.2 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 1.5 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 2.1 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 3 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 3.3 Properties ofdistribution s in the exponential family 3.4 Generalized linear models 3.5 Examples/ V1 j1 a1 l8 h) U\$ Z% H2 ~ 3.6 Exercises 4 Estimation 4.1 Introduction 4.2 Example: Failure times for pressure vessels 4.3 Maximum likelihood estimation 4.4 Poisson regression example 4.5 Exercises 5 Inference 5.1 Introduction8 x\$ {# N6 Z, Y: { 5.2 Sampling distribution for score statistics8 x. i2 {- k/ Y! V/ i; f& p( X% w ? 2002 by Chapman & Hall/CRC! m  b7 k0 M' j  G4 ], v 5" M6 A) s+ ~\$ H/ W 5.3 Taylor series approximations 5.4 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 6.2 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 6.5 Analysis ofc ovariance2 |! l( n\$ S' o4 x8 {9 f  a8 ^! e 6.6 General linear models 6.7 Exercises 7 Binary Variables and Logistic Regression' U\$ ]  C( I4 O8 `& r! f& Y 7.1 Probability distributions 7.2 Generalized linear models 7.3 Dose response models 7.4 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 7.9 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 8.2 Multinomial distribution( A- ^2 o( H' r5 d 8.3 Nominal logistic regression 8.4 Ordinal logistic regression 8.5 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 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 6 10 Survival Analysis 10.1 Introduction* f+ _& h- W& ]3 u& v* G1 c 10.2 Survivor functions and hazard functions 10.3 Empirical survivor function 10.4 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 11.1 Introduction 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 11.8 Exercises Software0 [; n, Y! r, ~3 ~. L; C1 N References0 N3 o& k) N& F: i# T. a3 S ? 2002 by Chapman & Hall/CRC 7 Preface% 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. The 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 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. The 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 exploratory 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 models. + s1 D+ q+ x7 e) q) I 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; these provide a unifying framework for many commonly used statistical techniques.& E" F\$ G! }( B/ I+ n5 H1 [ They also illustrate the ideas ofstatistical modelling. The 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 distributions 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 the analyses discussed in this book. Often, however, they do so with a different program or procedure for each type of analysis so that the unifying structure is not apparent. Some 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. In 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 and iterative calculations.3 Y& X! c/ K2 ^6 B- K6 F4 Z3 \ 1.2 Scope The statistical methods considered in this' z- G) A4 W9 R; |  q, s v威枝 0 s9 U  Z/ ^3 L 联系QQ：526781618 # q; m. b: u- m4 G 淘宝旺旺：跟朝流走 . T8 H! H2 x' H3 d" n; J\$ p 有需要的欢迎联系！专业代购电子书 ' G! H( F6 H6 K! g. z , y; E% Y" S( _0 j: K+ s ebook 英文电子书代购