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[分享] Handbook of Missing Data Methodology (2014, Chapman and Hall/CRC )

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sampson2010 发表于 2015-5-29 13:42:33 | 显示全部楼层 |阅读模式

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& x# q! C. k* b1 q1 K2 U4 b1 DTitle: Handbook of Missing Data Methodology' D: j( f0 H- e
Author(s): Fitzmaurice, Garrett M.; Kenward, Michael G.; Molenberghs, Geert; Tsiatis, Anastasios Athanasios; Verbeke, Geert3 L4 T2 m6 m0 ?6 e$ n/ Z3 f9 s: z
Series: Chapman & Hall/CRC handbooks of modern statistical methods
7 a9 @4 ~9 Z" I4 bYear: 2014
! f6 e, y; }0 S) ^( OLanguage: English        * x8 L( r$ m; S% z
Pages: 600
; v" e! {0 S: u. KISBN: 9781439854617, 1439854610, 9781439854624, 1439854629
+ ?& y8 }1 L- {+ `( [+ i) w9 ~Size: 11 MB (11380925 bytes)        " D8 T& w$ {: X7 @- y- r( v
Extension: pdf
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  • Provides a comprehensive and up-to-date summary of many methodological advances and the latest applications of missing data methods in empirical research
  • Describes major developments from the extensive statistical literature on parametric and semi-parametric models with missing data
  • Highlights the importance of sensitivity analysis
  • Explains how to manage missing data in clinical trials and surveys
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3 n  s$ m$ x- z0 C9 _5 ^3 b( cSummary7 t0 s% C- e/ x1 m! C1 W9 ?
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Missing data affect nearly every discipline by complicating the statistical analysis of collected data. But since the 1990s, there have been important developments in the statistical methodology for handling missing data. Written by renowned statisticians in this area, Handbook of Missing Data Methodology presents many methodological advances and the latest applications of missing data methods in empirical research.. N, \. s: }% s* h
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Divided into six parts, the handbook begins by establishing notation and terminology. It reviews the general taxonomy of missing data mechanisms and their implications for analysis and offers a historical perspective on early methods for handling missing data. The following three parts cover various inference paradigms when data are missing, including likelihood and Bayesian methods; semi-parametric methods, with particular emphasis on inverse probability weighting; and multiple imputation methods.6 D8 Y: a. K+ J3 S) x
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The next part of the book focuses on a range of approaches that assess the sensitivity of inferences to alternative, routinely non-verifiable assumptions about the missing data process. The final part discusses special topics, such as missing data in clinical trials and sample surveys as well as approaches to model diagnostics in the missing data setting. In each part, an introduction provides useful background material and an overview to set the stage for subsequent chapters.3 _8 q. K- i. N1 X% k+ a4 Y
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Covering both established and emerging methodologies for missing data, this book sets the scene for future research. It provides the framework for readers to delve into research and practical applications of missing data methods.
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! P4 ?+ ]+ u& s* UTable of Contents
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6 ^/ q1 p8 D" r$ ~7 a; }* C0 x% }Preliminaries - K# E! i) Y1 b: Z
Introduction and Preliminaries Garrett M. Fitzmaurice, Michael G. Kenward, Geert Molenberghs, Geert Verbeke, and Anastasios A. Tsiatis8 M1 X" I, W! Y! l+ X
+ ~2 L' X! s( c) p' @
Developments of Methods and Critique of ad hoc Methods James R. Carpenter and Michael G. Kenward  \/ M) U. D- f  ~) S' f
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Likelihood and Bayesian Methods # t9 d: h$ V6 ?, y
Introduction and Overview Michael G. Kenward, Geert Molenberghs, and Geert Verbeke9 L  j6 s$ X3 Y1 K2 I% L4 u
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Perspective and Historical Overview Michael G. Kenward and Geert Molenberghs( o; c, P6 e' v7 y

) }1 T8 N( Q$ X8 q" K  H9 w# OBayesian Methods Michael J. Daniels and Joseph W. Hogan( N- y3 B( F! ?
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Joint Modeling of Longitudinal and Time-to-Event Data Dimitris Rizopoulos7 b% h3 Y  }8 W( [

- S- `3 `2 f$ \Semi-Parametric Methods
& T0 d  c4 x6 N+ Q9 X4 ^( z4 mIntroduction and Overview Garrett M. Fitzmaurice; _/ w, M/ A6 ]0 S/ \# a

+ d! [+ j/ o, {# f, oMissing Data Methods: A Semi-Parametric Perspective Anastasios A. Tsiatis and Marie Davidian! e4 h' K6 b& P, g, N' E( z# y

* @3 G5 S# ^" W: aDouble-Robust Methods Andrea Rotnitzky and Stijn Vansteelandt4 x' `" i% Y( [

6 d' b7 P/ Z9 }7 |$ S/ o& nPseudo-Likelihood Methods for Incomplete Data Geert Molenberghs and Michael G. Kenward+ A. `5 J9 f! D: ~8 ~5 z& H8 Z" P

& f  @% m5 I% y: o1 a" \" VMultiple Imputation
  s8 P' G5 [9 m2 h0 \6 g6 T" {Introduction Michael G. Kenward
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# _) c! ]8 l3 ^& \. A2 tMultiple Imputation: Perspective and Historical Overview John B. Carlin# d' s3 x2 |7 P8 L, m. Z8 c; h7 J
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Fully Conditional Specification Stef van Buuren6 r/ l9 z# M( D. Z" m- Y' V

5 [7 F  f) E) x( I8 B# C& NMultilevel Multiple Imputation Harvey Goldstein and James R. Carpenter
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. t9 d+ A! B  O2 Q- n( ESensitivity Analysis
, G* ~+ R2 N1 M1 y' }5 oIntroduction and Overview Geert Molenberghs, Geert Verbeke, and Michael G. Kenward
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+ I1 K# O* k0 T" hA Likelihood-Based Perspective Geert Verbeke, Geert Molenberghs, and Michael G. Kenward
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A Semi-Parametric Perspective Stijn Vansteelandt
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Bayesian Sensitivity Analysis Joseph W. Hogan, Michael J. Daniels, and Liangyuan Hu" ^- |# z) F" e$ z/ `2 h

- @9 o" J; n# j3 t0 i5 w" @Sensitivity Analysis with Multiple Imputation James R. Carpenter and Michael G. Kenward5 k) F$ F. [9 x2 c5 s* Q

8 v( d2 X4 I/ N' I9 e  SThe Elicitation and Use of Expert Opinion Ian R. White8 M' J, X: X1 b# z
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Special Topics ; C7 K% J/ g7 l
Introduction and Overview Geert Molenberghs
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: F: h, z! N; X+ }2 B. J& J3 QMissing Data in Clinical Trials Craig Mallinckrodt
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Missing Data in Sample Surveys Thomas R. Belin and Juwon Song
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! a& }$ K( r9 U) ]  n# a( u8 T/ uModel Diagnostics Dimitris Rizopoulos, Geert Molenberghs, and Geert Verbeke
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Index9 y* H  c  s4 T8 h  J# {9 @7 K9 e2 V

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8 N! ^% O$ |9 w$ q Handbook of Missing Data Methodology (2014, Chapman and HallCRC ).part1.rar (6 MB, 下载次数: 59)

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yangyx_006 发表于 2020-7-30 00:49:28 | 显示全部楼层
非常感谢,新用户送了1个钢镚,然后发了新帖子又送了1个钢镚,最后凑齐2个钢镚才把part1和part2下载下来。
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