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Product Identifiers
PublisherSpringer New York
ISBN-10038795516X
ISBN-139780387955162
eBay Product ID (ePID)15038314348
Product Key Features
Number of PagesXviii, 264 Pages
Publication NameStatistical Matching : A Frequentist Theory, Practical Applications, and Alternative Bayesian Approaches
LanguageEnglish
SubjectProbability & Statistics / Multivariate Analysis, Probability & Statistics / General
Publication Year2002
TypeTextbook
Subject AreaMathematics
AuthorSusanne Rassler
SeriesLecture Notes in Statistics Ser.
FormatTrade Paperback
Dimensions
Item Weight29.3 Oz
Item Length9.3 in
Item Width6.1 in
Additional Product Features
Intended AudienceScholarly & Professional
LCCN2002-070554
Reviews"Statistical matching is one of the methods for the imputation of missing results from survey sampling...This book is totally focused on methods, applications, and results. It devotes little space to the presentation and derivation of various theorems." Technometrics, May 2004, "Statistical matching is one of the methods for the imputation of missing results from survey sampling…This book is totally focused on methods, applications, and results. It devotes little space to the presentation and derivation of various theorems." Technometrics, May 2004
Dewey Edition21
Series Volume Number168
Number of Volumes1 vol.
IllustratedYes
Dewey Decimal519.5
Table Of Content1.1 Statistical Matching -- Problems and Perspectives.- 1.2 Record Linkage Versu s Statistical Matching.- 1.3 Statistical Matching as Nonresponse Phenomenon.- 1.4 Identification Problems Inherent in Statistical Matching.- 1.5 Outline of th e Book.- 1.6 Bibliographic and Software Notes.- Frequentist Theory of Statistical Matching.- 2.1 Introduction and Chapters Outline.- 2.2 The Matching Process.- 2.3 Properties of the Matching Process.- 2.4 Matching by Propensity Scores.- 2.5 Obj ectives of Statisti cal Matching.- 2.6 Some Illustrations.- 2.7 Concluding Remarks.- Practical Applications of Statistical Matching.- 3.1 Introduction and Chapters Outline.- 3.2 History of Statistical Matching Techniques.- 3.3 Overview of Traditional Approaches.- Alternative Approaches to Statistical Matching.- 4.1 Introduction and Chapters Outline.- 4.2 Some Basic Notation.- 4.3 Multiple Imputation Inference.- 4.4 Regression Imputation with Random Residuals.- 4.5 Noniterative Multivariate Imputation Procedure.- 4.6 Data Augmentation.- 4.7 Iterative Univariate Imputations by Chained Equ ations.- 4.8 Simulation Study -- Multivariate Normal Data.- 4.9 Concluding Remarks.- Empirical Evaluation of Alternative Approaches.- 5.1 Introduction and Chapters Outline.- 5.2 Simulation Study Using Survey Data.- 5.3 Simulation Study Using Generated Data.- 5.4 Design of the Evaluation Study.- 5.5 Results Due to Alternative Approaches.- 5.6 Concluding Remarks.- Synopsis and Outlook.- 6.1 Synopsis.- 6.2 Outlook.- Some Technicalities.- Multivariate Normal Model Completely Observed.- Normally Distributed Data Not Jointly Observed.- Basic S-PLUS Routines.- EVALprio.- EVALd.- NIBAS.- Tables.- References.
SynopsisStatistically matching of separate survey samples - can this be efficient? When there is no single source file available about all the information of interest, techniques of matching different data sets are often applied. Then individual respondents on one survey are matched to those on another based on some common characteristics. The respondents in the resulting data set will have all the answers to all the questions in both original surveys. For example, government policy questions as well as media planning tasks may be answered by means of such a statistically matched data set. This book covers a wide range of different aspects concerning statistical matching that in Europe typically is called data fusion. A theoretical framework is derived to determine the advantages and disadvantages of statistical matching. Its history and practical applications are discussed, and alternative approaches are proposed and evaluated with real world marketing data. Answers to the question of efficiency are provided. A book about statistical matching will be of interest to researchers and practitioners in many data analysis areas, starting with data collection and the production of public use micro files, data banks, and data bases. People in the areas of database marketing, public health analysis, socioeconomic modeling, and also official statistics also will find it useful. Susanne R ssler is senior research assistant and lecturer at the Institute of Statistics and Econometrics at the University of Erlangen-N rnberg in Germany. She received her Ph.D. in 1995, having written a book about survey sampling theory with the focus on sampling with unequal probabilities. Later she started research about statistical matching. This book is the result of her "habilitation" thesis according to German academic tradition., Statistically matching of separate survey samples - can this be efficient? When there is no single source file available about all the information of interest, techniques of matching different data sets are often applied. Then individual respondents on one survey are matched to those on another based on some common characteristics. The respondents in the resulting data set will have all the answers to all the questions in both original surveys. For example, government policy questions as well as media planning tasks may be answered by means of such a statistically matched data set.This book covers a wide range of different aspects concerning statistical matching that in Europe typically is called data fusion. A theoretical framework is derived to determine the advantages and disadvantages of statistical matching. Its history and practical applications are discussed, and alternative approaches are proposed and evaluated with real world marketing data. Answers to the question of efficiency are provided.A book about statistical matching will be of interest to researchers and practitioners in many data analysis areas, starting with data collection and the production of public use micro files, data banks, and data bases. People in the areas of database marketing, public health analysis, socioeconomic modeling, and also official statistics also will find it useful.Susanne Rässler is senior research assistant and lecturer at the Institute of Statistics and Econometrics at the University of Erlangen-Nürnberg in Germany. She received her Ph.D. in 1995, having written a book about survey sampling theory with the focus on sampling with unequal probabilities. Later she started research about statistical matching. This book is the result of her "habilitation" thesis according to German academic tradition., Data fusion or statistical file matching techniques merge data sets from different survey samples to solve the problem that exists when no single file contains all the variables of interest. Media agencies are merging television and purchasing data, statistical offices match tax information with income surveys. Many traditional applications are known but information about these procedures is often difficult to achieve. The author proposes the use of multiple imputation (MI) techniques using informative prior distributions to overcome the conditional independence assumption. By means of MI sensitivity of the unconditional association of the variables not jointy observed can be displayed. An application of the alternative approaches with real world data concludes the book., Government policy questions and media planning tasks may be answered by this data set. It covers a wide range of different aspects of statistical matching that in Europe typically is called data fusion. A book about statistical matching will be of interest to researchers and practitioners, starting with data collection and the production of public use micro files, data banks, and data bases. People in the areas of database marketing, public health analysis, socioeconomic modeling, and official statistics will find it useful.
LC Classification NumberQA276-280
