LEADER 05556nam 2200757 a 450 001 9910817472203321 005 20230725030729.0 010 $a1-283-05228-8 010 $a9786613052285 010 $a0-470-90484-4 010 $a0-470-90483-6 035 $a(CKB)2670000000077516 035 $a(EBL)675003 035 $a(OCoLC)714797064 035 $a(SSID)ssj0000518300 035 $a(PQKBManifestationID)12183766 035 $a(PQKBTitleCode)TC0000518300 035 $a(PQKBWorkID)10492926 035 $a(PQKB)10335075 035 $a(MiAaPQ)EBC675003 035 $a(Au-PeEL)EBL675003 035 $a(CaPaEBR)ebr10454756 035 $a(CaONFJC)MIL305228 035 $a(EXLCZ)992670000000077516 100 $a20100517d2011 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aHandbook of statistical data editing and imputation /$fTon de Waal, Jeroen Pannekoek, Sander Scholtus 210 $aHoboken, N.J. $cWiley$dc2011 215 $a1 online resource (xi, 439 pages) $cillustrations 225 1 $aWiley handbooks in survey methodology 300 $aDescription based upon print version of record. 311 1 $a0-470-54280-2 320 $aIncludes bibliographical references and index. 327 $aHandbook of Statistical Data Editing and Imputation; Contents; PREFACE; 1 INTRODUCTION TO STATISTICAL DATA EDITING AND IMPUTATION; 1.1 Introduction; 1.2 Statistical Data Editing and Imputation in the Statistical Process; 1.3 Data, Errors, Missing Data, and Edits; 1.4 Basic Methods for Statistical Data Editing and Imputation; 1.5 An Edit and Imputation Strategy; References; 2 METHODS FOR DEDUCTIVE CORRECTION; 2.1 Introduction; 2.2 Theory and Applications; 2.3 Examples; 2.4 Summary; References; 3 AUTOMATIC EDITING OF CONTINUOUS DATA; 3.1 Introduction 327 $a3.2 Automatic Error Localization of Random Errors 3.3 Aspects of the Fellegi-Holt Paradigm; 3.4 Algorithms Based on the Fellegi-Holt Paradigm; 3.5 Summary; 3.A Appendix: Chernikova's Algorithm; References; 4 AUTOMATIC EDITING: EXTENSIONS TO CATEGORICAL DATA; 4.1 Introduction; 4.2 The Error Localization Problem for Mixed Data; 4.3 The Fellegi-Holt Approach; 4.4 A Branch-and-Bound Algorithm for Automatic Editing of Mixed Data; 4.5 The Nearest-Neighbor Imputation Methodology; References; 5 AUTOMATIC EDITING: EXTENSIONS TO INTEGER DATA; 5.1 Introduction 327 $a5.2 An Illustration of the Error Localization Problem for Integer Data 5.3 Fourier-Motzkin Elimination in Integer Data; 5.4 Error Localization in Categorical, Continuous, and Integer Data; 5.5 A Heuristic Procedure; 5.6 Computational Results; 5.7 Discussion; References; 6 SELECTIVE EDITING; 6.1 Introduction; 6.2 Historical Notes; 6.3 Micro-selection: The Score Function Approach; 6.4 Selection at the Macro-level; 6.5 Interactive Editing; 6.6 Summary and Conclusions; References; 7 IMPUTATION; 7.1 Introduction; 7.2 General Issues in Applying Imputation Methods; 7.3 Regression Imputation 327 $a7.4 Ratio Imputation 7.5 (Group) Mean Imputation; 7.6 Hot Deck Donor Imputation; 7.7 A General Imputation Model; 7.8 Imputation of Longitudinal Data; 7.9 Approaches to Variance Estimation with Imputed Data; 7.10 Fractional Imputation; References; 8 MULTIVARIATE IMPUTATION; 8.1 Introduction; 8.2 Multivariate Imputation Models; 8.3 Maximum Likelihood Estimation in the Presence of Missing Data; 8.4 Example: The Public Libraries; References; 9 IMPUTATION UNDER EDIT CONSTRAINTS; 9.1 Introduction; 9.2 Deductive Imputation; 9.3 The Ratio Hot Deck Method; 9.4 Imputing from a Dirichlet Distribution 327 $a9.5 Imputing from a Singular Normal Distribution 9.6 An Imputation Approach Based on Fourier-Motzkin Elimination; 9.7 A Sequential Regression Approach; 9.8 Calibrated Imputation of Numerical Data Under Linear Edit Restrictions; 9.9 Calibrated Hot Deck Imputation Subject to Edit Restrictions; References; 10 ADJUSTMENT OF IMPUTED DATA; 10.1 Introduction; 10.2 Adjustment of Numerical Variables; 10.3 Adjustment of Mixed Continuous and Categorical Data; References; 11 PRACTICAL APPLICATIONS; 11.1 Introduction; 11.2 Automatic Editing of Environmental Costs 327 $a11.3 The EUREDIT Project: An Evaluation Study 330 $aA practical, one-stop reference on the theory and applications of statistical data editing and imputation techniques Collected survey data are vulnerable to error. In particular, the data collection stage is a potential source of errors and missing values. As a result, the important role of statistical data editing, and the amount of resources involved, has motivated considerable research efforts to enhance the efficiency and effectiveness of this process. 410 0$aWiley handbooks in survey methodology. 606 $aStatistics$xStandards 606 $aData editing 606 $aData integrity 606 $aQuality control 606 $aStatistical services$xEvaluation 615 0$aStatistics$xStandards. 615 0$aData editing. 615 0$aData integrity. 615 0$aQuality control. 615 0$aStatistical services$xEvaluation. 676 $a001.4/22 700 $aWaal$b Ton de$0145008 701 $aPannekoek$b Jeroen$f1951-$01631513 701 $aScholtus$b Sander$f1983-$01631514 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910817472203321 996 $aHandbook of statistical data editing and imputation$93970328 997 $aUNINA