Statistical methods for handling incomplete data (Record no. 983578)

MARC details
000 -LEADER
fixed length control field 02373nam a22002417a 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240718110750.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 240226b |||||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781032118130
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 519.54
Item number KIM
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Kim, Jae Kwang
9 (RLIN) 14170
245 ## - TITLE STATEMENT
Title Statistical methods for handling incomplete data
250 ## - EDITION STATEMENT
Edition statement 2nd
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Name of publisher, distributor, etc. CRC Press
Place of publication, distribution, etc. London
Date of publication, distribution, etc. 2022
300 ## - PHYSICAL DESCRIPTION
Extent 364 p.
365 ## - TRADE PRICE
Price type code GBP
Price amount 44.99
500 ## - GENERAL NOTE
General note Table of Contents: 1. Introduction 2. Likelihood-based Approach 3. Computation 4. Imputation 5. Multiple Imputation 6. Fractional Imputation 7. Propensity Scoring Approach 8. Nonignorable Missing Data 9. Longitudinal and Clustered Data 10. Application to Survey Sampling 11. Data Integration 12. Advanced Topics
520 ## - SUMMARY, ETC.
Summary, etc. Due to recent theoretical findings and advances in statistical computing, there has been a rapid development of techniques and applications in the area of missing data analysis. Statistical Methods for Handling Incomplete Data covers the most up-to-date statistical theories and computational methods for analyzing incomplete data. 　 Features Uses the mean score equation as a building block for developing the theory for missing data analysis Provides comprehensive coverage of computational techniques for missing data analysis Presents a rigorous treatment of imputation techniques, including multiple imputation fractional imputation Explores the most recent advances of the propensity score method and estimation techniques for nonignorable missing data Describes a survey sampling application Updated with a new chapter on Data Integration Now includes a chapter on Advanced Topics, including kernel ridge regression imputation and neural network model imputation The book is primarily aimed at researchers and graduate students from statistics, and could be used as a reference by applied researchers with a good quantitative background. It includes many real data examples and simulated examples to help readers understand the methodologies. (https://www.routledge.com/Statistical-Methods-for-Handling-Incomplete-Data/Kim-Shao/p/book/9781032118130)
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Missing observations (Statistics)
9 (RLIN) 16614
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Multiple imputation (Statistics)
9 (RLIN) 16615
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Mathematics - Probability & Statistics - General
9 (RLIN) 16616
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Statistical matching
9 (RLIN) 16617
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Book
Source of classification or shelving scheme Dewey Decimal Classification
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Home library Current library Shelving location Date acquired Source of acquisition Cost, normal purchase price Total Checkouts Full call number Barcode Date last seen Copy number Cost, replacement price Price effective from Koha item type
    Dewey Decimal Classification       Bodh Gaya Bodh Gaya General Stacks 26/02/2024 Technical Bureau India Pvt. Ltd. 3155.37   519.54 KIM IIMG-006249 26/02/2024 1 4854.42 26/02/2024 Book

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