Cantitate/Preț
Produs
Total produse
Vezi coșul
Statistical Methods for Handling Incomplete Data de Jae Kwang Kim

Statistical Methods for Handling Incomplete Data

Autor Jae Kwang Kim, Jun Shao
en Limba Engleză Paperback – 29 ian 2024
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.
Citește tot Restrânge

Toate formatele și edițiile

Toate formatele și edițiile Preț Express
Paperback (1) 35769 lei  3-5 săpt. +2650 lei  5-11 zile
  CRC Press – 29 ian 2024 35769 lei  3-5 săpt. +2650 lei  5-11 zile
Hardback (1) 77342 lei  6-8 săpt.
  CRC Press – 19 noi 2021 77342 lei  6-8 săpt.

Preț: 35769 lei

Nou

Puncte Express: 537
Preț estimativ în valută:
6845€ • 7111$ • 5686£

Carte disponibilă

Livrare economică 13-27 ianuarie 25
Livrare express 28 decembrie 24 - 03 ianuarie 25 pentru 3649 lei

 Adaugă în coș

Wish list
Gift list
Am citit!
Adaugă în listă
Preluare comenzi: 021 569.72.76

Specificații

ISBN-13: 9781032118130
ISBN-10: 103211813X
Pagini: 380
Ilustrații: 12
Dimensiuni: 156 x 234 x 29 mm
Greutate: 0.55 kg
Ediția:2
Editura: CRC Press
Colecția Chapman and Hall/CRC

Cuprins

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

Notă biografică

Jae Kwang Kim is a LAS dean’s professor in the Department of Statistics at Iowa State University. He is a fellow of American Statistical Association (ASA) and Institute of Mathematical Statistics (IMS). He is the recipient of 2015 Gertude M. Cox award, sponsored by Washington Statistical Society and RTI international.
Jun Shao is a professor in the Department of Statistics at University of Wisconsin – Madison. He is a fellow of ASA and IMS, a former president of International Chinese Statistical Association and currently the founding editor of Statistical Theory and Related Fields.

Recenzii

"As a general comment, I must say that it is probably one of the most extensive, detailed and complete sources of information on the most up-to-date methods to deal with missing data, from simple imputation methods to more complex analysis techniques that take missingness into account. The book is well organized in 12 chapters that although could be read independently based on the readers needs/interest, it does have a hierarchy that makes sense going from more simple early chapters to more complex subjects later in the book."
~David Manteigas, ISCB Book Reviews

Descriere

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. This book covers the most up-to-date statistical theories and computational methods for analyzing incomplete data.