Titlebook: Applied Multiple Imputation; Advantages, Pitfalls Kristian Kleinke,Jost Reinecke,Martin Spiess Textbook 2020 Springer Nature Switzerland AG

controllers

書目名稱Applied Multiple Imputation影響因子(影響力)




書目名稱Applied Multiple Imputation影響因子(影響力)學(xué)科排名




書目名稱Applied Multiple Imputation網(wǎng)絡(luò)公開度




書目名稱Applied Multiple Imputation網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Applied Multiple Imputation被引頻次




書目名稱Applied Multiple Imputation被引頻次學(xué)科排名




書目名稱Applied Multiple Imputation年度引用




書目名稱Applied Multiple Imputation年度引用學(xué)科排名




書目名稱Applied Multiple Imputation讀者反饋




書目名稱Applied Multiple Imputation讀者反饋學(xué)科排名

hypnogram

2199-7357 ls in R with supplementary R code and data sets.Discusses thThis book explores missing data techniques and provides a detailed and easy-to-read introduction to multiple imputation, covering the theoretical aspects of the topic and offering hands-on help with the implementation. It discusses the pros

RADE

Untersuchungsdesign und Methodik,d with simple examples. In addition, conditions are given for analysing data sets without the need to explicitly model the missing data mechanism (“ignorability”). We also review diagnostic tools for incomplete data sets, both descriptive and based on a statistical test.

碌碌之人

Untersuchungsdesign und Methodik,ve practical advice which procedure might be suited best in a given scenario because valid inferences in applied research can only be expected based on informed decisions. A conclusion of this chapter will be that there is not the one method or technique that works best under every possible scenario.

limber

Untersuchungsdesign und Methodik,modeling approach, where only univariate marginal models are used to generate imputations. Additional topics are rounding, how to deal with restrictions and how to treat interaction or higher polynomial terms.

FLAGR

Missing Data Mechanism and Ignorability,d with simple examples. In addition, conditions are given for analysing data sets without the need to explicitly model the missing data mechanism (“ignorability”). We also review diagnostic tools for incomplete data sets, both descriptive and based on a statistical test.

legitimate

AVID

Multiple Imputation: Theory,modeling approach, where only univariate marginal models are used to generate imputations. Additional topics are rounding, how to deal with restrictions and how to treat interaction or higher polynomial terms.

蒼白

沙漠