Missing data occur in many types of studies and typically complicate the analysis. Multiple imputation, either using joint modeling or the more flexible fully conditional specification approach, are popular and work well in standard settings. In settings involving nonlinear associations or interactions, ...
The use of multiple imputation for missing data in uniform DIF analysis: Power and Type I error rates. Applied Measurement in Education, 24(4), 281-301.Finch, W. H. (2011). The use of multiple imputation for missing data in uniform DIF analysis: Power and type I error rates. Applied ...
Data imputation is crucial in data analysis as it addresses missing or incomplete data, ensuring the integrity of analyses. Imputed data enables the use of various statistical methods andmachine learning algorithms, improving model accuracy and predictive power. Without imputation, valuable information may...
Imputation techniques for incomplete data in quadratic discriminant analysisdiscriminant analysisstatistical classificationsingle imputationmultiple imputationWe have compared the efficacy of five imputation algorithms readily available in SAS for the quadratic discriminant function. Here, we have generated several ...
library(mice) imp <- mice(data, m) fit <- with(imp, analysis) pooled <- pool(fit) summary(pooled) data:包含缺失值的矩阵或数据框; imp:一个包含m个插补数据集的列表对象,同时还含有完成插补过程的信息。默认=5; analysis:用来设定应用于m个插补数据集的统计分析方法。如线性回归模型的lm()函数,...
Missing data is a widespread problem in many domains, creating challenges in data analysis and decision making. Traditional techniques for dealing with missing data, such as excluding incomplete records or imputing simple estimates (e.g., mean), are computationally efficient but may introduce bias ...
Using multiple imputation for analysis of incomplete data in clinical research. Sample loss and missing data are inevitable in multivariate and longitudinal research. Ad hoc approaches such as analysis of incomplete data or substitutin... McCleary,Lynn - 《Nursing Research》 被引量: 51发表: 2002年...
Distributed health data networks (DHDNs) leverage data from multiple sources or sites such as electronic health records (EHRs) from multiple healthcare systems and have drawn increasing interests in recent years, as they do not require sharing of subject
(2012). Multiple imputation and analysis with SAS. In J. W. Graham (Ed.), Missing data (pp. 151-190). New York: Springer.Graham JW. Multiple Imputation and Analysis with SPSS 17-20. In: Analysis and Design, editor. Missing Data. Springer: New York; 2012. p. 111-31....
Data envelopment analysis with missing data: an application to university libraries in Taiwan. Journal of the Operational Research Society 51, 897-905.C. Kao, S.T. Liu, Data envelopment analysis with missing data: an application to university libraries in Taiwan, J. Oper. Res. Soc. 51 (...