Amelia II is a complete R package for multiple imputation of missing data. The package implements a new expectation-maximization with bootstrapping algorithm that works faster, with larger numbers of variables, and is far easier to use, than various Markov chain Monte Carlo approaches, but gives essentially the same answers. The program also improves imputation models by allowing researchers to put Bayesian priors on individual cell values, thereby including a great deal of potentially valuable and extensive information. It also includes features to accurately impute cross-sectional datasets, individual time series, or sets of time series for different cross-sections. A full set of graphical diagnostics are also available. The program is ea...
Multiple imputation (MI) is increasingly used for handling missing data in medical research. The sta...
Dealing with missing data poses a challenge as the quality of data is a significant element when app...
Missing data are a common occurrence in real datasets. For epidemiological and prognostic factors st...
Amelia II is a complete R package for multiple imputation of missing data. The package implements a ...
Owing to its practicality as well as strong inferential properties, multiple imputation has been inc...
Our mi package in R has several features that allow the user to get inside the imputation process an...
In real-life situations, we often encounter data sets containing missing observations. Statistical m...
This paper introduces software packages for efficiently imputing missing data using deep learning me...
Missing data continues to be an issue not only the field of statistics but in any field, that deals ...
Following the seminal publications of Rubin about thirty years ago, statisticians have become increa...
Missing data are an important practical problem in many applications of statistics, including social...
Epidemiologic studies are frequently susceptible to missing information. Omitting observations with ...
Missing data is a common occurrence in clinical research. Missing data occurs when the value of the ...
Multiple imputation (MI) is a commonly applied method of statistically handling missing data. It inv...
Missing data is one of the challenges we are facing today in modeling valid statistical models. It r...
Multiple imputation (MI) is increasingly used for handling missing data in medical research. The sta...
Dealing with missing data poses a challenge as the quality of data is a significant element when app...
Missing data are a common occurrence in real datasets. For epidemiological and prognostic factors st...
Amelia II is a complete R package for multiple imputation of missing data. The package implements a ...
Owing to its practicality as well as strong inferential properties, multiple imputation has been inc...
Our mi package in R has several features that allow the user to get inside the imputation process an...
In real-life situations, we often encounter data sets containing missing observations. Statistical m...
This paper introduces software packages for efficiently imputing missing data using deep learning me...
Missing data continues to be an issue not only the field of statistics but in any field, that deals ...
Following the seminal publications of Rubin about thirty years ago, statisticians have become increa...
Missing data are an important practical problem in many applications of statistics, including social...
Epidemiologic studies are frequently susceptible to missing information. Omitting observations with ...
Missing data is a common occurrence in clinical research. Missing data occurs when the value of the ...
Multiple imputation (MI) is a commonly applied method of statistically handling missing data. It inv...
Missing data is one of the challenges we are facing today in modeling valid statistical models. It r...
Multiple imputation (MI) is increasingly used for handling missing data in medical research. The sta...
Dealing with missing data poses a challenge as the quality of data is a significant element when app...
Missing data are a common occurrence in real datasets. For epidemiological and prognostic factors st...