Handling Incomplete Data with Regression Imputation
نویسندگان
چکیده
منابع مشابه
Handling Incomplete High Dimensional Multivariate Longitudinal Data by Multiple Imputation Using a Longitudinal Factor Analysis Model
1. Introduction Longitudinal data sets often suffer from missing values. Because of the large number of variables in these data sets, even a small rate of missingness on some variables can result in a large number of incomplete cases. Multiple imputation (Rubin, 1996, Rubin and Schenker, 1986) is often used to handle missing data problems. When producing multiple imputations for the missing val...
متن کاملDual imputation model for incomplete longitudinal data.
Missing values are a practical issue in the analysis of longitudinal data. Multiple imputation (MI) is a well-known likelihood-based method that has optimal properties in terms of efficiency and consistency if the imputation model is correctly specified. Doubly robust (DR) weighing-based methods protect against misspecification bias if one of the models, but not necessarily both, for the data o...
متن کاملMultiple Imputation for Incomplete Data With Semicontinuous Variables
We consider the application of multiple imputation to data containing not only partially missing categorical and continuous variables, but also partially missing ‘semicontinuous’ variables (variables that take on a single discrete value with positive probability but are otherwise continuously distributed). As an imputation model for data sets of this type, we introduce an extension of the stand...
متن کاملOn the regression method of estimation of population mean from incomplete survey data through imputation
When some observations in the sample data are missing, the application of the regression method is considered for the estimation of population mean with and without the use of imputation. The performance properties of the estimators based on the methods of mean imputation, regression imputation and no imputation are analyzed and the superiority of one method over the other is examined.
متن کاملHandling Incomplete Categorical Data for Supervised Learning
Classification is an important research topic in knowledge discovery. Most of the researches on classification concern that a complete dataset is given as a training dataset and the test data contain all values of attributes without missing. Unfortunately, incomplete data usually exist in real-world applications. In this paper, we propose new handling schemes of learning classification models f...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Physics: Conference Series
سال: 2021
ISSN: 1742-6588,1742-6596
DOI: 10.1088/1742-6596/1752/1/012049