Variable selection and prediction with incomplete high-dimensional data
نویسندگان
چکیده
منابع مشابه
Variable Selection and Prediction with Incomplete High-dimensional Data.
We propose a Multiple Imputation Random Lasso (mirl) method to select important variables and to predict the outcome for an epidemiological study of Eating and Activity in Teens. In this study 80% of individuals have at least one variable missing. Therefore, using variable selection methods developed for complete data after listwise deletion substantially reduces prediction power. Recent work o...
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ژورنال
عنوان ژورنال: The Annals of Applied Statistics
سال: 2016
ISSN: 1932-6157
DOI: 10.1214/15-aoas899