Time-dependent prediction and evaluation of variable importance using superlearning in high-dimensional clinical 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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In the context of classification using high-dimensional data such as microarray gene expression data, it is often useful to perform preliminary variable selection. For example, the k-nearest-neighbors classification procedure yields a much higher accuracy when applied on variables with high discriminatory power. Typical (univariate) variable selection methods for binary classification are, e.g....
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ژورنال
عنوان ژورنال: Journal of Trauma and Acute Care Surgery
سال: 2013
ISSN: 2163-0755
DOI: 10.1097/ta.0b013e3182914553