Comparison of Methods for Variable Selection in High-Dimensional Linear Mixed Models
نویسنده
چکیده
Abstract. Currently is the analysis of high-dimensional data a popular field of research, thanks to many applications e.g. in genetics. At the same time, the type of problems that tend to arise in genetics, can often be modeled using LMMs in conjunction with high-dimensional data. In this paper we introduce two new methods and briefly compare them to existing methods, which can be used for variable selection in high-dimensional linear mixed models. As we will show in a small simulation study, both methods perform well compared to existing methods.
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