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UBC Theses and Dissertations

Applications of penalized likelihood methods for feature selection in statistical modeling Xu, Chen


Feature selection plays a pivotal role in knowledge discovery and contemporary scientific research. Traditional best subset selection or stepwise regression can be computationally expensive or unstable in the selection process, and so various penalized likelihood methods (PLMs) have received much attention in recent decades. In this dissertation, we develop approaches based on PLMs to deal with the issues of feature selection arising from several application fields. Motivated by genomic association studies, we first address feature selection in ultra-high-dimensional situations, where the number of candidate features can be huge. Reducing the dimension of the data is essential in such situations. We propose a novel screening approach via the sparsity-restricted maximum likelihood estimator that removes most of the irrelevant features before the formal selection. The model after screening serves as an excellent starting point for the use of PLMs. We establish the screening and selection consistency of the proposed method and develop efficient algorithms for its implementation. We next turn our attention to the analysis of complex survey data, where the identification of influential factors for certain behavioral, social, and economic indices forms a variable selection problem. When data are collected through survey sampling from a finite population, they have an intrinsic dependence structure and may provide a biased representation of the target population. To avoid distorted conclusions, survey weights are usually adopted in these analyses. We use a pseudo-likelihood to account for the survey weights and propose a penalized pseudo-likelihood method for the variable selection of survey data. The consistency of the proposed approach is established for the joint randomization framework. Lastly, we address order selection for finite mixture models, which provides a flexible tool for modeling data from a heterogeneous population. PLMs are attractive for such problems. However, this application requires maximizations over nonsmooth and nonconcave objective functions, which are computationally challenging. We transform the original multivariate objective function into a sum of univariate functions and design an iterative thresholding-based algorithm to efficiently solve the sparse maximization without ad hoc steps. We establish the convergence of the new algorithm and illustrate its efficiency through both simulations and real-data examples.

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