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Partial correlation screening for varying coefficient models | ||
| Journal of Mathematical Modeling | ||
| مقاله 2، دوره 8، شماره 4، آذر 2020، صفحه 363-376 اصل مقاله (322.41 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22124/jmm.2020.15692.1379 | ||
| نویسنده | ||
| Mohammad Kazemi* | ||
| Department of Statistics, Faculty of Mathematical Sciences, Shahrood University of Technology, Shahrood, Iran | ||
| چکیده | ||
| In this paper, we propose a two-stage approach for feature selection in varying coefficient models with ultra-high-dimensional predictors. Specifically, we first employ partial correlation coefficient for screening, and then penalized rank regression is applied for dimension-reduced varying coefficient models to further select important predictors and estimate the coefficient functions. Simulation studies are carried out to examine the performance of proposed approach. We also illustrate it by a real data example. | ||
| کلیدواژه‌ها | ||
| Big data؛ feature screening؛ partial correlation؛ rank regression | ||
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آمار تعداد مشاهده مقاله: 773 تعداد دریافت فایل اصل مقاله: 1,024 |
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