کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
696914 890352 2012 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Variable selection via RIVAL (removing irrelevant variables amidst Lasso iterations) and its application to nuclear material detection
موضوعات مرتبط
مهندسی و علوم پایه سایر رشته های مهندسی کنترل و سیستم های مهندسی
پیش نمایش صفحه اول مقاله
Variable selection via RIVAL (removing irrelevant variables amidst Lasso iterations) and its application to nuclear material detection
چکیده انگلیسی

In many situations, the number of data points is fixed, and the asymptotic convergence results of popular model selection tools may not be useful. A new algorithm for model selection, RIVAL (removing irrelevant variables amidst Lasso iterations), is presented and shown to be particularly effective for a large but fixed number of data points. The algorithm is motivated by an application of nuclear material detection where all unknown parameters are to be non-negative. Thus, positive Lasso and its variants are analyzed. Then, RIVAL is proposed and is shown to have some desirable properties, namely the number of data points needed to have convergence is smaller than existing methods.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Automatica - Volume 48, Issue 9, September 2012, Pages 2107–2115
نویسندگان
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