کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4960045 1445964 2017 28 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A penalized method for multivariate concave least squares with application to productivity analysis
ترجمه فارسی عنوان
یک روش جریمه برای مقادیر کوچک مقعر چند متغیر با استفاده از تجزیه و تحلیل بهره وری
کلمات کلیدی
رگرسیون مختصری رگرسیون محدب، روش مجازات، تابع تولید،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر علوم کامپیوتر (عمومی)
چکیده انگلیسی
We propose a penalized method for the least squares estimator of a multivariate concave regression function. This estimator is formulated as a quadratic programing (QP) problem with O(n2) constraints, where n is the number of observations. Computing such an estimator is a very time-consuming task, and the computational burden rises dramatically as the number of observations increases. By introducing a quadratic penalty function, we reformulate the concave least squares estimator as a QP with only non-negativity constraints. This reformulation can be adapted for estimating variants of shape restricted least squares, i.e. the monotonic-concave/convex least squares. The experimental results and an empirical study show that the reformulated problem and its dual are solved significantly faster than the original problem. The Matlab and R codes for implementing the penalized problems are provided in the paper.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: European Journal of Operational Research - Volume 257, Issue 3, 16 March 2017, Pages 1016-1029
نویسندگان
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