A penalized method for multivariate concave least squares with application to productivity analysis

Tutkimustuotos: Lehtiartikkelivertaisarvioitu

Tutkijat

  • Abolfazl Keshvari

Organisaatiot

Kuvaus

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.

Yksityiskohdat

AlkuperäiskieliEnglanti
Sivut1016-1029
Sivumäärä14
JulkaisuEuropean Journal of Operational Research
Vuosikerta257
Numero3
TilaJulkaistu - 16 maaliskuuta 2017
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu

ID: 11703191