In this paper we introduce some new test functions to assess the performance of global optimization methods. These functions have been selected partly because several of them are aesthetically appealing and partly because a few of them are really difficult to optimize, while all the functions...

Multicollinearity is a serious problem in applied regression analysis. Q. Paris (2001) introduced the MEL estimator to resolve the multicollinearity problem. This paper improves the MEL estimator to the Modular MEL (MMEL) estimator and shows by Monte Carlo experiments that MMEL estimator...

In this paper we introduce some new test functions to assess the performance of global optimization methods. These functions have been selected partly because several of them are aesthetically appealing and partly because a few of them are really difficult to optimize, while all the functions...

Most of the studies relating to estimation of joint production functions have noted two difficulties: first that allocation of inputs to different outputs is not known, and the second that a method of estimation cannot have more than one dependent variable, which necessitates construction of a...

In this paper, we create machine learning (ML) models to forecast home equity credit risk for individuals using a real-world dataset and demonstrate methods to explain the output of these ML models to make them more accessible to the end-user. We analyze the explainability of these models for...