6 citations · 10 across the 7 of their papers we have counts for
5 papers · 1 filter
Overfitting Reduction in Convex Regression
Zhiqiang Liao, Sheng Dai, Eunji Lim +1
Convex regression is a method for estimating the convex function from a data set. This method has played an important role in operations research, economics, machine learning, and…
Modeling economies of scope in joint production: Convex regression of input distance function
Timo Kuosmanen, Sheng Dai
Modeling of joint production has proved a vexing problem. This paper develops a radial convex nonparametric least squares (CNLS) approach to estimate the input distance function wi…
Optimal resource allocation: Convex quantile regression approach
Sheng Dai, Natalia Kuosmanen, Timo Kuosmanen +1
Optimal allocation of resources across sub-units in the context of centralized decision-making systems such as bank branches or supermarket chains is a classical application of ope…
Convex Support Vector Regression
Zhiqiang Liao, Sheng Dai, Timo Kuosmanen
Nonparametric regression subject to convexity or concavity constraints is increasingly popular in economics, finance, operations research, machine learning, and statistics. However…
Variable selection in convex quantile regression: L1-norm or L0-norm regularization?
Sheng Dai
The curse of dimensionality is a recognized challenge in nonparametric estimation. This paper develops a new L0-norm regularization approach to the convex quantile and expectile re…