17 citations · 38 across the 10 of their papers we have counts for
10 papers
Quantile estimation of CO2 marginal abatement cost across emission-generating technologies
Haleh Delnava, Sheng Dai
Marginal abatement cost (MAC) is a critical metric for designing efficient and cost-effective mitigation policies. However, existing MAC estimates are typically derived under diffe…
Economic growth of cities: Does resource allocation matter?
Sheng Dai, Timo Kuosmanen, Zhiqiang Liao
We study how efficient resource reallocation across cities affects potential aggregate growth. Using optimal resource allocation models and data on 284 China's prefecture-level cit…
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…
Partial frontiers are not quantiles
Sheng Dai, Timo Kuosmanen, Xun Zhou
Quantile regression and partial frontier are two distinct approaches to nonparametric quantile frontier estimation. In this article, we demonstrate that partial frontiers are not q…
Non-crossing convex quantile regression
Sheng Dai, Timo Kuosmanen, Xun Zhou
Quantile crossing is a common phenomenon in shape constrained nonparametric quantile regression. A recent study by Wang et al. (2014) has proposed to address this problem by imposi…
pyStoNED: A Python Package for Convex Regression and Frontier Estimation
Sheng Dai, Yu-Hsueh Fang, Chia-Yen Lee +1
Shape-constrained nonparametric regression is a growing area in econometrics, statistics, operations research, machine learning and related fields. In the field of productivity and…
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…