activity
20152020
most citedShape constrained kernel-weighted least squares: Application to production function estimation for Chilean manufacturing industries

6 citations · 10 across the 4 of their papers we have counts for

collaborators

6 papers

stat.ME2020

Least Squares Estimation of a Quasiconvex Regression Function

Somabha Mukherjee, Rohit K. Patra, Andrew L. Johnson +1

We develop a new approach for the estimation of a multivariate function based on the economic axioms of quasiconvexity (and monotonicity). On the computational side, we prove the e…

stat.AP2019★ 2 cited

An axiomatic nonparametric production function estimator: Modeling production in Japan's cardboard industry

Daisuke Yagi, Yining Chen, Andrew L. Johnson +1

We develop a new approach to estimate a production function based on the economic axioms of the Regular Ultra Passum law and convex non-homothetic input isoquants. Central to the d…

stat.AP2019

Direction Selection in Stochastic Directional Distance Functions

Kevin Layer, Andrew L. Johnson, Robin C. Sickles +1

Researchers rely on the distance function to model multiple product production using multiple inputs. A stochastic directional distance function (SDDF) allows for noise in potentia…

stat.AP2016★ 6 cited

Shape constrained kernel-weighted least squares: Application to production function estimation for Chilean manufacturing industries

Daisuke Yagi, Yining Chen, Andrew L. Johnson +1

In this paper we examine a novel way of imposing shape constraints on a local polynomial kernel estimator. The proposed approach is referred to as Shape Constrained Kernel-weighted…

stat.AP2016

Insights from Machine Learning for Evaluating Production Function Estimators on Manufacturing Survey Data

José Luis Preciado Arreola, Daisuke Yagi, Andrew L. Johnson

Organizations like U.S. Census Bureau rely on non-exhaustive surveys to estimate industry-level production functions in years in which a full Census is not conducted. When analyzin…

stat.ME2015★ 2 cited

Estimating Stochastic Production Frontiers: A One-stage Multivariate Semi-Nonparametric Bayesian Concave Regression Method

José Luis Preciado Arreola, Andrew L. Johnson

This paper describes a method to estimate a production frontier that satisfies the axioms of monotonicity and concavity in a non-parametric Bayesian setting. An inefficiency term t…