Subsampling bootstrap in network DEA
From MaRDI portal
Publication:2098049
DOI10.1016/J.EJOR.2022.06.022OpenAlexW4283463782MaRDI QIDQ2098049
Maria Michali, Akram Dehnokhalaji, Ben Clegg, Ali Emrouznejad
Publication date: 17 November 2022
Published in: European Journal of Operational Research (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.ejor.2022.06.022
Cites Work
- Unnamed Item
- Unnamed Item
- Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis
- Measuring the efficiency of decision making units
- ASYMPTOTICS AND CONSISTENT BOOTSTRAPS FOR DEA ESTIMATORS IN NONPARAMETRIC FRONTIER MODELS
- A computationally efficient, consistent bootstrap for inference with non-parametric DEA estimators
- Productivity and intermediate products:
- Asymptotic distribution of conical-hull estimators of directional edges
- Two-stage cooperation model with input freely distributed among the stages
- Network DEA: additive efficiency decomposition
- Deriving the DEA frontier for two-stage processes
- Additive efficiency decomposition in two-stage DEA
- Estimating and bootstrapping Malmquist indices
- Some asymptotic theory for the bootstrap
- Bootstrap methods: another look at the jackknife
- Non-parametric tests of returns to scale
- The origins and development of statistical approaches in non-parametric frontier models: a survey of the first two decades of scholarly literature (1998--2020)
- Network DEA pitfalls: divisional efficiency and frontier projection under general network structures
- Efficiency decomposition in two-stage data envelopment analysis: an application to non-life insurance companies in Taiwan
- Sensitivity Analysis of Efficiency Scores: How to Bootstrap in Nonparametric Frontier Models
- Large Sample Approximation of the Distribution for Convex-Hull Estimators of Boundaries
- A note on proving that the (modified) bootstrap works
- Maximum Likelihood, Consistency and Data Envelopment Analysis: A Statistical Foundation
- A general methodology for bootstrapping in non-parametric frontier models
- On Estimation of Monotone and Concave Frontier Functions
This page was built for publication: Subsampling bootstrap in network DEA