Combining data envelopment analysis and stochastic frontiers via a LASSO prior
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Publication:2079426
DOI10.1016/J.EJOR.2022.04.029OpenAlexW4224937305MaRDI QIDQ2079426
Publication date: 29 September 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.2022.04.029
Bayesian analysisdata envelopment analysistechnical efficiencyadaptive LASSOstochastic frontier analysis
Uses Software
Cites Work
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- Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis
- The Adaptive Lasso and Its Oracle Properties
- Estimation and inference in two-stage, semi-parametric models of production processes
- On estimating efficiency effects in a stochastic frontier model
- Globally flexible functional forms: the neural distance function
- Smooth approximations to monotone concave functions in production analysis: an alternative to nonparametric concave least squares
- Combining DEA and stochastic frontier models: an empirical Bayes approach.
- On the estimation of technical and allocative efficiency in a panel stochastic production frontier system model: some new formulations and generalizations
- On a high-dimensional model representation method based on copulas
- Improving finite sample approximation by central limit theorems for estimates from data envelopment analysis
- Technical, allocative and overall efficiency: estimation and inference
- A coherent approach to Bayesian data envelopment analysis
- Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain
- Marginal Likelihood from the Gibbs Output
- Sampling-Based Approaches to Calculating Marginal Densities
- Flexible Functional Forms and Global Curvature Conditions
- Ideal spatial adaptation by wavelet shrinkage
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Adaptive Rejection Sampling for Gibbs Sampling
- Bayes Factors
- On the marginal likelihood and cross-validation
- Optimally sparse representation in general (nonorthogonal) dictionaries via ℓ 1 minimization
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