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Minimax regret priors for efficiency estimation

Tsionas, Efthymios G. - In: European journal of operational research : EJOR 309 (2023) 3, pp. 1279-1285

Persistent link: https://www.econbiz.de/10014435011

Bayesian local influence analysis : with an application to stochastic frontiers

Tsionas, Efthymios G. - In: Economics letters 165 (2018), pp. 54-57

Persistent link: https://www.econbiz.de/10011973833

Stochastic frontier models with flexible random coefficients

Tsionas, Efthymios G. - In: Global business & economics review 20 (2018) 1, pp. 126-139

Persistent link: https://www.econbiz.de/10011953641

Multi-objective optimization using statistical models

Tsionas, Efthymios G. - In: European journal of operational research : EJOR 276 (2019) 1, pp. 364-378

Persistent link: https://www.econbiz.de/10011997890

Alternative Bayesian compression in Vector Autoregressions and related models

Tsionas, Efthymios G. - 2016

Persistent link: https://www.econbiz.de/10011581066

Microfoundations for stochastic frontiers

Tsionas, Efthymios G. - In: European journal of operational research : EJOR 258 (2017) 3, pp. 1165-1170

Persistent link: https://www.econbiz.de/10011644701

A Bayesian approach to find Pareto optima in multiobjective programming problems using Sequential Monte Carlo algorithms

Tsionas, Efthymios G. - In: Omega : the international journal of management science 77 (2018), pp. 73-79

Persistent link: https://www.econbiz.de/10011799159

Bounded rationality and thick frontiers in stochastic frontier analysis

Tsionas, Efthymios G. - In: European journal of operational research : EJOR 284 (2020) 2, pp. 762-768

Persistent link: https://www.econbiz.de/10012238794

Bayesian input-output table update using a benchmark LASSO prior

Tsionas, Efthymios G. - In: Economic systems research 32 (2020) 3, pp. 413-427

Persistent link: https://www.econbiz.de/10012263572

Robust Bayesian inference in stochastic frontier models

Tsionas, Efthymios G. - In: Journal of risk and financial management : JRFM 12 (2019) 4/183, pp. 1-9

We use the concept of coarsened posteriors to provide robust Bayesian inference via coarsening in order to robustify posteriors arising from stochastic frontier models. These posteriors arise from tempered versions of the likelihood when at most a pre-specified amount of data is used, and are...

Persistent link: https://www.econbiz.de/10012171282