The Em Algorithm for Standard Stochastic Frontier Models

Andrade, Bernardo Borba de; Souza, Geraldo da Silva e

doi:10.1590/0101-7438.2019.039.03.0361

Cited by 5 publications

(2 citation statements)

References 14 publications

(3 reference statements)

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“…Additionally, standard Newton techniques were used to compute connection weights for hidden-to-output layer neurons. Expectation maximization [ 39 ] and simulated likelihood [ 40 ] approaches can also be used to estimate these connection weights. When trying different techniques and solution procedures, ensembles may be built to lower estimation errors.…”

Section: Summary Conclusion and Directions For Future Workmentioning

confidence: 99%

A Radial Basis Function Neural Network for Stochastic Frontier Analyses of General Multivariate Production and Cost Functions

Pendharkar

2023

Neural Process Lett

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Production function techniques often impose functional form and other restrictions that limit their applicability. One common limitation in popular production function techniques is the requirement that all inputs and outputs must be positive numbers. There is a need to develop a production function analysis technique that is less restrictive in the assumptions it makes, and inputs it can process. This paper proposes such a general technique by linking fields of neural networks and econometrics. Specifically, two radial basis function (RBF) neural networks are proposed for stochastic production and cost frontier analyses. The functional forms of production and cost functions are considered unknown except that they are multivariate. Using simulated and real-world datasets, experiments are performed, and results are provided. The results illustrate that the proposed technique has broad applicability and performs equal to or better than the traditional stochastic frontier analysis technique.

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Section: Summary Conclusion and Directions For Future Workmentioning

confidence: 99%

A Radial Basis Function Neural Network for Stochastic Frontier Analyses of General Multivariate Production and Cost Functions

Pendharkar

2023

Neural Process Lett

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“…The validation process produced under the algorithm EM-expectation maximization, which was invented in 1977 by [3,25].…”

Section: Bayesian Networkmentioning

confidence: 99%

Using Bayesian Networks to Estimate the Effectiveness of Innovative Projects

Naumov

Voronenko

Наумова

et al. 2021

Lecture Notes in Computational Intelligence and Decision Making

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The paper proposes an application of Bayesian methodology to analyze the attachment effectiveness in the national economy. The methods for creation the BNs structure, their parametric learning, validation, and scenario analysis are examined. The research results show that at the highest level of capital investments, the financial activity result will be 48% more active, while the net profit indicator will rise by 8%. To make the profitability equal 100%, it is desirable for us to decrease the payback term by 15% and increase the level of net profit by 9%. This study can be useful for investors, managers, economists, financiers and other investment professionals. Keywords: Innovative projects• Attachment effectiveness • Bayesian networks • Validation • scenario analysis "What-if"

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