Monte Carlo Simulation for Modified Parametric Of Sample Selection Models Through Fuzzy Approach

Monte Carlo Simulation for Modified Parametric Of Sample Selection Models Through Fuzzy Approach

Yaya Sudarya Triana

Information Systems Department Information System, Universitas Mercu Buana Jakarta, Indonesia

American Journal of Basic and Applied Sciences

The sample selection model is a combination of the regression and probit models. The models are usually estimated by Heckman’s two-step estimator. However, Heckman’s two-step estimator often performs poorly. In the context of parametric methods, the sample selection model is studied. The best approach is to take advantage of the tools provided by the theory of fuzzy sets. It appears very suitable for modeling vague concepts. It is difficult to determine some of the criteria and arrive at a quantitative value. Fuzzy sets theory and its properties through the concept of fuzzy number. The fuzzy function used for solving uncertain of a parametric sample selection model. Estimates from the fuzzy are used to calculate some of equation of the sample selection model. Finally, estimates of the Mean, Root Mean Square Error (RMSE) and the other estimators can be obtained by Heckman two-step estimator through iteration from some parameters and some of values.

Keywords:  Fuzzy, Heckman’s, Monte Carlo, Sample selection model, Simulation

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How to cite this article:
Melo, M.C.F, Macêdo, T.S, Oliveira, J.C, Araújo, M.G.C, Vidal, A.K.L. Odontologic atention on oncological practice.American Journal of Basic and Applied Sciences, 2018, 1:6. (Accepted for publication, Online first, Under proofreading)


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