Generalized predictive recursion maximum likelihood for robust mixture regression
© Published under licence by IOP Publishing Ltd. In the application of econometric model, the error distribution is unknown and is not easily to specify in the likelihood function. In some situations, there might exist a mixture distribution in the errors and thus the traditional estimation method w...
محفوظ في:
| المؤلفون الرئيسيون: | , , |
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| التنسيق: | وقائع المؤتمر |
| منشور في: |
2018
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85051398682&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/59120 |
| الوسوم: |
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| الملخص: | © Published under licence by IOP Publishing Ltd. In the application of econometric model, the error distribution is unknown and is not easily to specify in the likelihood function. In some situations, there might exist a mixture distribution in the errors and thus the traditional estimation method would probably yield a biased result. In this study, this mixture distribution of the error term is taken into account and the generalized semiparametric estimation is presented and applied in regression model. We also use an experiment study and the real application analysis to check the performance of this estimator in regression model. The performance of this estimation is then compared with that of conventional Least Squares method in the real data analysis. |
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