نوع مقاله : مقاله پژوهشی
گروه آمار-دانشگاه بین المللی امام خمینی قزوین
عنوان مقاله [English]
Quantile regression model and its generalizations, including M-quantile regression model, are analyzed usually via a nonparametric approach and their parameters are estimated using some iterative optimization algorithms. For these reason, in these models confidence intervals and hypotheses testing have done perforce using rank-based or bootstrapping approaches. In this paper, we consider parametric analysis of M-quantile model. It is shown that, the frequentist based approach of maximum likelihood estimation leads to results that are similar to the nonparametric approach. Hence, in order to achieve a more afficient model, we have been used the Bayes theory and a hierarchical Bayes model has been developed. The efficiency of the proposed model has been assessed via a simulation study and real word example. The results show that the Bayesian approach of m-quantile regression analysis is more efficient than the correspond frequantist approach, for all sample sizes. In addition, the proposed model truly takes into account the effect of the outlier observation, which causes skewness in response variable distribution, in modeling.