Asymptotic Properties of Error Density Estimator in Regression Model Under -Mixing Assumptions

作者：Liang Han Ying^*; Niu Si Li

来源：Communications in Statistics - Theory and Methods, 2009, 38(6): 761-783.

DOI：10.1080/03610920802306741

• Asymptotic normality
• Consistency
• Error density
• Law of the iterated logarithm
• -mixing

In this article, we study the consistency of the error density estimator in nonparametric regression models when the errors form a stationary -mixing sequences. These results improve the results of Cheng (2004) from the i.i.d. assumption to -mixing condition and weaken the restrictions for the bandwidths an. Also, the rates of strong convergence for the estimator are investigated. Furthermore, we derive the asymptotic normality and the law of the iterated logarithm of the histogram-type error density estimator, which complement the conclusion in Cheng (2002) in -mixing setting.

• 出版日期2009
• 单位同济大学