平稳过程条件密度的双重核估计

DOUBLE KERNEL ESTIMATORS OF CONDITION DENSITY OF STATIONARY PROCESSES

  • 摘要: 本文在样本序列为平稳、φ-混合情形下研究了赵林城和刘志军提出的条件密度f(y|x)的双重核估计fn(y|x)的逐点强相合性和渐近正态性。我们对混合系数φ的限制是很弱的。

     

    Abstract: Let (Xn, Yn); n≥1 be Rp×Rq-valued random vectors sequence of stationary processes φ-Mixing having common joint density gx, y), Let hx) be the marginal density of X1 and Let fy|x)=gx, y)/ hx) be the conditional density of Y2 on X1, then the double kernel estimates of fy|x) is defined by f_n(y \mid x)=\sum_i=1^n K_1\left(\fracx-X_ia_n\right) K_2\left(\fracy-Y_ib_n\right) /\leftb_n^q \sum_i=1^n K_1\left(\fracx-X_ia_n\right)\right,where K1 and K2 are probability density function on Rp and Rq. respectively and both αn and bn are sequences of positive numbers converging to zero. In the paper, we study the pointwise consistency and asymptotic normality of fn(y|x)under the case of dependent asmple.

     

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