Kernel density estimation for linear processes

Lanh Tat Tran

Stochastic Processes and their Applications, 1992, vol. 41, issue 2, 281-296

Abstract: Let X1,...,Xn be n consecutive observations of a linear process , where [mu] is a constant and {Zt} is an innovation process consisting of independent and identically distributed random variables with mean zero and finite variance. Assume that X1 has a probability density [latin small letter f with hook]. Uniform strong consistency of kernel density estimators of [latin small letter f with hook] is established, and their rates of convergence are obtained. The estimators can achieve the rate of convergence (n-1 log n)1/3 in L[infinity] norm restricted to compacts under weak conditions.

Keywords: kernel; density; estimator; bandwidth; linear; process; uniform; convergence (search for similar items in EconPapers)
Date: 1992
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