Recursive estimation of the transition distribution function of a Markov process: A symptotic normality

George G. Roussas

Statistics & Probability Letters, 1991, vol. 11, issue 5, 435-447

Abstract: Let X1,..., Xn + 1 be the first n + 1 random variables from a strictly stationary Markov process which satisfies certain additional regularity conditions. On the basis of these random variables, a recursive nonparametric estimate of the one-step transition distribution function is shown to be asymptotically normal. The class of Markov processes studied includes the Markov processes usually considered in the literature; namely, processes which either satisfy Doeblin's hypothesis, or, more generally, are geometrically ergodic.

Keywords: Markov; processes; Doeblin's; hypothesis; geometric; ergodicity; [rho]-mixing; transition; distribution; function; recursive; estimate; asymptotic; normality (search for similar items in EconPapers)
Date: 1991
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Citations: View citations in EconPapers (3)

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