Abstract
This chapter discusses estimation for stochastic processes. It presents the assumption that if {ξn} is a discrete time stationary Markov process satisfying Doeblin's condition (D0), then it can be shown that {ξn} is ϕ-mixing with ϕ(n) = aρn for some a ≥ 1 and 0 <ρ < 1. The chapter discusses conditions on the basis of the assumption presented in the chapter that K(·) is a bounded, symmetric probability density function defined on R satisfying another set of conditions.
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