Functions of a Markov Process that are Markovian

  • Richard A. Davis
  • Keh-Shin Lii
  • Dimitris N. Politis
Part of the Selected Works in Probability and Statistics book series (SWPS)


In this paper we are primarily concerned with discrete time parameter Markov processes {X(n)}, n = 0, 1, 2, …, with stationary transition mechanism. The processes {Y(n)} = {f(X(n))} generated by a given many-one function f and the processes {X(n)} with a fixed stationary transition mechanism are constructed. The processes {Y(n)} are in a one-to-one correspondence with the possible initial distributions of {X(n)}. The object of the paper is to determine conditions under which {Y(n)} is Markovian, whatever the initial distribution of {X(n)}. Necessary and sufficient conditions for the new processes {Y(n)} to be Markovian are obtained under the assumption that the family of measures corresponding to the fixed transition mechanism (of {X(n)}) is dominated [4]. The conditions are expressed, of course, in terms of the function f(•) and the transition mechanism. Generally the processes {Y(n)} do not have stationary transition mechanism. The conditions simplify in the case of a continuous time parameter Markov chain. Some of the discussions may at times have the flavor of those used in considering the concept of sufficiency [4]. Some aspects of the problem discussed in the paper are touched on in [2].


Probability Measure Markov Process Transition Function Initial Distribution Transition Mechanism 
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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Richard A. Davis
    • 1
  • Keh-Shin Lii
    • 2
  • Dimitris N. Politis
    • 3
  1. 1.Department of StatisticsColumbia UniversityNew YorkUSA
  2. 2.Department of StatisticsUniversity of CaliforniaRiversideUSA
  3. 3.Department of MathematicsUniversity of California, San DiegoLa JollaUSA

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