Bounds in total variation distance for discrete-time processes on the sequence space
Let ℙ and ℙ~ be the laws of two discrete-time stochastic processes defined on the sequence space Sℕ, where S is a finite set of points. In this paper we derive a bound on the total variation distance d TV(ℙ, ℙ~) in terms of the cylindrical projections of ℙ and ℙ~. We apply the result to Markov chain...
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| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/137236 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Let ℙ and ℙ~ be the laws of two discrete-time stochastic processes defined on the sequence space Sℕ, where S is a finite set of points. In this paper we derive a bound on the total variation distance d TV(ℙ, ℙ~) in terms of the cylindrical projections of ℙ and ℙ~. We apply the result to Markov chains with finite state space and random walks on ℤ with not necessarily independent increments, and we consider several examples. Our approach relies on the general framework of stochastic analysis for discrete-time obtuse random walks and the proof of our main result makes use of the predictable representation of multidimensional normal martingales. Along the way, we obtain a sufficient condition for the absolute continuity of ℙ~ with respect to ℙ which is of interest in its own right. |
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