21 research outputs found

    Limit distributions of random walks on stochastic matrices

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    Problems similar to Ann. Prob. 22 (1994) 424–430 and J. Appl. Prob. 23 (1986) 1019–1024 are considered here. The limit distribution of the sequence XnXn−1 ··· X1, where (Xn)n≥1 is a sequence of i.i.d. 2 × 2 stochastic matrices with each Xn distributed as μ, is identified here in a number of discrete situations. A general method is presented and it covers the cases when the random components Cn and Dn (not necessarily independent), (Cn, Dn) being the first column of Xn, have the same (or different) Bernoulli distributions. Thus (Cn, Dn) is valued in {0, r}2, where r is a positive real number. If for a given positive real r, with 0 \u3c r ≤ 1 2 , r−1Cn and r−1Dn are each Bernoulli with parameters p1 and p2 respectively, 0 \u3c p1, p2 \u3c 1 (which means Cn ∼ p1δ{r} + (1 − p1)δ{0} and Dn ∼ p2δ{r} + (1 − p2)δ{0}), then it is well known that the weak limit λ of the sequence μn exists whose support is contained in the set of all 2 × 2 rank one stochastic matrices. We show that S(λ), the support of λ, consists of the end points of a countable number of disjoint open intervals and we have calculated the λ-measure of each such point. To the best of our knowledge, these results are new

    Identifiability of the multivariate normal by the maximum and the minimum

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    In this paper, we have discussed theoretical problems in statistics on identification of parameters of a non-singular multi-variate normal when only either the distribution of the maximum or the distribution of the minimum is known

    Real and functional analysis

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