Bounds for Tail Probabilities of the Sample Variance

Bounds for Tail Probabilities of the Sample Variance

Optimal Bounds on Tail Probabilities

The required number of references for the reorganization process under CS to approach the optimum within 1 + : The ratio between a Chebyshev-based bound and the stopping point in Theorem 7. The required number of references for the reorganization process under CS to approach the optimum within 1 + : The ratio between a Hoeeding-based bound and the stopping point in (1.22). 22 The Laplace transf...

Bounds on Tail Probabilities of Discrete Distributions

There has not been much work concerning bounds on tail probabilities of discrete distributions+ In the work of Johnson, Kotz, and Kemp @9# , for example, only two ~quite bad! upper bounds on Poisson tail probabilities are quoted+ This article examines several more generally applicable methods to derive lower and upper bounds on tail probabilities of discrete distributions+ Section 2 presents an...

Asymptotic algorithm for computing the sample variance of interval data

The problem of the sample variance computation for epistemic inter-val-valued data is, in general, NP-hard. Therefore, known efficient algorithms for computing variance require strong restrictions on admissible intervals like the no-subset property or heavy limitations on the number of possible intersections between intervals. A new asymptotic algorithm for computing the upper bound of the samp...

Exact Bounds on Sample Variance ofInterval

We provide a feasible (quadratic time) algorithm for computing the lower bound V on the sample variance of interval data. The problem of computing the upper bound V is, in general, NP-hard. We provide a feasible algorithm that computes V for many reasonable situations.