Asymptotic optimality of data-driven Neyman's tests for uniformity

Asymptotic Optimality of Certain Multi - Alternativesequential Tests

It is known that certain combinations of one-sided sequential probability ratio tests are asymptotically optimal (relative to the expected sample size) for problems involving a nite number of possible distributions when probabilities of errors tend to zero and observations are independent and identically distributed according to one of the underlying distributions. The objective of this paper i...

Asymptotic Optimality of CertainMultihypothesis Sequential Tests: Non-i.i.d. Case

It is known that certain combinations of one-sided sequential probability ratio tests are asymptotically optimal (relative to the expected sample size) for problems involving a nite number of possible distributions when probabilities of errors tend to zero and observations are independent and identically distributed according to one of the underlying distributions. The objective of this paper i...

Multihypothesis sequential probability ratio tests - Part I: Asymptotic optimality

The problem of sequential testing of multiple hypotheses is considered, and two candidate sequential test procedures are studied. Both tests are multihypothesis versions of the binary sequential probability ratio test (SPRT), and are referred to as MSPRT’s. The first test is motivated by Bayesian optimality arguments, while the second corresponds to a generalized likelihood ratio test. It is sh...

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...

Asymptotic Optimality of Nonparametric Tests for Restricted Alternatives in Multivariate Mixed Models