We study the sequential testing problem of two alternative hypotheses regarding an unknown parameter in exponential family when observations are costly. In a Bayesian setting, can be embedded Markovian framework. Using conditional probability one as underlying spatial variable, we show that cost function is concave and posterior distribution becomes more concentrated time goes on. Moreover, mon...

the  main  assumptions  in  liner  mixed  model  are normality  and  independency  of  random  effect component.   unfortunately,   these   two  assumptions   might   be  unrealistic   in  some   situations. therefore, in this paper, we will discuss about the analysis of bayesian analysis of non-normal and non-independent mixed model using skew-normal/independent distributions, and finally, thi...

A decade ago, a meta-analysis showed that identification of a suspect from a sequential lineup versus a simultaneous lineup was more diagnostic of guilt (Steblay, Dysart, Fulero, & Lindsay, 2001). Since then, controversy and debate regarding sequential superiority has emerged. We report the results of a new meta-analysis involving 72 tests of simultaneous and sequential lineups from 23 differen...

An approximation tothe sequential updating of the distribution flocation parameters of a linear time series model is developed for non-normal observations. The behaviour of the resulting non-linear recursive filtering algorithm isexamined and shown to have certain desirable properties for a variety of non-normal error distributions. Illustrative examples are given and relationships with previou...

Suppose that local characteristics of several independent compound Poisson and Wiener processes change suddenly and simultaneously at some unobservable disorder time. The problem is to detect the disorder time as quickly as possible after it happens and minimize the rate of false alarms at the same time. These problems arise, for example, from managing product quality in manufacturing systems a...

On some probability space (Ω,F,P), let (X)t≥0, 1 ≤ i ≤ d be d independent Brownian motions with constant drifts μ(i), 1 ≤ i ≤ d, and (T (j) n , Z n )n≥1, 1 ≤ j ≤ m be m independent compound Poisson processes independent of the Brownian motions (X)t≥0, 1 ≤ i ≤ d. For every 1 ≤ j ≤ m, (T (j) n )n≥1 are the arrival times, and (Z n )n≥1 are the marks on some measurable space (E, E), with arrival ra...