Two stochastic models for the geeta distribution

The first stochastic model is based upon two urns A and B, where A contains a fixed number of white and black balls and B is empty. The player selects an integer β ≥, 2 and draws the balls one by one (with replacement) from urn A and balls of the same colour are put in urn B. The process is continued as long as the number of white balls in B exceeds (β-1) times the number of black balls in B. The player stops after drawing β(x-1) balls and is declared to be a winner if urn B has (x-1) black balls. It is shown that x has the Geeta distribution.

Assuming that the mean μ is a function of two parameters θ and β it has been shown that for small changes inthe value of θ there exists a difference-differential equation which leads to the Geeta distribution.     

On the asymptotic distribution of an estimate of the change point in a failure rate

The failure rate r(t) is assumed to have the shape of the"first"part of the"bathtub"model, i.e.r(t) is non-increasing for t<r and is constant for t> r. Asymptotic distribution of one of the estimates proposed earlier has been investigated in this paper. This leads to a test for the hypothesis HQ r<r ₀ vs H :r>r (where TQ > 0). Asymptotic expression for the power of this test under Pitman alternatives is derived. Some simulations are reported.     

Characterizations of the poisson process using,the variance

Let γ(t) be the residual life at time t of the renewal process {A(t), t > 0}, which has F as the common distribution function of the inter-arrival times. In this article we prove that if Var(γ(t)) is constant, then F will be exponentially or geometrically distributed under the assumption F is continuous or discrete respectively. An application and a related example also are given.     

Multi-stage estimation of the common location parameter of several exponential distributions

The problem of constructing a confidence interval of ‘preassigned width and coverage probability’ considered by Costanza/ Hamdy and Son(1986) is further analyzed. Several multi-stage estimation procedures [ like, purely sequential, accelerated sequential and three-stage procedures ] are utilized to deal with the same estimation problem. The relative advantages and disadvantages of these procedures are discussed.     

Heine-euler extensions of the poisson distribution

The paper shows that the Heine and Euler distributions (Benkherouf and Bather, 1988) are members of a family of q-series anologues of the Poisson distribution, with similar probability mass functions, but different restrictions on their parameters, and different modes of genesis and properties. The relationships between the Heine, Euler, pseudo-Euler, Poisson and geometric distributions are explored. Illustrative data sets are discussed.     

Estimating the discovery rate in a continuous time recapture model

We consider a family of marked Poisson process models for the discovery of distinct errors in a computer program and also for sampling, in continu-ous time, a population containing an unknown number of distinct biological species. Captures (selections or discoveries) are assumed to occur at a con-stant rate, each event consisting of the discovery of a distinct process (error or species) or the recurrence of a previously discovered process. Using a generalization of Nayak’s (1988) model we derive confidence limits for the discovery rate. The limits are based on the asymptotic distribution of a scaled logarithmic function of the maximum likelihood estimator.