A birth and death process with logistic mean population

An example of density dependent-birth and death process whose mean satisfies the logistic equation as proposed by Gompertz is given. Explicit expressions for the probability generating function and non-trivialstationarydistribution are obtained.     

On an application of the diffusion approximation of the generalized birth and death process for stochastic population projection

Assuming that both birth and death rates are density and time dependent, a diffusion approximation of the generalized birth and death process has been considered in this paper to obtain a suitable stochastic population model describing the population size and its moments. A simple method of estimating the parameters of the model Is discussed. The predictions of the expected size of the population, and the variance are made and compared with the corresponding census figures as well as with another deterministic projection series made for the corresponding period.     

Geometric Ergodicity and Moment Conditions for a Seasonal GARCH Model with Periodic Coefficients

A seasonal GARCH process with periodic coefficients is considered and conditions for periodic stationarity, geometric ergodicity, β-mixing property with exponential decay rate, and existence of higher-order moments are obtained.     

Asymptotic Methods of Enumeration and Applications to Markov Chain Models

Abstract

The purpose of this article is to present analytic methods for determining the asymptotic behaviour of the coefficents of power series that can be applied to homogeneous discrete quasi death and birth processes. It turns that there are in principle only three types for the asymptotic behaviour. The process either converges to the stationary distribution or it can be approximated in terms of a reflected Brownian motion or by a Brownian motion. In terms of Markov chains these cases correspond to positive recurrence, to null recurrence, and to non recurrence. The same results hold for the continuous case, too.     

A formal test for the stationarity of the incidence rate using data from a prevalent cohort study with follow-up

In a prevalent cohort study with follow-up, the incidence process is not directly observed since only the onset times of prevalent cases can be ascertained. Assessing the “stationarity” of the underlying incidence process can be important for at least three reasons, including an improvement in efficiency when estimating the survivor function. We propose, for the first time, a formal test for stationarity using data from a prevalent cohort study with follow-up. The test makes use of a characterization of stationarity, an extension of this characterization developed in this paper, and of a test for matched pairs of right censored data. We report the results from a power study assuming varying degrees of departure from the null hypothesis of stationarity. The test is also applied to data obtained as part of the Canadian Study of Health and Aging (CSHA) to verify whether the incidence rate of dementia amongst the elderly in Canada has remained constant.     

Weak and strong representations for quantile processes from finite populations with application to simulation size in resampling inference

We prove three results on the weak or strong representations for quantile processes of samples drawn randomly with or without replacement from a finite population. As an application of the strong-approximation result (Theorem 2), we give an approach for determining the order of B, the number of Monte Carlo simulations required for the accuracy of resampling inference. In typical situations the order of B is between nb_n and n^2+δ, where n is the original sample size, δ > 0, and (log log n)/b_n → 0.