Parameter estimation in a verhulst stochastic model

Summary Maximum likelihood estimation is a well-known statistical tool. When applied to the study of dynamical continuous-time phenomena, it requires some specific assumptions. In this paper we discuss some properties of the maximum likelihood estimator for a stochastic Verhulst model and we show some simulation results on the behaviour of the corresponding discrete estimator.     

单片机导向“全反差”型立轴风力发电机

本文介绍一种用单片微机导向的新型高效全反差型风力发电机,并对其控制系统进行了分析讨论。     

The relationship between socioeconomic status and infant mortality in a metropolitan aggregate, 1989–1991

This paper presents the results of an ecological analysis of the relationship between infant mortality and economic status in a metropolitan aggregate comprised of seven of the larger cities in Ohio during the three years centering on the 1990 census. Using a summary income score derived for the census tract of mother's usual residence, the census tracts in the seven metropolitan centers were divided into broad income groupings and three-year average infant mortality rates were computed for each area, by age, sex, race, and selected causes of death. The most important conclusion to be drawn from the data is that in spite of some remarkable declines in overall levels of infant mortality during the past few decades, there continues to be a very clear and pronounced inverse association between income status and infant mortality. The general inverse association is observed for both sexes, for whites and nonwhites, and for all major causes of infant death. At the same time, the data reveal notable differences in the magnitude of the relationship by sex, and especially by age at death, race, and cause of death. Explanations of these differences are suggested, and a conclusion notes some of the difficulties encountered in developing programs aimed at closing the infant mortality gap between the richest and poorest segments of the society.This is an expanded version of a paper presented at the annual meetings of the Eastern Sociological Society in Baltimore, Maryland (March 17–20, 1994).     

A lower bound for the transition density function of a stochastic differential equation

Let W_t be a one-dimensional Brownian motion on the probability space (Ω,F,P), and let dx_t = a(x_t)dt + b(x_t)dw_t, b²(x) > 0, be a one-dimensional Ito stochastic differential equation. For a(x) = a₀ + a₁x + … + a_nxⁿ on a bounded interval we obtain a lower bound for p(t,x,y), the transition density function of the homogeneous Markov process x_t, depending directly on the coefficients a₀,a₁, …, a_n, and b(x).     

一类推广的Halanay不等式的稳定性研究及其应用

利用Halanay不等式的推广形式,证明了一类时滞微分系统的指数稳定性,得到了一个新的积分条件,推广和改进了已有的结果,并结合例子加以说明.     

Dependent Insurance Risk Model: Deterministic Threshold

This article considers a dependent insurance risk model. We assume that the inter-arrival time depends on the previous claim size through a deterministic threshold structure. Adjustment coefficient and Lundberg-type upper bound for the ruin probability are obtained. In case of exponential claim size, an explicit solution for the ruin probability is obtained by solving a system of ordinary delay differential equations. Some numerical results are included for illustration purposes.     

Almost Periodic Solutions for Stochastic Differential Equations Driven By G-Brownian Motion

In this paper, we introduce the concept of the p-mean almost periodicity for stochastic processes in non linear expectation spaces. The existence and uniqueness of square-mean almost periodic solutions to some non linear stochastic differential equations driven by G-Brownian motion are established under some assumptions for the coefficients. The asymptotic stability of the unique square-mean almost periodic solution in the square-mean sense is also discussed.     

A detection algorithm for the first jump time in sample trajectories of jump-diffusions driven by α-stable white noise

Abstract

The purpose of this paper is to develop a detection algorithm for the first jump point in sampling trajectories of jump-diffusions which are described as solutions of stochastic differential equations driven by α-stable white noise. This is done by a multivariate Lagrange interpolation approach. To this end, we utilize computer simulation algorithm in MATLAB to visualize the sampling trajectories of the jump-diffusions for various combinations of parameters arising in the modeling structure of stochastic differential equations.     

Cointegrated systems II

The three invited papers in this special issue of Econometric Reviews on "Cointegrated Systems II" complement the previous special issue of the journal. The paper by Eric Zivot and Peter Phillips provides a comprehensive Bayesian analysis of trend determination in economic time series. Two interesting comments on some aspects of current research involving cointegration and the modelling of dynamic economic relationships are provided by Clive Granger and Denzil Fiebig.     

Using regression mixture models with non-normal data: examining an ordered polytomous approach

Mild to moderate skew in errors can substantially impact regression mixture model results; one approach for overcoming this includes transforming the outcome into an ordered categorical variable and using a polytomous regression mixture model. This is effective for retaining differential effects in the population; however, bias in parameter estimates and model fit warrant further examination of this approach at higher levels of skew. The current study used Monte Carlo simulations; 3000 observations were drawn from each of two subpopulations differing in the effect of X on Y. Five hundred simulations were performed in each of the 10 scenarios varying in levels of skew in one or both classes. Model comparison criteria supported the accurate two-class model, preserving the differential effects, while parameter estimates were notably biased. The appropriate number of effects can be captured with this approach but we suggest caution when interpreting the magnitude of the effects.