Methodology and Computing in Applied Probability最新文献

We study the first exit time of a fractional Brownian motion with a drift from a parabolic domain. Actually, we explore three different regimes. In the first regime, the role of drift is negligible. In the second regime, the role of drift is dominating. The behavior of exit probability is the same as that of the crossing probability of a certain moving non-random boundary. In particular, the most interesting, intermediate regime, where all factors come into play, has been solved in this paper. Finally, numerical simulations are conducted, providing an approximate range for the asymptotic estimates to illustrate the practical implications and potential applications of our main results.

Let (Z(t)= exp left( sqrt{ 2} B_H(t)- left|t right|^{2H}right) , tin mathbb {R}) with (B_H(t),tin mathbb {R}) a standard fractional Brownian motion (fBm) with Hurst parameter (H in (0,1]) and define for x non-negative the Berman function

where the random variable R independent of Z has survival function (1/x,xgeqslant 1) and

In this paper we consider a general random field (rf) Z that is a spectral rf of some stationary max-stable rf X and derive the properties of the corresponding Berman functions. In particular, we show that Berman functions can be approximated by the corresponding discrete ones and derive interesting representations of those functions which are of interest for Monte Carlo simulations presented in this article.

The Hawkes process models have been recently become a popular tool for modeling and analysis of neural spike trains. In this article, motivated by neuronal spike trains study, we propose a novel multivariate generalized linear Hawkes process model, where covariates are included in the intensity function. We consider the problem of simultaneous variable selection and estimation for the multivariate generalized linear Hawkes process in the high-dimensional regime. Estimation of the intensity function of the high-dimensional point process is considered within a nonparametric framework, applying B-splines and the SCAD penalty for matters of sparsity. We apply the Doob-Kolmogorov inequality and the martingale central limit theory to establish the consistency and asymptotic normality of the resulting estimators. Finally, we illustrate the performance of our proposal through simulation and demonstrate its utility by applying it to the neuron spike train data set.

In this paper, the stochastic dynamics of a hybrid delay food chain model with harvesting and Lévy jumps in a polluted environment is studied by using stochastic analysis techniques. Under some basic assumptions, criterions about stochastic persistence in mean and extinction of each species are established, as well as global attractivity and the existence of optimal harvesting strategy (OHS) of the system. The accurate expressions for the optimal harvesting effort (OHE) and the maximum of expectation of sustainable yield (MESY) are given. Our results show that the stochastic dynamics and OHS of the system are closely correlated with both time delays and environmental noises. Finally, some numerical simulations are introduced to illustrate the main results.

This paper examines the classical matching distribution arising in the “problem of coincidences”. We generalise the classical matching distribution with a preliminary round of allocation where items are correctly matched with some fixed probability, and remaining non-matched items are allocated using simple random sampling without replacement. Our generalised matching distribution is a convolution of the classical matching distribution and the binomial distribution. We examine the properties of this latter distribution and show how its probability functions can be computed. We also show how to use the distribution for matching tests and inferences of matching ability.

For a one-dimensional Wiener process with stochastic resetting (mathcal{X}(t)), obtained from an underlying Wiener process X(t), we study the statistical properties of its first-passage time through zero, when starting from (X>0,) and its first-passage area, that is the random area enclosed between the time axis and the path of the process (mathcal{X} (t)) up to the first-passage time through zero. By making use of solutions of certain associated ODEs, we are able to find explicit expressions for the Laplace transforms of the first-passage time and the first-passage area, and their single and joint moments.

An apportionment paradox occurs when the rules for apportionment in a political system or distribution system produce results which seem to violate common sense. For example, The Alabama paradox occurs when the total number of seats increases but decreases the allocated number of a state and the population paradox occurs when the population of a state increases but its allocated number of seats decreases. The Balinski-Young impossibility theorem showed that there is no deterministic apportionment method that can avoid the violation of the quota rule and doesn’t have both the Alabama and the population paradoxes. In this paper, we propose a randomized apportionment method as a stochastic solution to the Balinski-Young impossibility.

On a finite sequence of binary (0-1) trials we define a random variable enumerating patterns of length subject to certain constraints. For sequences of independent and identically distributed binary trials exact probability mass functions are established in closed forms by means of combinatorial analysis. An explicit expression of the mean value of this random variable is obtained. The results associated with the probability mass functions are extended on sequences of exchangeable binary trials. An application in Information theory concerning counting of a class of run-length-limited binary sequences is provided as a direct byproduct of our study. Illustrative numerical examples exemplify further the results.

Waiting is a major factor influencing the perception of delay-sensitive customers in the service industry. In the process of queueing, some customers often have a psychological expectation of waiting time in the face of uncertain delay information, so that customer service utility depends not only on the actual waiting time, but also on the relative amount of the actual waiting time and the psychological expectation of waiting time. Therefore, this paper investigates how the reference time effect affects heterogeneous customers' queueing decisions and service system efficiency measures (system throughput and social welfare) in an M/G/1 queue with limited service resources and capacity. The results show that the equilibrium joining probability of customers, the system throughput and social welfare are relatively higher as the proportion of customers with high tolerance levels in the queue increases. In addition, the maintenance of customer homogeneity is better for the improvement of service resource utilization, while the maintenance of customer heterogeneity is better for social welfare. As the psychological expected waiting time increases, the equilibrium joining probability of potential customers and the system throughput increase, while the equilibrium joining probability of existing customers decreases, and the social welfare shows a non-monotonic trend of first decreasing and then increasing. The equilibrium queueing strategies for each type of customer and the service system efficiency measures are not monotonic with the change of the reference time effect parameter. Finally, the optimal social welfare is increasing with respect to the degree of reference time effect and the psychological expectation of waiting time.