Moderate deviation principle for autoregressive processes

A moderate deviation principle for autoregressive processes is established. As statistical applications we provide the moderate deviation estimates of the least square and the Yule–Walker estimators of the parameter of an autoregressive process. The main assumption on the autoregressive process is the Gaussian integrability condition for the noise, which is weaker than the assumption of Logarithmic Sobolev Inequality in [H. Djellout, A. Guillin, L. Wu, Moderate deviations of empirical periodogram and nonlinear functionals of moving average processes, Ann. I. H. Poincaré-PR 42 (2006) 393–416].     

Large deviations for squares of Bessel and Ornstein-Uhlenbeck processes

Let (X_t⁽⁾,t0) be the BESQ process starting at x. We are interested in large deviations as for the family {^–1X_t⁽⁾,tT}, – or, more generally, for the family of squared radial OU process. The main properties of this family allow us to develop three different approaches: an exponential martingale method, a Cramér–type theorem, thanks to a remarkable additivity property, and a Wentzell–Freidlin method, with the help of McKean results on the controlled equation. We also derive large deviations for Bessel bridges.Mathematics Subject Classification (2000): 60F10, 60J60     

Large and moderate deviations for infinite-dimensional autoregressive processes

We consider large and moderate deviations for the empirical mean and covariance of hilbertian autoregressive processes. As an application we obtain moderate deviations principles for the eigenvalues and associated projectors of the empirical covariance.     

Large deviation principle for enhanced Gaussian processes

We study large deviation principles for Gaussian processes lifted to the free nilpotent group of step N. We apply this to a large class of Gaussian processes lifted to geometric rough paths. A large deviation principle for enhanced (fractional) Brownian motion, in Hölder- or modulus topology, appears as special case.     

Small random perturbations of one-dimensional diffusion processes

In the present paper we consider the small random perturbations of one-dimensional diffusion processes. By virtue of stochastic analysis methods, we investigate the asymptotics of the mean exit times and probabilistical estimates of the exit times as the perturbations tend to zero. Partially supported by the Young Teachers Foundation of Beijing Institute of Technology     

无穷维自回归过程的中偏差原理

本文考虑无穷维自回归过程经验协方差函数的中偏差原理,仅对自回归过程的随机扰动项做了高斯可积性的假设,这个条件比[4]中的对数Sobolev不等式要弱很多.主要利用了m-相依随机变量的中偏差结果和Ellis-Grtner定理,推广了[6]的结果.     

Moduli of continuity of local times of strongly symmetric Markov processes via Gaussian processes

LetX be a strongly symmetric standard Markov process on a locally compact metric spaceS with 1-potential densityu ¹(x, y). Let {L _t ^y , (t, y)R ⁺×S} denote the local times ofX and letG={G(y), yS} be a mean zero Gaussian process with covarianceu ¹(x, y). In this paper results about the moduli of continuity ofG are carried over to give similar moduli of continuity results aboutL _t ^y considered as a function ofy. Several examples are given with particular attention paid to symmetric Lévy processes.The research of both authors was supported in part by a grant from the National Science Foundation. In addition the research of Professor Rosen was also supported in part by a PSC-CUNY research grant. Professor Rosen would like to thank the Israel Institute of Technology, where he spent the academic year 1989–90 and was supported, in part, by the United States-Israel Binational Science Foundation. Professor Marcus was a faculty member at Texas A&M University while some of this research was carried out.     

On the first passage times of reflected O-U processes with two-sided barriers

In this article, we give the Laplace transform of the first passage times of reflected Ornstein-Uhlenbeck process with two-sided barriers. AMS Subject Classifications 60H10 · 60G40 · 90B05 Supported by NSF of China.     

延拓分支过程

本给出一类连续时间延拓分支过程Ｘ（ｔ）的大偏差原理成立的条件以及有关的一类上鞅。     

Asymptotic expansions for Laplace transforms of Markov processes

Let ${\mu }^{?}$ be the probability measures on $D[0,T]$ of suitable Markov processes ${\{{\xi }_t^{?}\}}_{0\le t\le T}$ (possibly with small jumps) depending on a small parameter $?>0$, where $D[0,T]$ denotes the space of all functions on $[0,T]$ which are right continuous with left limits. In this paper we investigate asymptotic expansions for the Laplace transforms ${\int }_{D[0,T]}\exp ?\{{?}^{?1}F(x)\}{\mu }^{?}(dx)$ as $?\to 0$ for smooth functionals F on $D[0,T]$. This study not only recovers several well-known results, but more importantly provides new expansions for jump Markov processes. Besides several standard tools such as exponential change of measures and Taylor's expansions, the novelty of the proof is to implement the expectation asymptotic expansions on normal deviations which were recently derived in [13].     

Occupation times of intervals until first passage times for spectrally negative Lévy processes

In this paper, we identify Laplace transforms of occupation times of intervals until first passage times for spectrally negative Lévy processes. New analytical identities for scale functions are derived and therefore the results are explicitly stated in terms of the scale functions of the process. Applications to option pricing and insurance risk models are also presented.     

Stochastic ordering of Gini indexes for multivariate elliptical risks

In this paper, we show that the conjecture, made by Samanthi et al. (2016), on the ordering of Gini indexes of multivariate normal risks with respect to the strength of dependence, is not true. By using the positive semi-definite ordering of covariance matrices, we can obtain the usual stochastic order of the Gini indexes for multivariate normal risks. This can be generalized to multivariate elliptical risks. We also investigate the monotonicity of the Gini indexes in the usual stochastic order when the covariance (dispersion, resp.) matrices of multivariate normal (elliptical, resp) risks increase componentwise. In addition, we derive a large deviation result for the Gini indexes of multivariate normal risks.     

A large-deviation result for regenerative processes

A large-deviation result is proved for processes. The result is applicable when regeneration points exist for the sequence. Examples of such applications are indicated.This work was supported by The Dean's Fund for Research of the Owen Graduate School of Management of Vanderbilt University.     

A large deviation for occupation times of super-stable process

1.IntroductionMotiffedbysomelargedeviationresultsforbranchingparticlesystem,thelargedeviationresultsforsuperprocesseshavebeeninvestigatedrecently.CoxandGffeathll]startedtheinvestigationofthelargedevistionforcriticalbranchingBrochanmotion.IscoeIZIcarr...     

Phase-type distributions for failure times

Phase-type distributions describe the random time taken for a Markov process to reach an absorbing state. In the context of component failure, sequential movement through the transient states (phases) of such a system could describe the ageing process with movement out of these states (absorption) corresponding to failure. Thus, the lifetime of a component is the absorption time and the probability distribution of these times can be written in terms of the solution of a system of differential equations for which there are many convenient computational algorithms. A variety of different distributions is possible by varying the parameters of the process and hazard rates of various shapes can be constructed, allowing different patterns of variation in observed data to be modelled. These distributions are applied to some industrial data-sets and further features of the processes discussed.     

Rough paths analysis of general Banach space-valued Wiener processes

In this article, we carry out a rough paths analysis for Banach space-valued Wiener processes. We show that most of the features of the classical Wiener process pertain to its rough path analog. To be more precise, the enhanced process has the same scaling properties and it satisfies a Fernique type theorem, a support theorem and a large deviation principle in the same Hölder topologies as the classical Wiener process does. Moreover, the canonical rough paths of finite dimensional approximating Wiener processes converge to the enhanced Wiener process. Finally, a new criterion for the existence of the enhanced Wiener process is provided which is based on compact embeddings. This criterion is particularly handy when analyzing Kunita flows by means of rough paths analysis which is the topic of a forthcoming article.     

Quasi-maximum likelihood estimators in generalized linear models with autoregressive processes

The paper studies a generalized linear model(GLM)y_t = h(x_t~T β) + ε_t,t = l,2,...,n,where ε_1 = η_1,ε_1 =ρε_t +η_t,t = 2,3,...;n,h is a continuous differentiable function,η_t's are independent and identically distributed random errors with zero mean and finite variance σ~2.Firstly,the quasi-maximum likelihood(QML) estimators of β,p and σ~2 are given.Secondly,under mild conditions,the asymptotic properties(including the existence,weak consistency and asymptotic distribution) of the QML estimators are investigated.Lastly,the validity of method is illuminated by a simulation example.     

Backward and forward recurrence times,and their interrelationships in Bellman-Harris branching processes

Random variables are constructed for a Bellman-Harris branching process which are the extensions of backward and forward recurrence times from an ordinary renewal process. The sum of the two extended random variables is also constructed, and then the distribution functions for all three random variables are developed. The asymptotic limits for all three time-dependent distributions are also obtained.