Statistical inference for detrended point processes

We consider a multivariate point process with a parametric intensity process which splits into a stochastic factor b_t and a trend function a_t of a squared polynomial form with exponents larger than . Such a process occurs in a situation where an underlying process with intensity b_t can be observed on a transformed time scale only. On the basis of the maximum likelihood estimator for the unknown parameter a detrended (or residual) process is defined by transforming the occurrence times via integrated estimated trend function. It is shown that statistics (mean intensity, periodogram estimator) based on the detrended process exhibit the same asymptotic properties as they do in the case of the underlying process (without trend function). Thus trend removal in point processes turns out to be an appropriate method to reveal properties of the (unobservable) underlying process – a concept which is well established in time series. A numerical example of an earthquake aftershock sequence illustrates the performance of the method.     

Statistical inference in stochastic processes

(Probability: Pure and Applied, Series of Textbooks and Reference Books, vol. 6), edited by N. U. Prabhuand I. W. Basawa, Marcel Dekker, New York (1991), 288 pp. $89.75 (USA and Canda), $107.50 (All other countries). ISBN 0-8247-8417-0     

Statistical estimation for reflected skew processes

In this note, we construct estimators for the parameters that rule the law of  reflected skewed diffusion processes. The convergence properties of these estimators rely on the ergodic properties of these processes.     

The shape of the hazard rate for finite continuous-time birth-death processes

We consider the suggestion that the shape of a hazard rate can be predicted from the quasi-stationary distribution of the process, demonstrate that this hypothesis requires specific conditions, and both eliminate and suggest methods by means of which these predictions might be made.     

Statistical inference for Vasicek-type model driven by Hermite processes

Let $Z$ denote a Hermite process of order $q\ge 1$ and self-similarity parameter $H\in (\frac{1}{2},1)$. This process is $H$-self-similar, has stationary increments and exhibits long-range dependence. When $q=1$, it corresponds to the fractional Brownian motion, whereas it is not Gaussian as soon as $q?2$. In this paper, we deal with a Vasicek-type model driven by $Z$, of the form $d{X}_t=a(b?X_t)dt+d{Z}_t$. Here, $a>0$ and $b\in \mathbb{R}$ are considered as unknown drift parameters. We provide estimators for $a$ and $b$ based on continuous-time observations. For all possible values of $H$ and $q$, we prove strong consistency and we analyze the asymptotic fluctuations.     

Asymptotic inference for continuous-time Markov chains

Summary This paper deals with asymptotic optimal inference in a time-continuous ergodic Markov chain with countable state space, based on observation of the process up to timet. Let the infinitesimal generator depend on an unknown parameter. Under weak assumptions on the parametrization, we show local asymptotic normality for the statistical model ast. As a consequence, limit distributions of sequences of competing estimators for the unknown parameter are more spread out than a specified normal distribution.     

Combined nonparametric inference and state estimation for mixed poisson processes

Summary Given independent, identically distributed copies of a mixed Poisson process N on a LCCB space E, i.e., a Cox process whose directing measure is of the form m ^*, where 0 is a random variable with distribution and m ^* is a measure on E, we construct strongly consistent and asymptotically normal estimators of m ^* and the Laplace transform l . Methods are presented for estimating the directing measure of the (n+1)^st process by combining the data for that process with estimates of appropriate quantities, the latter based on the first n processes. The case where different processes are observed over different sets is addressed.Research supported in part by the Air Force Office of Scientific Research, USAF, grant number AFOSR 82-0029. The United States Government is authorized to reproduce and distribute reprints for Governmental purposes     

Optimal transformations for prediction in continuous-time stochastic processes: finite past and future

In the classical Wiener-Kolmogorov linear prediction problem, one fixes a linear functional in the future of a stochastic process, and seeks its best predictor (in the L²-sense). In this paper we treat a variant of the prediction problem, whereby we seek the most predictable non-trivial functional of the future and its best predictor; we refer to such a pair (if it exists) as an optimal transformation for prediction. In contrast to the Wiener-Kolmogorov problem, an optimal transformation for prediction may not exist, and if it exists, it may not be unique. We prove the existence of optimal transformations for finite past and future intervals, under appropriate conditions on the spectral density of a weakly stationary, continuous-time stochastic process. For rational spectral densities, we provide an explicit construction of the transformations via differential equations with boundary conditions and an associated eigenvalue problem of a finite matrix.This research was partially supported by ARO (MURI grant) DAAH04-96-1-0445, NSF grant DMS-0074276, and CNPq grant 301179/00-0.     

Statistical inference with minimumd-risk

Translated from Issledovaniya po Prikladnoi Matematike, No. 11, Part 2, pp. 25–39, 1984. Presented at the seminar of the Department of Probability Theory and Mathematical Statistics, NIIMM, May 21, 1981.     

Penny-packings with minimal second moments

We consider the problem of packingn disks of unit diameter in the plane so as to minimize the second moment about their centroid. Our main result is an algorithm which constructs packings that are optimal among hexagonal packings. Using the algorithm, we prove that, except forn=212, then-point packings obtained by Graham and Sloane [1] are optimal among hexagonal packings. We also prove a result that makes precise the intuition that the greedy algorithm of Graham and Sloane produces approximately circular packings.     

Limit theorems,scaling of moments and intermittency for integrated finite variance supOU processes

Superpositions of Ornstein–Uhlenbeck type (supOU) processes provide a rich class of stationary stochastic processes for which the marginal distribution and the dependence structure may be modeled independently. We show that they can also display intermittency, a phenomenon affecting the rate of growth of moments. To do so, we investigate the limiting behavior of integrated supOU processes with finite variance. After suitable normalization four different limiting processes may arise depending on the decay of the correlation function and on the characteristic triplet of the marginal distribution. To show that supOU processes may exhibit intermittency, we establish the rate of growth of moments for each of the four limiting scenarios. The rate change indicates that there is intermittency, which is expressed here as a change-point in the asymptotic behavior of the absolute moments.     

Average stochastic games for continuous-time jump processes

We consider nonzero-sum games for continuous-time jump processes with unbounded transition rates under expected average payoff criterion. The state and action spaces are Borel spaces and reward rates are unbounded. We introduce an approximating sequence of stochastic game models with extended state space, for which the uniform exponential ergodicity is obtained. Moreover, we prove the existence of a stationary almost Markov Nash equilibrium by introducing auxiliary static game models. Finally, a cash flow model is employed to illustrate the results.     

Empirical likelihood inference for diffusion processes with jumps

In this paper, we consider the empirical likelihood inference for the jump-diffusion model. We construct the confidence intervals based on the empirical likelihood for the infinitesimal moments in the jump-diffusion models. They are better than the confidence intervals which are based on the asymptotic normality of point estimates.     

Asymptotic inference for stochastic processes

This is a survey of some aspects of large-sample inference for stochastic processes. A unified framework is used to study the asymptotic properties of tests and estimators parameters in discrete-time, continuous-time jump-type, and diffusion processes. Two broad families of processes, viz, ergodic and non-ergodic type are introduced and the qualitative differences in the asymptotic results for the two families are discussed and illustrated with several examples. Some results on estimation and testing via Bayesian, nonparametric, and sequential methods are also surveyed briefly.     

树依谱矩的排序

首先给出了图的四种变换,得到其对任意图的谱矩的影响规律,并且利用图的这四种变换给出了树依谱矩序列s4的字典序排在前三位和后三位的图及其特征。     

Forecasting continuous-time processes with applications to signal extraction

The paper derives forecasting and signal extraction estimates for continuous time processes. We present explicit formulas for filters and filter kernels that yield minimum mean square error estimates of future values of the process or an unobserved component, based on a continuum of values in the semi-infinite past. The class of processes considered are cumulations of moving average processes, which includes the CARIMA class. Explicit examples are calculated, and some discussion of applications to signal extraction is provided. We also provide an explicit algorithm for spectral factorization of continuous-time moving averages.