A new adaptive local linear prediction method and its application in hydrological time series.
A New Approach to Modeling of Stochastic Processes Based on Principal Components of Henkel Matrix
A new estimation algorithm from measurements with multiple-step random delays and packet dropouts.
A note on Anderson's note on a stationary autoregressive process
A form of the covariance matrix of a weakly stationary first-order autoregressive process is established.
A Note on Optimum Spacing of Observations from a Continuous Time Simple Markov Process.
A note on prediction for discrete time series
Let be a stationary and ergodic time series taking values from a finite or countably infinite set and that is a function of the process with finite second moment. Assume that the distribution of the process is otherwise unknown. We construct a sequence of stopping times along which we will be able to estimate the conditional expectation from the observations in a point wise consistent way for a restricted class of stationary and ergodic finite or countably infinite alphabet time series...
A note on the application of integrals involving cyclic products of kernels.
In statistics of stochastic processes and random fields, a moment function or a cumulant of an estimate of either the correlation function or the spectral function can often contain an integral involving a cyclic product of kernels. We define and study this class of integrals and prove a Young-Hölder inequality. This inequality further enables us to study asymptotics of the above mentioned integrals in the situation where the kernels depend on a parameter. An application to the problem of estimation...
A Note on the Asymptotic Behaviour of a Periodic Multitype Galton-Watson Branching Process
2000 Mathematics Subject Classification: 60J80.In this work, the problem of the limiting behaviour of an irreducible Multitype Galton-Watson Branching Process with period d greater than 1 is considered. More specifically, almost sure convergence of some linear functionals depending on d consecutive generations is studied under hypothesis of non extinction. As consequence the main parameters of the model are given a convenient interpretation from a practical point of view. For a better understanding...
A note on the Poisson disorder problem
A note on the properties of generalised separable spatial autoregressive process.
A note on the third-order moment structure of bilinear model with non-independent shocks.
A Proof of a Relationship Between the Generalized Variances for Associated Autoregressive and Moving Average Processes.
A Proof of Asymptotic Normality for some VARX Models.
Time Series with Approximated Beta Marginal
A recursive nonparametric estimator for the transition kernel of a piecewise-deterministic Markov process
In this paper, we investigate a nonparametric approach to provide a recursive estimator of the transition density of a piecewise-deterministic Markov process, from only one observation of the path within a long time. In this framework, we do not observe a Markov chain with transition kernel of interest. Fortunately, one may write the transition density of interest as the ratio of the invariant distributions of two embedded chains of the process. Our method consists in estimating these invariant...
A recursive robust Bayesian estimation in partially observed financial market
I propose a nonlinear Bayesian methodology to estimate the latent states which are partially observed in financial market. The distinguishable character of my methodology is that the recursive Bayesian estimation can be represented by some deterministic partial differential equation (PDE) (or evolution equation in the general case) parameterized by the underlying observation path. Unlike the traditional stochastic filtering equation, this dynamical representation is continuously dependent on the...
A remark on models of Box-Jenkins type
A Remark on Robustness of Linear Best Estimates.
A scalarization technique for computing the power and exponential moments of Gaussian random matrices.
A sharp analysis on the asymptotic behavior of the Durbin–Watson statistic for the first-order autoregressive process
The purpose of this paper is to provide a sharp analysis on the asymptotic behavior of the Durbin–Watson statistic. We focus our attention on the first-order autoregressive process where the driven noise is also given by a first-order autoregressive process. We establish the almost sure convergence and the asymptotic normality for both the least squares estimator of the unknown parameter of the autoregressive process as well as for the serial correlation estimator associated with the driven noise....