On the asymptotic behaviour of stationary gaussian processes
On the central limit problem for processes of zero entropy
On the computation of the exact distribution of power divergence test statistics
In this paper we introduce several algorithms to generate all the vectors in the support of a multinomial distribution. Computational studies are carried out to analyze their efficiency with respect to the CPU time and to calculate their efficiency frontiers. The proposed algorithm is used to calculate exact distributions of power divergence test statistics under the hypothesis of uniformity. Finally, several exact power comparisons are done for different divergence statistics and families of alternatives...
On The Decomposition Of Arbitrary Second Ordered Stochastic Process
On the existence of a class of stationary quantum stochastic processes
On the extremal behavior of sub-sampled solutions of stochastic difference equations.
On the functional central limit theorem for stationary processes
On the group pulse processes. II. Power spectrum of the processes with independent points
On the group pulse processes. III. Power spectrum of the processes with independent intervals
On the integrability of the ergodic maximal function
On the limit distributions of kth order statistics for semi-pareto processes
Asymptotic properties of the kth largest values for semi-Pareto processes are investigated. Conditions for convergence in distribution of the kth largest values are given. The obtained limit laws are represented in terms of a compound Poisson distribution.
On the linear prediction problem of certain non-stationary stochastic processes.
On the mode-change problem for random measures.
On the optimal strategy in a random game.
On the spectral representation of the sampling cardinal series expansion of weakly stationary stochastic processes.
The spectral representation of the sampling cardinal series expansion (SCSE) of non-band-limited weakly stationary scalar and vector stochastic processes and their correlation functions are derived. The upper bound of the mean-square aliasing error is given for vector processes.
On threshold autoregressive processes
On unequally spaced AR(1) process
Discrete autoregressive process of the first order is considered. The process is observed at unequally spaced time instants. Both least squares estimate and maximum likelihood estimate of the autocorrelation coefficient are analyzed. We show some dangers related with the estimates when the true value of the autocorrelation coefficient is small. Monte-Carlo method is used to illustrate the problems.