O teorii strategických her
On stationarity of a multiple doubly stochastic model
On residual analysis for time series models
AR(1) processes with given moments of marginal distribution
Nonnegative multivariate processes
On extrapolation in multiple ARMA processes
Long memory time series models
Bayesian analysis of the model of hidden periodicities
Linear approximations to some non-linear AR(1) processes
Some methods for approximating non-linear AR(1) processes by classical linear AR(1) models are proposed. The quality of approximation is studied in special non-linear AR(1) models by means of comparisons of quality of extrapolation and interpolation in the original models and in their approximations. It is assumed that the white noise has either rectangular or exponential distribution.
On non-nested regression models
A generalization of a test for non-nested models in linear regression is derived for the case when there are several regression models with more regressors.
On multiple normal probabilities of rectangles
Iterative solution of the best linear extrapolation problem in multidimensional stationary random sequences
An iterative method for linear extrapolation of twodimensional random sequences is derived. Steps of this procedure are based (i) on Jaglom’s method, (ii) on Hájek’s method. A numerical example is given in the both cases. Finally the iterative method is generalized for the - dimensional case.
On evaluation of some two-dimensional normal probabilities
The most significant interaction in a contingency table
The Bayes approach in multiple autoregressive series
The efficiency of estimates in stationary autoregressive series
On the Bayes approach in general multiple autoregressive series
On interactions in contingency tables
Periodic autoregression with exogenous variables and periodic variances
The periodic autoregressive process with non-vanishing mean and with exogenous variables is investigated in the paper. It is assumed that the model has also periodic variances. The statistical analysis is based on the Bayes approach with a vague prior density. Estimators of the parameters and asymptotic tests of hypotheses are derived.
Statistical analysis of periodic autoregression
Methods for estimating parameters and testing hypotheses in a periodic autoregression are investigated in the paper. The parameters of the model are supposed to be random variables with a vague prior density. The innovation process can have either constant or periodically changing variances. Theoretical results are demonstrated on two simulated series and on two sets of real data.
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