A number of statistics that arise in time series analysis can be represented as the sum of a partial realization of a possibly serially dependent and nonstationary discrete-parameter stochastic process. The almost sure and $L_p, p > 1$, convergence of such statistics is investigated, under various moment conditions. The results are applied to the least squares estimates of multiple regressions
Change-point analysis is devoted to the detection and estimation of the time of structural changes w...
AbstractConsider the following Itô stochastic differential equation dX(t) = ƒ(θ0, X(t)) dt + dW(t), ...
Kernel estimators of conditional expectations and joint probability densities are studied in the con...
AbstractA recent theorem of T. L. Hai, H. Robbins, and C. Z. Wei (J. Multivariate Anal. 9 (1979), 34...
Introduction Chapter I. Time Series 1.1 Sample of a stochastic process 1.2 Stationarity and trend of...
AbstractMaximum likelihood and approximate maximum likelihood estimates of parameters of random proc...
A vector autoregression with deterministic terms with no restrictions to its characteristic roots is...
A vector autoregression with deterministic terms and with no restrictions to its characteristic root...
AbstractThis paper establishes several almost sure asymptotic properties of general autoregressive p...
A vector autoregression with deterministic terms and with no restrictions to its characteristic root...
This paper analyzes the consistency properties of classical estimators for limited dependent variabl...
AbstractMultiple linear regression models with non random regressors in continuous time are consider...
Almost sure convergence properties of least-squares estimates in stochastic regression models and an...
This paper considers a simulation-based estimator for a general class of Markovian processes and exp...
Some problems of' statistical inference for discrete-valued time series are investigated in this stu...
Change-point analysis is devoted to the detection and estimation of the time of structural changes w...
AbstractConsider the following Itô stochastic differential equation dX(t) = ƒ(θ0, X(t)) dt + dW(t), ...
Kernel estimators of conditional expectations and joint probability densities are studied in the con...
AbstractA recent theorem of T. L. Hai, H. Robbins, and C. Z. Wei (J. Multivariate Anal. 9 (1979), 34...
Introduction Chapter I. Time Series 1.1 Sample of a stochastic process 1.2 Stationarity and trend of...
AbstractMaximum likelihood and approximate maximum likelihood estimates of parameters of random proc...
A vector autoregression with deterministic terms with no restrictions to its characteristic roots is...
A vector autoregression with deterministic terms and with no restrictions to its characteristic root...
AbstractThis paper establishes several almost sure asymptotic properties of general autoregressive p...
A vector autoregression with deterministic terms and with no restrictions to its characteristic root...
This paper analyzes the consistency properties of classical estimators for limited dependent variabl...
AbstractMultiple linear regression models with non random regressors in continuous time are consider...
Almost sure convergence properties of least-squares estimates in stochastic regression models and an...
This paper considers a simulation-based estimator for a general class of Markovian processes and exp...
Some problems of' statistical inference for discrete-valued time series are investigated in this stu...
Change-point analysis is devoted to the detection and estimation of the time of structural changes w...
AbstractConsider the following Itô stochastic differential equation dX(t) = ƒ(θ0, X(t)) dt + dW(t), ...
Kernel estimators of conditional expectations and joint probability densities are studied in the con...