Moderate deviations for stable Markov chains and regression models.
Moderate deviations for the Durbin–Watson statistic related to the first-order autoregressive process
The purpose of this paper is to investigate moderate deviations for the Durbin–Watson statistic associated with the stable first-order autoregressive process where the driven noise is also given by a first-order autoregressive process. We first establish a moderate deviation principle 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. It enables us to provide a moderate deviation...
Moindres carrés pondérés et poursuite
Moyennes mobiles centrées et non-centrées. Construction et comparaison
MSE of the best linear predictor in nonorthogonal models
Multidimensional random processes with normal covariances
Multiscale behavior of a simple model for stock markets.
Multivariate smooth transition AR model with aggregation operators and application to exchange rates
An overview of multivariate modelling based on logistic and exponential smooth transition models with transition variable generated by aggregation operators and orders of auto and exogenous regression selected by information criterion separately for each regime is given. Model specification procedure is demonstrated on trivariate exchange rates time series. The application results show satisfactory improvement in fit when particular aggregation operators are used. Source code in the form of Mathematica...
New variance ratio tests to identify random walk from the general mean reversion model.
Non-negative linear processes
Conditions under which the linear process is non-negative are investigated in the paper. In the definition of the linear process a strict white noise is used. Explicit results are presented also for the models AR(1) and AR(2).
Nonnegative multivariate processes
Nonparametric density estimators based on nonstationary absolutely regular random sequences.
Nonparametric estimation of the ratios of derivatives of a multivariate distribution density from dependent observations.
Note on approximate non-Gaussian filtering with nonlinear observation relation
Note on inappropriate trend and seasonal elimination
On a class of estimators in a multivariate RCA(1) model
This work deals with a multivariate random coefficient autoregressive model (RCA) of the first order. A class of modified least-squares estimators of the parameters of the model, originally proposed by Schick for univariate first-order RCA models, is studied under more general conditions. Asymptotic behavior of such estimators is explored, and a lower bound for the asymptotic variance matrix of the estimator of the mean of random coefficient is established. Finite sample properties are demonstrated...
On a Criterion for the Selection of Models for Stationary Time Series.
On a method of estimating parameters in non-negative ARMA models
On a simple estimate of correlations of stationary random sequences
On a Szegö type limit theorem, the Hölder-Young-Brascamp-Lieb inequality, and the asymptotic theory of integrals and quadratic forms of stationary fields *
Many statistical applications require establishing central limit theorems for sums/integrals or for quadratic forms , where Xt is a stationary process. A particularly important case is that of Appell polynomials h(Xt) = Pm(Xt), h(Xt,Xs) = Pm,n (Xt,Xs), since the “Appell expansion rank" determines typically the type of central limit theorem satisfied by the functionals ST(h), QT(h). We review and extend here to multidimensional indices, along lines conjectured in [F. Avram and M.S. Taqqu,...