The Box-Jenkins approach to time series analysis
The Box-Jenkins approach to time series analysis and forecasting : principles and applications
The characterization of efficient sequential plans for the Ornstein-Uhlenbeck velocity process
The cross-validation method in the polynomial regression.
The Effect of Parameter Estimation Errors on the Variance of Forecasts
The effects of series on cointegration inference.
The efficiency of estimates in stationary autoregressive series
The empirical TES methodology: Modeling empirical time series.
The estimation of product standard time by artificial neural networks in the molding industry.
The Exact Likelihood Function for a Space Time Model.
The expected cumulative operational time for finite semi-Markov systems and estimation
In this paper we, firstly, present a recursive formula of the empirical estimator of the semi-Markov kernel. Then a non-parametric estimator of the expected cumulative operational time for semi-Markov systems is proposed. The asymptotic properties of this estimator, as the uniform strongly consistency and normality are given. As an illustration example, we give a numerical application.
The failure of orthogonality under nonstationarity: should we care about it?
The finite automata approaches in stringology
We present an overview of four approaches of the finite automata use in stringology: deterministic finite automaton, deterministic simulation of nondeterministic finite automaton, finite automaton as a model of computation, and compositions of finite automata solutions. We also show how the finite automata can process strings build over more complex alphabet than just single symbols (degenerate symbols, strings, variables).
The Invertibility of Sampled and Aggregated ARM A Models.
The Kolmogorov-Smirnov distance as criterion of choice of estimators with application to the first order auto-regressive case : a Monte Carlo study
The likelihood ratio test for the number of components in a mixture with Markov regime
The likelihood ratio test for the number of components in a mixture with Markov regime
We study the LRT statistic for testing a single population i.i.d. model against a mixture of two populations with Markov regime. We prove that the LRT statistic converges to infinity in probability as the number of observations tends to infinity. This is a consequence of a convergence result of the LRT statistic for a subproblem where the parameters are restricted to a subset of the whole parameter set.
The lower variance bound of the disorder moment estimator for the negative binomial and Poisson processes
The optimum sequential test of a finite number of hypotheses for statistically dependent observations
The ordering of experimental designs. A Hilbert space approach