The question of the serial dependence of successive REM-NREM sleep cycles was examined. The experiments were performed in two different settings: 309 sleep episodes of 11 healthy young sleepers (age range, 20-36 years) were recorded under entrained conditions in the sleep laboratory; 5 of these subjects also slept in an isolation unit (underground apartment) with free-running sleep-wake cycles ...

We have developed a growth process that leads to the direct formation of self-assembled InAs quantum dots on InP 001 by solid-source molecular beam epitaxy avoiding the previous formation of quantum wires usually obtained by this technique. The process consists of a periodically alternated deposition of In and As correlated with InAs 4 2 ↔ 2 4 surface reconstruction changes. Based on the result...

The set of all positive definite periodic covariances that a linear discrete periodically time-varying plant with a fixed order controller may process is characterized in this paper. The controllers which assign such periodic covariance to any linear discrete periodically time-varying system are given explicitly in the closed form. The freedom in those covariance controllers is explicitly param...

This paper establishes the strong consistency and asymptotic normality of the quasi-maximum likelihood estimator (QMLE) for a GARCH process with periodically time-varying parameters. We first give a necessary and sufficient condition for the existence of a strictly periodically stationary solution for the periodic GARCH (P -GARCH) equation. As a result, it is shown that the moment of some posit...

We consider the autoregressive estimation for periodically correlated processes, using the parameterization given by the partial autocorrelation function. We propose an estimation of these parameters by extending the sample partial autocorrelation method to this situation. The comparison with other methods is made. Relationships with the stationary multivariate case are discussed.

We study estimation problems for periodically correlated, non gaussian processes. We estimate the correlation functions and the spectral densities from continuous-time samples. From a random time sample, we construct three types of estimators for the spectral densities and we prove their consistency.