International Telemetering Conference Proceedings / October 20-23, 2003 / Riviera Hotel and Convention Center, Las Vegas, Nevada / Multiple antennas promise high data capacity for wireless communications. Most space-time coding schemes in literature focus on the rich scatter environment. In this paper, we argue that minimax criterion is a good design criterion for space-time codes over the avionic telemetry channels. This design criterion is different than those of space-time codes over rich scattering Rayleigh fading channels. Theoretical and numerical results show that the codes with optimal performance in Rayleigh fading channels do not necessarily have optimal performance in avionic telemetry channels. Therefore, the space-time codes should be carefully designed/selected when used in the avionic telemetry channels.

Markovo proceso tikimybinio mato absoliutaus tolydumo nesudėtingos sąlygos leidžia gauti atitinkamų statistinių eksperimentų tikėtinumo santykio pavidalą, kurio asimptotinės savybės susijusios su dviejų paprastų hipotezių asimptotiniais atskyrimo uždaviniais, kai yra taikomas maksimalaus tikėtinumo arba minimakso kriterijus. Tų paprastų hipotezių asimptotinis atskyrimas yra charakterizuojamas 1-os ir 2-os rūšies klaidos tikimybėmis, kurių asimptotinis elgesys priklausomai nuo optimalaus statistinio kriterijaus parinkimo užsirašo dvejomis formulėmis. Maksimalaus kriterijaus atveju tokia formulė buvo gauta bendriausiu atveju, tik nebuvo pritaikyta Markovo procesui su dideliu būsenų skaičiumi. Šiame darbe kaip tik parodyti šie taikymai. Taikant maksimalaus tikėtinumo kriterijų (Neimono-Pirsono) atitinkamas rezultatas buvo gautas tik tuo atveju, kai stebėjimai yra nepriklausomi ir vienodai pasiskirstę. Analogiškas rezultatas gautas bendriausiu atveju – gautos sąlygos, kada galioja atitinkama asimptotinė formulė. Kartu, pavyzdžiuose yra parodyti šios asimptotinės formulės taikymai, kai stebimas Markovo procesas su dideliu būsenų skaičiumi. / Absolute continuity simple conditions of probabilistic measure of Markov process allows you to get relevand statistical experiments likelihood ratio form, which asymptotic properties is associated with the asymptotic separation of the two simple hypotheses tasks, when is applied maximum likelihood (Neiman-Pirson) or minimax criterion. That asymptotic separation of the two simple hypothesis is characterized by type I and type II errors of probability, which asymptotic behavior depending on the optimal statistical criterion selection note down by two formulas. In maximum likelihood criterion case, formula was obtained on a very general case, not only been applied of the Markov process with a large number of states. These applications are shown at this work. Using maximum likelihood criterion (Neiman-Pirson) corresponding result was obtained only in that case, when observations are independent and identically distributed. Analogous result were obtained on a very general case – from conditions, when is valid the asymptotic formula. In examples of this work are shown that asymptotic formula applications, when is observed Markov process with a large number of states.

Le problème d'intérêt est d'estimer la fonction de concentration et l'aire sous la courbe (AUC) à travers l'estimation des paramètres d'un modèle de régression linéaire avec un processus d'erreur autocorrélé. On construit un estimateur linéaire sans biais simple de la courbe de concentration et de l'AUC. On montre que cet estimateur construit à partir d'un plan d'échantillonnage régulier approprié est asymptotiquement optimal dans le sens où il a exactement la même performance asymptotique que le meilleur estimateur linéaire sans biais (BLUE). De plus, on montre que le plan d'échantillonnage optimal est robuste par rapport à la misspecification de la fonction d'autocovariance suivant le critère du minimax. Lorsque des observations répétées sont disponibles, cet estimateur est consistant et a une distribution asymptotique normale. Les résultats obtenus sont généralisés au processus d'erreur de Hölder d'indice compris entre 0 et 2. Enfin, pour des tailles d'échantillonnage petites, un algorithme de recuit simulé est appliqué à un modèle pharmacocinétique avec des erreurs corrélées. / The problem of interest is to estimate the concentration curve and the area under the curve (AUC) by estimating the parameters of a linear regression model with autocorrelated error process. We construct a simple linear unbiased estimator of the concentration curve and the AUC. We show that this estimator constructed from a sampling design generated by an appropriate density is asymptotically optimal in the sense that it has exactly the same asymptotic performance as the best linear unbiased estimator (BLUE). Moreover, we prove that the optimal design is robust with respect to a misspecification of the autocovariance function according to a minimax criterion. When repeated observations are available, this estimator is consistent and has an asymptotic normal distribution. All those results are extended to the error process of Hölder with index including between 0 and 2. Finally, for small sample sizes, a simulated annealing algorithm is applied to a pharmacokinetic model with correlated errors.