Die Familienverhältnisse der Privatangestellten in höheren Diensten in der Čechoslovakischen Republik
Die Geschwindigkeit der Glivenko-Cantelli-Konvergenz gemessen in der Prohorov-Metrik.
Die messende Prüfung bei Abweichung von der Normalverteilungsannahme.
Die Nichtkonsistenz der M.-L. Schätzer im Â?Switching Regression" Problem.
Die Schätzung des linearen Todesprozesses bei fehlerbehafteten Beobachtungen.
Die Verteilung der Gestorbenen um das Normalalter
Die wirtschaftlichste Verteilung des Prüfaufwandes auf die zwei Stufen eines Modells
Die zeichnerische Behandlung von Plänen für messende Prüfung.
Differences and Derivatives in Kernel Estimation.
Different approaches to weighted voting systems based on preferential positions
Voting systems produce an aggregated result of the individual preferences of the voters. In many cases the aggregated collective preference – the result of the voting procedure – mirrors much more than anything else the characteristics of the voting systems. Preferential voting systems work most of the time with equidistant differences between the adjacent preferences of an individual voter. They produce, as voting systems usually do, some paradoxical results under special circumstances. However,...
Different kinds of sufficiency in the general Gauss-Markov model
Differentiability of excessive functions of one-dimensional diffusions and the principle of smooth fit
The principle of smooth fit is probably the most used tool to find solutions to optimal stopping problems of one-dimensional diffusions. It is important, e.g., in financial mathematical applications to understand in which kind of models and problems smooth fit can fail. In this paper we connect-in case of one-dimensional diffusions-the validity of smooth fit and the differentiability of excessive functions. The basic tool to derive the results is the representation theory of excessive functions;...
Differentiability Properties of the s-Mean mg at s = 1
Differentiated shift-invariant multivariate integral operators.
Diffusions with measurement errors. I. Local asymptotic normality
We consider a diffusion process which is observed at times for , each observation being subject to a measurement error. All errors are independent and centered gaussian with known variance . There is an unknown parameter within the diffusion coefficient, to be estimated. In this first paper the case when is indeed a gaussian martingale is examined: we can prove that the LAN property holds under quite weak smoothness assumptions, with an explicit limiting Fisher information. What is perhaps...
Diffusions with measurement errors. I. Local Asymptotic Normality
We consider a diffusion process X which is observed at times i/n for i = 0,1,...,n, each observation being subject to a measurement error. All errors are independent and centered Gaussian with known variance pn. There is an unknown parameter within the diffusion coefficient, to be estimated. In this first paper the case when X is indeed a Gaussian martingale is examined: we can prove that the LAN property holds under quite weak smoothness assumptions, with an explicit limiting Fisher information....
Diffusions with measurement errors. II. Optimal estimators
We consider a diffusion process which is observed at times for , each observation being subject to a measurement error. All errors are independent and centered gaussian with known variance . There is an unknown parameter to estimate within the diffusion coefficient. In this second paper we construct estimators which are asymptotically optimal when the process is a gaussian martingale, and we conjecture that they are also optimal in the general case.
Diffusions with measurement errors. II. Optimal estimators
We consider a diffusion process X which is observed at times i/n for i = 0,1,...,n, each observation being subject to a measurement error. All errors are independent and centered Gaussian with known variance pn. There is an unknown parameter to estimate within the diffusion coefficient. In this second paper we construct estimators which are asymptotically optimal when the process X is a Gaussian martingale, and we conjecture that they are also optimal in the general case.
Dimensionalidad intrínseca de un conjunto de palabras aisladas.
La Dimensionalidad Intrínseca (DI) de un conjunto de objetos hace referencia al número mínimo de parámetros necesarios para la "generación" de dichos objetos. La estimación de la DI aplicada al caso de las pronunciaciones de palabras presenta interés porque permite obtener una medida escalar conveniente de la dificultad de las distintas tareas de Reconocimiento de Palabras Aisladas. Debido a que el conjunto de objetos a tratar (palabras pronunciadas) no presenta estructura de espacio vectorial,...
Directional Analysis of Fibre Processes Related to Boolean Models.