Detekce změn v časových řadách a její aplikace v ekologii
Determining the Parameters of Statistical Tests by Fuzzy Constraints.
Deux structures statistiques fondamentales en analyse de la variance univariée et multivariée
Deviation inequalities and moderate deviations for estimators of parameters in an Ornstein-Uhlenbeck process with linear drift.
Deviation inequalities and moderate deviations for estimators of parameters in bifurcating autoregressive models
The purpose of this paper is to investigate the deviation inequalities and the moderate deviation principle of the least squares estimators of the unknown parameters of general th-order asymmetric bifurcating autoregressive processes, under suitable assumptions on the driven noise of the process. Our investigation relies on the moderate deviation principle for martingales.
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.
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,...
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.
Directional Analysis of Fibre Processes Related to Boolean Models.
Discrete random processes with memory: Models and applications
The contribution focuses on Bernoulli-like random walks, where the past events significantly affect the walk's future development. The main concern of the paper is therefore the formulation of models describing the dependence of transition probabilities on the process history. Such an impact can be incorporated explicitly and transition probabilities modulated using a few parameters reflecting the current state of the walk as well as the information about the past path. The behavior of proposed...
Discrete sampling of an integrated diffusion process and parameter estimation of the diffusion coefficient
Discrete sampling of an integrated diffusion process and parameter estimation of the diffusion coefficient
Let (Xt) be a diffusion on the interval (l,r) and Δn a sequence of positive numbers tending to zero. We define Ji as the integral between iΔn and (i + 1)Δn of Xs. We give an approximation of the law of (J0,...,Jn-1) by means of a Euler scheme expansion for the process (Ji). In some special cases, an approximation by an explicit Gaussian ARMA(1,1) process is obtained. When Δn = n-1 we deduce from this expansion estimators of the diffusion coefficient of X based on (Ji). These estimators are shown...
Discretization problems on generalized entropies and -divergences
Discriminability of robust test under heavy contamination
Discriminating between causal structures in Bayesian Networks given partial observations
Given a fixed dependency graph that describes a Bayesian network of binary variables , our main result is a tight bound on the mutual information of an observed subset of the variables . Our bound depends on certain quantities that can be computed from the connective structure of the nodes in . Thus it allows to discriminate between different dependency graphs for a probability distribution, as we show from numerical experiments.