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In this paper we describe a general probabilis-tic framework for modeling waveforms such as heartbeats from ECGdata. The model is based on segmental hidden Markov mod-els(as usedin speechrecognition) ...
In this paper we describe a general probabilis-tic framework for modeling waveforms such as heartbeats from ECGdata. The model is based on segmental hidden Markov mod-els(as usedin speechrecognition) ...
The random networks enriched with additional structures asmetric and group-symmetry in background metric space are investigated. The important quantities like he clustering coefficient as well as the ...
In this paper we study the problem of estimating the mixing coefficients between two random vari-ables. Three different mixing coefficients are studied,namely alpha-mixing, beta-mixing and phi-mixing ...
In this thesis we study adaptive nonparametric regression with noise misspecifi-cation and the complexity of approximation of random fields in dependence of the dimension. First, we consider the prob...
This paper studies polar sets of anisotropic Gaussian random fields,i.e. sets which a Gaussian random field does not hit almost surely. The main assumptions are that the eigenvalues of the covariance ...
Random graphs, where the connections between nodes are considered random variables, have wide applicability in the social sciences. Exponential-family Random Graph Models (ERGM) have shown themselves ...
In Bayesian theory, calculating a posterior probability distribution is highly important but usually difficult. Therefore, some methods have been put forward to deal with such problem, among which, th...
Our problem is to nd a good approximation to the P-value of the maximum of a random eld of test statistics for a cone alternative at each point in a sample of Gaussian random elds. These test stati...
Many statistical data are imprecise due to factors such as mea-surement errors, computation errors, and lack of information. In such cases, data are better represented by intervals rather thanby singl...
A general stochastic model is developed for the total interference in wideband systems, denoted as the PNSC(α) Interference Model. It allows one to obtain, analytic representations in situations where...
In array processing, a common problem is to estimate the angles of arrival of $K$ deterministic sources impinging on an array of $M$ antennas, from $N$ observations of the source signal, corrupted by ...
This paper deals with the problem of parameter estimation based on certain eigenspaces of the empirical covariance matrix of an observed multidimensional time series, in the case where the time series...
I met Peter J. Bickel for the first time in 1981. He came to Jerusalem for a year; I had just started working on my Ph.D. studies.
In this paper we propose a new robust estimation method based on random projections which is adaptive, produces an automatic robust estimate, while being easy to compute for high or infinite dimension...

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