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Modeling Waveform Shapes with Random Eects Segmental Hidden Markov Models
Modeling Waveform Shapes Random Eects Segmental Hidden Markov Models
2012/9/19
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) ...
Modeling Waveform Shapes with Random Eects Segmental Hidden Markov Models
Modeling Waveform Shapes Random Eects Segmental Hidden Markov Models
2012/9/19
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) ...
On computation of clustering coefficient in a class of random networks
random graph clustering degree of separation
2012/9/18
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 ...
Mixing Coefficients Between Discrete and Real Random Variables: Computation and Properties
Mixing Coefficients Between Discrete Real Random Variables Computation Properties
2012/9/17
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 ...
Adaptive estimation in regression and complexity of approximation of random fields
regression and complexity approximation random fields
2012/9/17
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...
Polar sets of anisotropic Gaussian random fields
Anisotropic Gaussian fields Hitting probabilities Polar sets Hausdorff dimension European option Jump diffusion Calibration
2012/9/18
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 ...
A Random Weighting Approach for Posterior Distributions
posterior distribution asymptotic expansion random weighting method
2012/9/19
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...
Detecting sparse cone alternatives for Gaussian random fields, with an application to fMRI
random elds Euler characteristic kinematic formulae volumes of tubes expansion order-restricted inference multivari-ate one-sided hypotheses non-negative least squares.
2012/9/19
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...
A Normal Hierarchical Model and Minimum Contrast Estimation for Random Intervals
random intervals Normality hierarchical Choquet functional minimum contrast estimator strong consistency asymptotic normality.
2012/9/19
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...
Generalized Interference Models in Doubly Stochastic Poisson Random Fields for Wideband Communications: the PNSC(alpha) model
Interference models Cox Process Doubly Stochastic Poisson Stable Process Isotropicα-stable Complexα-stable
2012/9/19
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...
Large information plus noise random matrix models and consistent subspace estimation in large sensor networks
Large information plus noise random matrix models consistent subspace estimation large sensor networks
2011/7/7
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 ...
A Subspace Estimator for Fixed Rank Perturbations of Large Random Matrices
Large Random Matrix Theory MUSIC Algorithm Extreme Eigenvalues
2011/7/6
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...
A Random Walk with Drift: Interview with Peter J. Bickel
Random Walk Interview Peter J. Bickel
2011/7/5
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.
Resistant estimates for high dimensional and functional data based on random projections
Resistant estimates high dimensional functional data based
2011/7/5
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...