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We present an EM algorithm for Maximum Likelihood estimation of the location, scale, and skew, and shape parameters of the z distribution, also known as the generalized logistic function (type IV). We use the Barndorff-Nielsen, Kent, and Sørensen representation of the z distribution as a Gaussian location-scale mixture to derive an EM algorithm for estimating the location, scale, skew, and shape parameters...
We present an EM algorithm for Maximum Likelihood (ML) estimation of the location, structure matrix, skew or drift, and shape parameters of Barndorff-Nielsen's Generalized Hyperbolic distribution, which is the Gaussian Location Scale mixture (or Normal Variance Mean Mixture) with Generalized Inverse Gaussian (GIG) scale mixing distribution. We use the GLSM representation along with the closed form...
Interest in risk measurement for spot price has increased since the worldwide deregulation and liberalization of electricity started in the early 90's. This paper is focused on quantifying risk for the Nordic Power Exchange (Nord Pool) system price. Our analysis is based on a generalized autoregressive conditional heteroskedastic (GARCH) process with skewed exponential power innovations to model the...
In this paper, a calibration method for a triaxial accelerometer using a triaxial gyroscope is presented. The method uses a sensor fusion approach, combining the information from the accelerometers and gyroscopes to find an optimal calibration using Maximum likelihood. The method has been tested by using real sensors in smart-phones to perform orientation estimation and verified through Monte Carlo...
The estimation of sinusoidal signals is a very well researched area, and it is well known that two signals can be resolved well for frequency separation below the Fourier resolution at high enough signal to noise ratio. However, in the case of many closely spaced sinusoids estimation is impaired for separations well above the Fourier resolution, and the dependence on signal to noise ratio is involved...
Locally Stationary Wavelet processes provide a flexible way of describing the time/space evolution of autocovariance structure over an ordered field such as an image/time-series. Classically, estimation of such models assume continuous smoothness of the underlying spectra and are estimated via local kernel smoothers. We propose a new model which permits spectral jumps, and suggest a regularised estimator...
As higher-order datasets become more common, researchers are primarily focused on how to analyze and compress them. However, the most common challenge encountered in any type of data, including tensor data, is noise. Furthermore, the methods developed for denoising vector or matrix type datasets cannot be applied directly to higherorder datasets. This motivates the development of denoising methods...
The Adaptive Compressive Outlier Sensing (ACOS) method, proposed recently in (Li & Haupt, 2015), is a randomized sequential sampling and inference method designed to locate column outliers in large, otherwise low rank, matrices. While the original ACOS established conditions on the sample complexity (i.e., the number of scalar linear measurements) sufficient to enable accurate outlier localization...
An approach of regularizing Tyler's robust M-estimator of the co-variance matrix is proposed. We also provide an automatic choice of the regularization parameter in the high-dimensional regime. Simulations show its advantage over the sample covariance estimator and Tyler's M-estimator when data is heavy-tailed and the number of samples is small. Compared with the previous approaches of regularizing...
A combination of the hierarchical Dirichlet process (HDP) and the Potts model is proposed for the joint segmentation/classification of a set of images with shared classes. Images are first divided into homogeneous regions that are assumed to belong to the same class when sharing common characteristics. Simultaneously, the Potts model favors configurations defined by neighboring pixels belonging to...
Based on the Ziv-Zakai methodology to bound estimators, we derived an estimation bound able to predict the mean square error degradation due to model mismatches. In this article, we build upon this result to provide a performance comparison between mean and median estimators in the presence of outliers. The latter is well known to be statistically more robust than the mean in the presence of outliers...
This article proposes a first theoretical performance analysis of the training phase of large dimensional linear echo-state networks. This analysis is based on advanced methods of random matrix theory. The results provide some new insights on the core features of such networks, thereby helping the practitioner when using them.
In response to the demand on data-analytic tools that monitor time-varying connectivity patterns within brain networks, the present paper introduces a framework for clustering (unsupervised learning) of dynamically evolving connectivity states of networks. This work advocates learning of network dynamics on Riemannian manifolds, capitalizing on the well-known fact that popular features in statistics...
Identifying arbitrary topologies of power networks is a computationally hard problem due to the number of hypotheses that grows exponentially with the network size. A new variational inference approach is developed for efficient marginal inference of every line status in the network. Optimizing the variational model is transformed to and solved as a discriminative learning problem. A major advantage...
Data-injection attacks on spatial field detection corrupt a subset of measurements to cause erroneous decisions. We consider a centralized decision scheme exploiting spatial field smoothness to overcome lack of knowledge on system parameters such as noise variance. We obtain closed-form expressions for system performance and investigate strategies for an intruder injecting false data in a fraction...
Orthostatic intolerance (OI) is a clinical syndrome, which is characterized by symptoms and loss of consciousness before impeding syncope and that it has been reported that is caused by orthostatic hypotension (OH). The phenomenon of irreversibility is specific for non-equilibrium systems and is presented in the complexity of cardiovascular system control signals. This study is focused to quantify...
The distances between DNA Transcription Regulatory Elements (TRE) provide important clues to their dependencies and function within the gene regulation process. However, the locations of those TREs as well as their cross distances between occurrences are stochastic, in part due to the inherent limitations of Next Generation Sequencing methods used to localize them, in part due to biology itself. This...
Line outage detection and localization play pivotal roles in contingency analysis, power flow optimization, and situational awareness delivery in power grids. Hence, agile detection and localization of line outages enhance the efficiency of operations and their resilience against cascading failures. This paper proposes a stochastic graphical framework for localizing line outage events. This framework...
We solve a communication problem between a UAV and a set of relays, in the presence of a jamming UAV, using differential game theory tools. The standard solution involves a set of coupled Bellman equations which are hard to solve. We propose a new approach in which this kind of games can be approximated as pursuit-evasion games. The problem is posed in terms of optimizing capacity and it is approximated...
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