An arbitrary source function cannot be determined fully from projection data that are limited in number and range of viewing angle. There exists a null subspace in the Hilbert space of possible source functions about which the available projection measurements provide no information. The null-space components of deterministic solutions are usually zero, giving rise to unavoidable artifacts. It ...

Inference about dependencies in a multiway data array can be made using the array normal model, which corresponds to the class of multivariate normal distributions with separable covariance matrices. Maximum likelihood and Bayesian methods for inference in the array normal model have appeared in the literature, but there have not been any results concerning the optimality properties of such est...

For a wireless sensor network (WSN) with a large number of low-cost, battery-driven, multiple transmission power leveled sensor nodes of limited transmission bandwidth, then conservation of transmission resources (power and bandwidth) is of paramount importance. Towards this end, this paper considers the problem of power scheduling of Kalman filtering for general linear stochastic systems subje...

High dimensional covariance estimation is known to be a difficult problem, has many applications and is of current interest to the larger statistical community. We consider the problem of estimating the covariance matrix of a multivariate normal distribution in the “large p small n” setting. Several approaches to high dimensional covariance estimation have been proposed in the literature. In ma...

Title of Dissertation: Nonparametric Quasi-likelihood in Longitudinal Data Analysis Xiaoping Jiang, Doctor of Philosophy, 2004 Dissertation directed by: Professor Paul J. Smith Statistics Program Department of Mathematics This dissertation proposes a nonparametric quasi-likelihood approach to estimate regression coefficients in the class of generalized linear regression models for longitudinal ...

Multisensor distributed information fusion white noise wiener deconvolution estimator is presented in this paper. The algorithm is using the modern time series analysis method and white noise estimator under the linear minimum variance optimal fusion criterion. Gevers-Wouters (G-W) algorithm are also used in this paper. This paper presents information fusion algorithm including scalar weighted ...

Estimating the eigenvalues of a population covariance matrix from a sample covariance matrix is a problem of fundamental importance in multivariate statistics; the eigenvalues of covariance matrices play a key role in many widely techniques, in particular in Principal Component Analysis (PCA). In many modern data analysis problems, statisticians are faced with large datasets where the sample si...

We propose a recursive generalized total least-squares (RGTLS) estimator that is used in parallel with a noise covariance estimator (NCE) to solve the errors-in-variables problem for multi-input-single-output linear systems with unknown noise covariance matrix. Simulation experiments show that the suggested RGTLS with NCE procedure outperforms the common recursive least squares (RLS) and recurs...