We present a Bayesian method for estimating small area parameters under an inverse Gaussian model. The method is extended to estimate small area parameters for finite populations. The Gibbs sampler is proposed as a mechanism for implementing the Bayesian paradigm. We illustrate the method by application to household income survey data, comparing it against the usual lognormal model for positive...

A general framework is developed to treat inverse problems with parameters that are random fields. It involves a sampling method that exploits the sensitivity derivatives of the control variable with respect to the random parameters. As the sensitivity derivatives are computed only at the mean values of the relevant parameters, the related extra cost of the present method is a fraction of the t...

A new numerical procedure is proposed for the reconstruction of the shape and volume of unknown objects from measurements of their radiation in the far field. This procedure is a variant and the linear sampling method and has a very acceptable computational load and is fully automated. It is based on exploiting an iteratively computed truncated singular-value decomposition and heuristics to ext...

We discuss some known and some new results on the score function (SF) approach for simulation analysis. We show that while simulating a single sample path from the underlying system or from an associated system and applying the Radon-Nikodym measure one can: estimate the performance sensikGries (gradient, Hessian etc.) of the underlying system with respect to some parameter (vector of parameter...

We propose an algorithm for generating normal random variates that is based on the acceptance–rejection method and uses a piecewise majorizing function. The piecewise function has 2048 equal-area pieces, 2046 of which are constant, and the two extreme pieces are curves that majorize the tails. The proposed algorithm has not only good performance from correlation induction perspective, but also ...

It is common practice to use weighting, poststratiication, and raking to correct for sampling and nonsampling biases and to improve eeciency of estimation in sample surveys. However, there is no standard method for computing sampling variances of estimates that use these adjustments in combination. In this paper we develop such a method, using three ideas: (1) a general notation that uniies the...

Due to event‐triggered sampling in a system, or maybe with the aim of reducing data storage, tracking many applications will encounter irregular sampling time. By calculating the matrix exponential using an inverse Laplace transform, this paper transforms the irregular sampling tracking problem to the problem of tracking with time‐varying parameters of a system. ...

Title of dissertation: TENSOR COMPLETION FOR MULTIDIMENSIONAL INVERSE PROBLEMS WITH APPLICATIONS TO MAGNETIC RESONANCE RELAXOMETRY Ariel Hafftka, Doctor of Philosophy, 2016 Dissertation directed by: Professor Wojciech Czaja Department of Mathematics This thesis deals with tensor completion for the solution of multidimensional inverse problems. We study the problem of reconstructing an approxima...