Maximum likelihood (ML) estimation of categorical multitrait-multimethod (MTMM) data is challenging because the likelihood involves high-dimensional integrals over the crossed method and trait factors, with no known closed-form solution. The purpose of the study is to introduce three newly developed ML methods that are eligible for estimating MTMM models with categorical responses: Variational ...

the aim of this study is to introduce a parametric mixture model to analysis the competing-risks data with two types of failure. in mixture context, i t h type of failure is i th component. the baseline failure time for the first and second types of failure are modeled as proportional hazard models according to weibull and gompertz distributions, respectively. the covariates affect on both the ...

We study maximum selection and sorting of n numbers using pairwise comparators that output the larger of their two inputs if the inputs are more than a given threshold apart, and output an adversariallychosen input otherwise. We consider two adversarial models. A non-adaptive adversary that decides on the outcomes in advance based solely on the inputs, and an adaptive adversary that can decide ...

This paper addresses the problem of estimating the parameters of a Bayesian network from incomplete data. This is a hard problem, which for computational reasons cannot be effectively tackled by a full Bayesian approach. The workaround is to search for the estimate with maximum posterior probability. This is usually done by selecting the highest posterior probability estimate among those found ...

The purpose of this paper is to give an overview on the use of the Expectation-Maximization (EM) algorithm in software reliability modeling. This algorithm is related to Maximum Likelihood Estimates (MLE) of parameters in a context of missing data. Different ways to implement this algorithm are highlighted for hidden Markov models in software reliability.

In this paper, we use a general mathematical and experimental methodology to analyze image deconvolution. The main procedure is to use an example image convolving it with a know Gaussian point spread function and then develop algorithms to recover the image. Observe the deconvolution process by adding Gaussian and Poisson noise at different signal to noise ratios. In addition, we will describe ...

A maximum likelihood blind source separation algorithm is developed. The temporal dependencies are explained by assuming that each source is an AR process and the distribution of the associated i.i.d. innovations process is described using a Mixture of Gaussians. Unlike most maximum likelihood methods the proposed algorithm takes into account both spatial and temporal information, optimization ...