In this paper we present a review of some results about inference based on φ-divergence measures, under assumptions of multinomial sampling and loglinear models. The minimum φ-divergence estimator, which is seen to be a generalization of the maximum likelihood estimator is considered. This estimator is used in a φdivergence measure which is the basis of new statistics for solving three importan...

A method is proposed for the detection of item bias with respect to observed or unobserved subgroups. The method uses quasi-loglinear models for the incomplete subgroup x testscore x item 1 x x itek k contingency table. If subgroup membership is unknown the models are Haberman's incomplete-latent-class models. The (conditional) Rasch model is formulated as a quasi-loglinear model. The parameter...

A method is proposed for the detection of item bias with respect to observed or unobserved subgroups. The method uses quasi-loglinear models for the incomplete subgroup x test score x Item 1 × • • • x item k contingency table. If subgroup membership is unknown the models are Haberman's incomplete-latent-class models. The (conditional) Rasch model is formulated as a quasi-loglinear model. The pa...

Evaluation of efficiency of each of the DMUs (Decision Making Units) in a company is a very important task. Thus, the studies of evaluation of efficiency are being actively carried out, based on production function. Until quite recently, the loglinear production function (the Cobb-Douglas function) has been used for evaluation purposes. The loglinear model evaluates the DMUs by measuring the av...

Veska Noncheva, Nuno Marques Abstract: Our approach for knowledge presentation is based on the idea of expert system shell. At first we will build a graph shell of both possible dependencies and possible actions. Then, reasoning by means of Loglinear models, we will activate some nodes and some directed links. In this way a Bayesian network and networks presenting loglinear models are generated.

In this paper we consider inference based on very general divergence measures, under assumptions of multinomial sampling and loglinear models. We define the minimum φ-divergence estimator, which is seen to be a generalization of the maximum likelihood estimator. This estimator is then used in a φ-divergence goodness-of-fit statistic, which is the basis of two new statistics for solving the prob...

Upper and lower bounds on cell counts in cross-classifications of nonnegative counts play important roles in a number of practical problems, including statistical disclosure limitation, computer tomography, mass transportation, cell suppression, and data swapping. Some features of the Frechet bounds are well known, intuitive, and regularly used by those working on disclosure limitation methods,...

Neural spike trains, which are sequences of very brief jumps in voltage across the cell membrane, were one of the motivating applications for the development of point process methodology. Early work required the assumption of stationarity, but contemporary experiments often use time-varying stimuli and produce time-varying neural responses. More recently, many statistical methods have been deve...