In the application of the popular maximum likelihood method to factor analysis, the number of factors is commonly determined through a two-stage procedure, in which stage 1 performs parameter estimation for a set of candidate models and then stage 2 chooses the best according to certain model selection criterion. Usually, to obtain satisfactory performance, a large set of candidates is used and...

With the advent of effective medical and surgical therapy for pulmonary and systemic thromboembolic disease, it has become essential for the radiologist to diagnose this disease in the lung as rapidly and accurately as possible. The appearance of pulmonary emboll and thrombi on the x-ray film is varied, and in this paper an attempt will be made to review the roentgenographlc appearance in 25 ca...

Nonclinical in vivo animal studies have to be completed before starting clinical studies of the pharmacokinetic behavior of a drug in humans. The drug exposure in animal studies is often measured by the area under the concentration versus time curve (AUC). The classical complete data design where each animal is sampled for analysis at every time point is applicable for large animals only. In th...

We present a general result giving us families of incomplete and boundedly complete families of discrete distributions. For such families, the classes of unbiased estimators of zero with finite variance and of parametric functions which will have uniformly minimum variance unbiased estimators with finite variance are explicitly characterized. The general result allows us to construct a large nu...

Real-world network datasets often contain a wealth of complex topological information. In the face of these data, researchers often employ methods to extract reduced networks containing the most important structures or pathways, sometimes known as ‘skeletons’ or ‘backbones’. Numerous such methods have been developed. Yet data are often noisy or incomplete, with unknown numbers of missing or spu...

The Bayesian network formalism is becoming increasingly popular in many areas such as decision aid, diagnosis and complex systems control, in particular thanks to its inference capabilities, evenwhen data are incomplete. Besides, estimating the parameters of a fixed-structure Bayesian network is easy. However, very few methods are capable of using incomplete cases as a base to determine the str...

We present an efficient method for learning Bayesian network models and parameters from incomplete data. With our approach an approximation is obtained of the predictive distribution. By way of this distribution any learning algorithm that works for complete data can be easily adapted to work for incomplete data as well. Our method exploits the dependence relations between the variables explici...