Central problems in the eld of computer vision are learning object models from examples, classiication, and localization of objects. In this paper we will motivate the use of a classical statistical approach to deal with these problems: the missing information principle. Based on this general technique we derive the Expectation Maximization algorithm and deduce statistical methods for learning ...

The unified hybrid censoring is a mixture of generalized Type-I and Type-II hybrid censoring schemes. This article presents the statistical inferences on Generalized Exponential Distribution parameters when the data are obtained from the unified hybrid censoring scheme. It is observed that the maximum likelihood estimators can not be derived in closed form. The EM algorithm for computing the ma...

This paper presents the statistical inference on the parameters of the Burr type III distribution, when the data are Type-II hybrid censored. The maximum likelihood estimators are developed for the unknown parameters using the EM algorithm method. We provided the observed Fisher information matrix using the missing information principle which is useful for constructing the asymptotic confidence...

An experimental test of the descriptive adequacy of the restated diversification principle is presented. The principle postulates that risk-averse utility maximizers will pool risks for their mutual benefit, even if information is missing about the probabilities of losses. It is enough for people to assume that they face equal risks when they pool risks. The results of the experiment support th...

In this article, Lindley's measure of average information is used to measure the information contained in incomplete observations on the vector of unknown regression coefficients [9]. This measure of information may be used to compute the missing regressor values.