The problem of forming images that are optimal with respect to a Mean Square Error (MSE) criterion, based on nite data, is considered. First, it is shown that the MSE criterion is consistent with the general goal of classifying images, in that decreasing the MSE guarantees a decrease in the probability of misclassifying an image. The problem of choosing sampling locations for image formation th...

Five classification algorithms namely J48, Naive Bayes, Multilayer Perceptron, IBK and Bayes Net are evaluated using Mc Nemar’s test over datasets including both nominal and numeric attributes. It was found that Multilayer Perceptron performed better than the two other classification methods for both nominal and numerical datasets. Furthermore, it was observed that the results of our evaluation...

Maximum likelihood methods are by far the most popular methods for deriving statistical estimators. However, parametric likelihoods require distributional specifications. The empirical likelihood is a nonparametric likelihood function that does not require such distributional assumptions, but is otherwise analogous to its parametric counterpart. Both likelihoods assume that the random variables...

Like glaciers throughout the world, tropical glaciers in the northern Andes have retreated during the last century. In this study, the retreat of Andean glaciers in Colombian and Venezuela has been mapped from Landsat and ASTER images acquired between 1984 and 2004. Glacier retreat has been mapped for three glaciated regions in Columbia, the Sierra Nevada de Santa Marta, the Sierra Nevada del C...

Missing data are ubiquitous in psychological research. They may come about as an unwanted result of coding or computer error, participants' non-response absence, missing values be intentional, planned designs. We discuss the effects on χ²-based goodness-of-fit indices Structural Equation Modeling (SEM), specifically Root Mean Squared Error Approximat...

We consider the best quadratic unbiased estimators of the integrated variance in the presence of independent market microstructure noise. We establish the asymptotic normality of a feasible best quadratic unbiased estimator under the assumption of constant volatility and show that it is asymptotically e cient when the market microstructure noise is normal. Since the class of quadratic estimator...