We develop a new method for variable selection in nonparametric functional concurrent regression. The commonly used linear model (FLCM) is far too restrictive assuming linearity of the covariate effects, which not necessarily true many real-world applications. (NPFCM), on other hand, much more flexible and can capture complex dynamic relationships present between response covariates. extend cla...

in this paper we intend to improve the explanatory power of regressions when the deletion method is used for the remedy of multicolinearity. if one deletes the variable (s) that is (are) responsible for multicolinearity, he loses some information that is not common between the deleted variable (s) and the other remaining variables in the regression. to improve this method, we run the deleted va...

Relative importance of regressor variables is an old topic that still awaits a satisfactory solution. When interest is in attributing importance in linear regression, averaging over orderings methods for decomposing R2 are among the state-of-theart methods, although the mechanism behind their behavior is not (yet) completely understood. Random forests—a machinelearning tool for classification a...

We propose a latent variable approach for multiple clustering of categorical data. We use logistic regression models for the conditional distribution of observable features given the latent cluster variables. This model supports an interpretation of the different clusterings as representing distinct, independent factors that determine the distribution of the observed features. We apply the mode...

The problem of learning the structure of a DAG model in the presence of latent variables presents many formidable challenges. In particular there are an innnite number of latent variable models to consider, and these models possess features which make them hard to work with. We describe a class of graphical models which can represent the conditional independence structure induced by a latent va...

abstract: this paper represents a novel use of artificial neural networks in medical science. the proposed technique involves training a multi layer perceptron (mlp) (a kind of artificial neural network) with a bp learning algorithm to recognize a pattern for the diagnosing and prediction of five blood disorders, through the results of blood tests from h1 machine. the blood test parameters and ...