Genetic association studies lead to simultaneous categorical data analysis. The sample for every genetic locus consists of a contingency table containing the numbers of observed genotype-phenotype combinations. The goal of the statistical analysis is to detect associations between the (potentially very large) set of genetic markers and the (typically binary) phenotype of interest. This is a par...

cytomegalovirus (cmv) and rubella are considered as dangerous viral infections to the fetus. the findings of this research can clear the possible progress made thus far toward prevention in this part of the country. the data of all referees to genetic center of shahid beheshti hospital in hamadan, including the rubella and cmv tests were recorded in questionnaires and analyzed by logistic regre...

Classical multivariate statistical inference methods are often based on the sample mean vector and covariance matrix. They are then optimal under the assumption of multivariate normality but loose in efficiency in the case of heavy tailed distribution. In this paper nonparametric and robust competitors based on the spatial signs and ranks are discussed and the R statistical software package to ...

BACKGROUND Logistic regression is the most common statistical model for processing multivariate data in the medical literature. Artificial intelligence models like an artificial neural network (ANN) and genetic algorithm (GA) may also be useful to interpret medical data. AIMS The purpose of this study was to perform artificial intelligence models on a medical data sheet and compare to logisti...

Methods in multivariate statistical analysis are essential for working with large amounts of geophysical data— data from observational arrays, from satellites or from numerical model output. In classical multivariate statistical analysis, there is a hierarchy of methods, starting with linear regression (LR) at the base, followed by principal component analysis (PCA), and finally canonical corre...

BACKGROUND With a large number of potentially relevant clinical indicators penalization and ensemble learning methods are thought to provide better predictive performance than usual linear predictors. However, little is known about how they perform in clinical studies where few cases are available. We used Random Forests and Partial Least Squares Discriminant Analysis to select the most salient...

For rough heterogeneous samples, the contrast observed in XPS images may result from both changes in elemental or chemical composition and sample topography. Background image acquisition and subtraction are frequently utilized to minimize topographical effects so that images represent concentration variations in the sample. This procedure may significantly increase the data acquisition time. Mu...