Methods for regression analysis in high-dimensional data

Bayesian models for sparse regression analysis of high dimensional data

This paper considers the task of building efficient regression models for sparse multivariate analysis of high dimensional data sets, in particular it focuses on cases where the numbers q of responses Y = (y k , 1 ≤ k ≤ q) and p of predictors X = (xj , 1 ≤ j ≤ p) to analyse jointly are both large with respect to the sample size n, a challenging bi-directional task. The analysis of such data set...

Robust Ridge Regression for High-Dimensional Data

Ridge regression, being based on the minimization of a quadratic loss function, is sensitive to outliers. Current proposals for robust ridge regression estimators are sensitive to “bad leverage observations”, cannot be employed when the number of predictors p is larger than the number of observations n; and have a low robustness when the ratio p=n is large. In this paper a ridge regression esti...

Combining feature ranking methods for high dimensional data analysis

Statistical Learning Methods for High Dimensional Genomic Data Statistical Learning Methods for High Dimensional Genomic Data Title: Statistical Learning Methods for High Dimensional Genomic Data

Due to their high-dimensionality, -omics technologies require the development of computational methods that are able to work with large number of variables. Each data type is characterized by its method of measurement and by the biological aspect under study. Understanding the data properties allows the design of sophisticated and effective computational models that are able to uncover and expl...

Statistical Analysis Methods for the fMRI Data

Functional magnetic resonance imaging (fMRI) is a safe and non-invasive way to assess brain functions by using signal changes associated with brain activity. The technique has become a ubiquitous tool in basic, clinical and cognitive neuroscience. This method can measure little metabolism changes that occur in active part of the brain. We process the fMRI data to be able to find the parts of br...

Robust high-dimensional semiparametric regression using optimized differencing method applied to the vitamin B2 production data

Background and purpose: By evolving science, knowledge, and technology, we deal with high-dimensional data in which the number of predictors may considerably exceed the sample size. The main problems with high-dimensional data are the estimation of the coefficients and interpretation. For high-dimension problems, classical methods are not reliable because of a large number of predictor variable...