Variance Component Selection With Applications to Microbiome Taxonomic Data

Variance Component Selection With Applications to Microbiome Taxonomic Data

1 Department of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ, United States, 2 Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA, United States, 3 Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Department of Medicine, University of Arizona, Tucson, AZ, United States, 4 Division of Pulmonary, Critical Care, Sleep, and Occ...

On Estimation Following Selection with Applications on k-Records and Censored Data

Let X1 and X2 be two independent random variables from gamma populations Pi1,P2 with means alphaθ1 and alphaθ2 respectively, where alpha(> 0) is the common known shape parameter and θ1 and θ2 are scale parameters. Let X(1) ≤ X(2) denote the order statistics ofX1 and X2. Suppose that the population corresponding to the largest X(2) (or the smallest X(1)) observation is selected. The problem ofin...

Advances in independent component analysis with applications to data mining

This thesis considers the problem of finding latent structure in high dimensional data. It is assumed that the observed data are generated by unknown latent variables and their interactions. The task is to find these latent variables and the way they interact, given the observed data only. It is assumed that the latent variables do not depend on each other but act independently. A popular metho...

Analysis of High Dimensional Compositional Data Containing Structural Zeros with Applications to Microbiome Data

This paper is motivated by the recent interest in the analysis of high dimensional microbiome data. A key feature of this data is the presence of ‘structural zeros’ which are microbes missing from an observation vector due to an underlying biological process and not due to error in measurement. Typical notions of missingness are insufficient to model these structural zeros. We define a general ...

Column subset selection with applications to neuroimaging data