Aaron J. Molstad | DeepAI

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∙ 12/19/2022

Direct covariance matrix estimation with compositional data

Compositional data arise in many areas of research in the natural and bi...

0 Aaron J. Molstad, et al. ∙

∙ 08/29/2022

Multiresolution categorical regression for interpretable cell type annotation

In many categorical response regression applications, the response categ...

0 Aaron J. Molstad, et al. ∙

∙ 06/21/2022

Conditional probability tensor decompositions for multivariate categorical response regression

In many modern regression applications, the response consists of multipl...

0 Aaron J. Molstad, et al. ∙

∙ 11/30/2021

A Convex-Nonconvex Strategy for Grouped Variable Selection

This paper deals with the grouped variable selection problem. A widely u...

0 Xiaoqian Liu, et al. ∙

∙ 11/23/2021

Binned multinomial logistic regression for integrative cell type annotation

Categorizing individual cells into one of many known cell type categorie...

0 Keshav Motwani, et al. ∙

∙ 08/04/2021

Dimension reduction for integrative survival analysis

We propose a constrained maximum partial likelihood estimator for dimens...

0 Aaron J. Molstad, et al. ∙

∙ 04/04/2021

Scalable algorithms for semiparametric accelerated failure time models in high dimensions

Semiparametric accelerated failure time (AFT) models are a useful altern...

0 Piotr M. Suder, et al. ∙

∙ 01/21/2021

A unified method for multivariate mixed-type response regression

We propose a new method for multivariate response regressions where the ...

0 Karl Oskar Ekvall, et al. ∙

∙ 07/15/2020

A likelihood-based approach for multivariate categorical response regression in high dimensions

We propose a penalized likelihood method to fit the bivariate categorica...

0 Aaron J. Molstad, et al. ∙

∙ 03/01/2020

Estimating Multiple Precision Matrices with Cluster Fusion Regularization

We propose a penalized likelihood framework for estimating multiple prec...

0 Bradley S. Price, et al. ∙

∙ 01/23/2020

A covariance-enhanced approach to multi-tissue joint eQTL mapping with application to transcriptome-wide association studies

Transcriptome-wide association studies based on genetically predicted ge...

0 Aaron J. Molstad, et al. ∙

∙ 09/11/2019

Insights and algorithms for the multivariate square-root lasso

We study the multivariate square-root lasso, a method for fitting the mu...

0 Aaron J. Molstad, et al. ∙

∙ 08/31/2018

An explicit mean-covariance parameterization for multivariate response linear regression

We develop a new method to fit the multivariate response linear regressi...

0 Aaron J. Molstad, et al. ∙

∙ 08/30/2018

Gaussian process regression for survival time prediction with genome-wide gene expression

Predicting the survival time of a cancer patient based on his/her genome...

0 Aaron J. Molstad, et al. ∙

∙ 09/23/2016

A penalized likelihood method for classification with matrix-valued predictors

We propose a penalized likelihood method to fit the linear discriminant ...

0 Aaron J. Molstad, et al. ∙