From Bilinear Regression to Inductive Matrix Completion: A Quasi-Bayesian Analysis.

In this paper, we study the problem of bilinear regression, a type of statistical modeling that deals with multiple variables and multiple responses. One of the main difficulties that arise in this problem is the presence of missing data in the response matrix, a problem known as inductive matrix completion. To address these issues, we propose a novel approach that combines elements of Bayesian statistics with a quasi-likelihood method.

Evaluation of diagnostic factors used to refer children with constipation for rectal biopsies.

Purpose: Children with constipation and suspected Hirschsprung's disease are referred for rectal biopsy. Since this is an invasive procedure, appropriate indications should be applied to minimize the number of "unnecessary" biopsies.

Methods: We reviewed all constipated children who underwent a rectal biopsy to diagnose a possible Hirschsprung's disease at a tertiary referral hospital over a 6-year period (2013-2018).

Boosting heritability: estimating the genetic component of phenotypic variation with multiple sample splitting.

Background: Heritability is a central measure in genetics quantifying how much of the variability observed in a trait is attributable to genetic differences. Existing methods for estimating heritability are most often based on random-effect models, typically for computational reasons. The alternative of using a fixed-effect model has received much more limited attention in the literature.