Birgé and Massart proposed in 2001 the slope heuristics as a way to choose optimally from
data an unknown multiplicative constant in front of a penalty. It is built upon the notion of …

With the growing amount and diversity of intermediate omics data complementary to
genomics (eg DNA methylation, gene expression, and protein abundance), there is a need …

Mixture of experts (MoE) is a popular class of models in statistics and machine learning that
has sustained attention over the years, due to its flexibility and effectiveness. We consider …

Mixture of experts (MoE) are a popular class of statistical and machine learning models that
have gained attention over the years due to their flexibility and efficiency. In this work, we …

Heterogeneity is a hallmark of complex diseases. Regression‐based heterogeneity
analysis, which is directly concerned with outcome–feature relationships, has led to a …

We are motivated by the problem of identifying potentially nonlinear regression relationships
between high-dimensional outputs and high-dimensional inputs of heterogeneous data …

Model selection, via penalized likelihood type criteria, is a standard task in many statistical
inference and machine learning problems. Progress has led to deriving criteria with …

This article focuses on the practical issue of a recent theoretical method proposed for trend
estimation in high dimensional time series. This method falls within the scope of the low-rank …

With the development of high-throughput sequencing, whole-genome analysis, such as
genomic prediction and genome-wide association studies (GWAS), plays an important role …

In this paper, we present the R package TrendTM dedicated to the trend estimation of high
dimensional time series matrices. The main features of this package is the possibility to take …