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with Variable selection as keyword

Bayesian high-dimensional covariate selection in non-linear mixed-effects models using the SAEM algorithm

High-dimensional variable selection, with many more covariates than observations, is widely documented in standard regression models, but there are still few tools to address it in non-linear mixed-effects models where data are collected repeatedly on several individuals. In this work, variable selection is approached from a Bayesian perspective an...

A non-asymptotic risk bound for model selection in a high-dimensional mixture of experts via joint rank and variable sel...

We are motivated by the problem of identifying potentially nonlinear regression relationships between high-dimensional outputs and high-dimensional inputs of heterogeneous data. This requires regression, clustering, and model selection, simultaneously. In this framework, we apply the mixture of experts models which are among the most popular ensemb...

Variable selection methods in sparse GLARMA models

In this thesis, we propose novel variable selection methods for sparse GLARMA (Generalised Linear Autoregressive Moving Average) models, which can be used for modelling discrete-valued time series. This models allow us to introduce some dependence in a Generalised Linear Model (GLM). Specifically, in Chapter 2, we present an estimation procedure fo...

Exploring Risk Factors and Prediction Models for Sudden Cardiac Death with Machine Learning

Sudden cardiac death (SCD) is defined as a sudden natural death presumed to be of cardiac cause, heralded by abrupt loss of consciousness in the presence of witness, or in the absence of witness occurring within an hour after the onset of symptoms. Despite progress in clinical profiling and interventions, it remains a major public health problem, a...

Simultaneous clustering and variable selection: A novel algorithm and model selection procedure.

Published in Behavior research methods

The growing availability of high-dimensional data sets offers behavioral scientists an unprecedented opportunity to integrate the information hidden in the novel types of data (e.g., genetic data, social media data, and GPS tracks, etc.,) and thereby obtain a more detailed and comprehensive view towards their research questions. In the context of c...

Subsampling based variable selection for generalized linear models.

Published in Computational statistics & data analysis

A novel variable selection method for low-dimensional generalized linear models is introduced. The new approach called AIC OPTimization via STABility Selection (OPT-STABS) repeatedly subsamples the data, minimizes Akaike's Information Criterion (AIC) over a sequence of nested models for each subsample, and includes in the final model those predicto...

Taking into account radiation dose distributions to the heart in predicting radiation-induced valvulopathy : application...

The average recovery rate for childhood cancers is now 70 to 80%. Radiotherapy is one of the most recommended treatments, but it can cause critical iatrogenic effects in the long term, notably radiation-induced valvulopathy. Models identifying the patients most at risk could make it possible to personalize follow-up protocols and identify these adv...

Feature selection for support vector regression using a genetic algorithm.

Published in Biostatistics (Oxford, England)

Support vector regression (SVR) is particularly beneficial when the outcome and predictors are nonlinearly related. However, when many covariates are available, the method's flexibility can lead to overfitting and an overall loss in predictive accuracy. To overcome this drawback, we develop a feature selection method for SVR based on a genetic algo...