28 citations · 66 across the 12 of their papers we have counts for
12 papers
Covariance regression with random forests
Cansu Alakus, Denis Larocque, Aurelie Labbe
Capturing the conditional covariances or correlations among the elements of a multivariate response vector based on covariates is important to various fields including neuroscience…
Bayesian Complementary Kernelized Learning for Multidimensional Spatiotemporal Data
Mengying Lei, Aurelie Labbe, Lijun Sun
Probabilistic modeling of multidimensional spatiotemporal data is critical to many real-world applications. As real-world spatiotemporal data often exhibits complex dependencies th…
Spatial Aggregation and Temporal Convolution Networks for Real-time Kriging
Yuankai Wu, Dingyi Zhuang, Mengying Lei +2
Spatiotemporal kriging is an important application in spatiotemporal data analysis, aiming to recover/interpolate signals for unsampled/unobserved locations based on observed signa…
Scalable Spatiotemporally Varying Coefficient Modelling with Bayesian Kernelized Tensor Regression
Mengying Lei, Aurelie Labbe, Lijun Sun
As a regression technique in spatial statistics, the spatiotemporally varying coefficient model (STVC) is an important tool for discovering nonstationary and interpretable response…
RFpredInterval: An R Package for Prediction Intervals with Random Forests and Boosted Forests
Cansu Alakus, Denis Larocque, Aurelie Labbe
Like many predictive models, random forests provide point predictions for new observations. Besides the point prediction, it is important to quantify the uncertainty in the predict…
Detecting differentially methylated regions in bisulfite sequencing data using quasi-binomial mixed models with smooth covariate effect estimates
Kaiqiong Zhao, Karim Oualkacha, Lajmi Lakhal-Chaieb +6
Identifying disease-associated changes in DNA methylation can help to gain a better understanding of disease etiology. Bisulfite sequencing technology allows the generation of meth…