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Found 3 papers, 1 papers with code
A least distance estimator for a multivariate regression model using deep neural networks
We propose a deep neural network (DNN) based least distance (LD) estimator (DNN-LD) for a multivariate regression problem, addressing the limitations of the conventional methods.
A gradient-based variable selection for binary classification in reproducing kernel Hilbert space
Variable selection is essential in high-dimensional data analysis.
Bayesian estimation of a semiparametric recurrent event model with applications to the penetrance estimation of multiple primary cancers in Li-Fraumeni Syndrome
We employed a family-wise likelihood that facilitates using genetic information inherited through the family pedigree and properly adjusted for the ascertainment bias that was inevitable in studies of rare diseases by using an inverse probability weighting scheme.
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