Demystifying a class of multiply robust estimators
We demystify the multiply robust estimators under the missing at random assumption and show they are essentially doubly robust estimators.
We demystify the multiply robust estimators under the missing at random assumption and show they are essentially doubly robust estimators.
We study the sparse composite quantile regression under ultrahigh dimensionality and make theoretical and algorithmic contributions.
We study multi-source insurance data using carefully designed group regularization and provide an efficient algorithm to solve the problem.
We propose a nonparametric approach to expectile regression estimation via model combination.
We propose two variants of the ADMM algorithm to solve the weighted lasso and elastic net penalized quantile regression and show they are very efficient.
We consider sparse penalized asymmetric least squares regression and its generalizations.
We study the effect of modulated noise on discrimination among different conspecific calls by treefrogs in a simulated environment