In this note we derive the rate of divergence of the penalty term for consistent model selection in the linear regression model under a general error structure.

In this note we propose an estimator of the survival function at a given time point using a Bayesian argument, an approach different from that of [Susarla, V., Van Ryzin, J., 1976. Nonparametric Bayesian estimation of survival curves from incomplete observations, Journal of the American...

We consider nonparametric estimation of a smooth function of one variable. Global selection procedures cannot sufficiently account for local sparseness of the covariate nor can they adapt to local curvature of the regression function. We propose a new method for selecting local smoothing...

In this paper we are concerned with detecting the true structure of a varying-coefficient partially linear model. The first issue is to identify whether a coefficient is parametric. The second issue is to select significant covariates in both nonparametric and parametric portions. In order to...

We give a new consistency proof for high-dimensional quantile regression estimators. A consequence of this proof is that the number of significant regressors can grow at a rate slog2(s)=o(n). To our best knowledge, this is the fastest rate achieved for high-dimensional quantile regression.

This article considers sparse covariance matrix estimation of high dimension. In contrast to the existing methods which are based on the residual estimation from least squares estimator, we utilize residuals from ridge estimator with the adaptive thresholding technique to estimate the error...