Mian Huang

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Robust variable selection procedures through penalized regression have been gaining increased attention in the literature. They can be used to perform variable selection and are expected to yield robust estimates. However, to the best of our knowledge, the robustness of those penalized regression procedures has not been well characterized. In this paper, we(More)
In this paper, we study a class of semiparametric mixtures of regression models, in which the regression functions are linear functions of the predictors, but the mixing proportions are smoothing functions of a covariate. We propose a one-step backfit-ting estimation procedure to achieve the optimal convergence rates for both regression parameters and the(More)
Motivated by an analysis of US house price index data, we propose nonparametric finite mixture of regression models. We study the identifiability issue of the proposed models, and develop an estimation procedure by employing kernel regression. We further systematically study the sampling properties of the proposed estimators, and establish their asymptotic(More)
When the functional data are not homogeneous, e.g., there exist multiple classes of functional curves in the dataset, traditional estimation methods may fail. In this paper, we propose a new estimation procedure for the Mixture of Gaussian Processes, to incorporate both functional and inhomogeneous properties of the data. Our method can be viewed as a(More)
This paper is concerned with quantile regression for a semiparametric regression model, in which both the conditional mean and conditional variance function of the response given the covariates admit a single-index structure. This semiparametric regression model enables us to reduce the dimension of the covariates and simultaneously retains the flexibility(More)
Detecting how genes regulate biological shape has become a multidisciplinary research interest because of its wide application in many disciplines. Despite its fundamental importance, the challenges of accurately extracting information from an image, statistically modeling the high-dimensional shape and meticulously locating shape quantitative trait loci(More)
A University of California author or department has made this article openly available. Thanks to the Academic Senate's Open Access Policy, a great many UC-authored scholarly publications will now be freely available on this site. Let us know how this access is important for you. We want to hear your story! Abstract Functional linear models are important(More)
Medical information exchange and integration is the effective method to solve the interoperability and medical information island, and is the basis of medical information sharing. In this paper, we take medical texts and medical images as the basic integrated objects, DICOM, HL7 messages and datasets as the integrated units, efficient DI-COM, HL7 message(More)
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