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20112019
most citedActive learning for binary classification with variable selection

1 citations · 1 across the 8 of their papers we have counts for

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7 papers · 1 filter

stat.ME2019

Sequential estimation for GEE with adaptive variables and subject selection

Zimu Chen, Zhanfeng Wang, Yuan-chin Ivan Chang

Modeling correlated or highly stratified multiple-response data becomes a common data analysis task due to modern data monitoring facilities and methods. Generalized estimating equ…

stat.ME2019

Modeling Function-Valued Processes with Nonseparable and/or Nonstationary Covariance Structure

Evandro Konzen, Jian Qing Shi, Zhanfeng Wang

We discuss a general Bayesian framework on modeling multidimensional function-valued processes by using a Gaussian process or a heavy-tailed process as a prior, enabling us to hand…

stat.ME2018

Distributed sequential method for analyzing massive data

Zhanfeng Wang, Yuan-chin Ivan Chang

To analyse a very large data set containing lengthy variables, we adopt a sequential estimation idea and propose a parallel divide-and-conquer method. We conduct several convention…

stat.ME2018

Robust functional ANOVA model with t-process

Chen Zhang, Zimu Chen, Zhanfeng Wang +1

Robust estimation approaches are of fundamental importance for statistical modelling. To reduce susceptibility to outliers, we propose a robust estimation procedure with t-process…

stat.ME2017

A Robust t-process Regression Model with Independent Errors

Wang Zhanfeng, Noh Maengseok, Lee Youngjo +1

Gaussian process regression (GPR) model is well-known to be susceptible to outliers. Robust process regression models based on t-process or other heavy-tailed processes have been d…

stat.ME2017

Nearly Semiparametric Efficient Estimation of Quantile Regression

Kani Chen, Yuanyuan Lin, Zhanfeng Wang +1

As a competitive alternative to least squares regression, quantile regression is popular in analyzing heterogenous data. For quantile regression model specified for one single quan…