105 citations · 127 across the 14 of their papers we have counts for
14 papers
Global Bias-Corrected Divide-and-Conquer by Quantile-Matched Composite for General Nonparametric Regressions
Yan Chen, Lu Lin
The issues of bias-correction and robustness are crucial in the strategy of divide-and-conquer (DC), especially for asymmetric nonparametric models with massive data. It is known t…
Online Updating Statistics for Heterogenous Updating Regressions via Homogenization Techniques
Lin Lu, Lu Jun, Li Weiyu
Under the environment of big data streams, it is a common situation where the variable set of a model may change according to the condition of data streams. In this paper, we propo…
Unified Rules of Renewable Weighted Sums for Various Online Updating Estimations
Lu Lin, Weiyu Li, Jun Lu
This paper establishes unified frameworks of renewable weighted sums (RWS) for various online updating estimations in the models with streaming data sets. The newly defined RWS lay…
A race-DC in Big Data
Lu Lin, Jun Lu
The strategy of divide-and-combine (DC) has been widely used in the area of big data. Bias-correction is crucial in the DC procedure for validly aggregating the locally biased esti…
A Global Bias-Correction DC Method for Biased Estimation under Memory Constraint
Lu Lin, Feng Li
This paper establishes a global bias-correction divide-and-conquer (GBC-DC) rule for biased estimation under the case of memory constraint. In order to introduce the new estimation…
A simple and efficient profile likelihood for semiparametric exponential family
Lu Lin, Lili Liu, Xia Cui
Semiparametric exponential family proposed by Ning et al. (2017) is an extension of the parametric exponential family to the case with a nonparametric base measure function. Such a…