Variable Selection and Function Estimation in Additive Nonparametric Regression Using a Data-Based Prior: Comment

1999 ◽  
Vol 94 (447) ◽  
pp. 798
Author(s):  
E. I. George ◽  
R. E. McCulloch
Keyword(s):  
Variable Selection ◽  
Nonparametric Regression ◽  
Function Estimation ◽  
Additive Nonparametric Regression ◽  
And Function

scholarly journals Kernel methods in system identification, machine learning and function estimation: A survey

Automatica ◽  
2014 ◽  
Vol 50 (3) ◽  
pp. 657-682 ◽  
Author(s):  
Gianluigi Pillonetto ◽  
Francesco Dinuzzo ◽  
Tianshi Chen ◽  
Giuseppe De Nicolao ◽  
Lennart Ljung
Keyword(s):  
Machine Learning ◽  
System Identification ◽  
Kernel Methods ◽  
Function Estimation ◽  
And Function

scholarly journals Near-Linear Time Local Polynomial Nonparametric Estimation with Box Kernels

2021 ◽  
Author(s):  
Yining Wang ◽  
Yi Wu ◽  
Simon S. Du
Keyword(s):  
Operations Management ◽  
Polynomial Regression ◽  
Linear Time ◽  
Big Data Analytics ◽  
Function Estimation ◽  
Local Polynomial ◽  
Efficiency And Effectiveness ◽  
Exploratory Data ◽  
And Function ◽  
Novel Applications

Summary of Contribution: Big data analytics has become essential for modern operations research and operations management applications. Statistics methods, such as nonparametric density and function estimation, play important roles in predictive and exploratory data analysis for economics and operations management problems. In this paper, we concentrate on efficiently computing local polynomial regression estimates. We significantly accelerate the computation of such local polynomial estimates by novel applications of multidimensional binary indexed trees ( Fenwick 1994 ) and lazy memory allocation via hashing. Both time and space complexity of our proposed algorithm are nearly linear in the number of inputs. Simulation results confirm the efficiency and effectiveness of our proposed methods.

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