Inference of online updating approach to nonparametric smoothing of big data

doi:10.52396/JUST-2021-0078

Journal of University of Science and Technology of China ›› 2021, Vol. 51 ›› Issue (5): 390-403.DOI: 10.52396/JUST-2021-0078

• Research Articles: Mathematics • Previous Articles Next Articles

Inference of online updating approach to nonparametric smoothing of big data

Chen Ziyu¹, Guo Xiao^2*

1.Department of Statistics and Finance, School of Management, University of Science and Technology of China, Hefei 230026, China;
2.International Institute of Finance, School of Management, University of Science and Technology of China, Hefei 230601, China

Received:2021-03-17 Revised:2021-05-20 Online:2021-05-31 Published:2021-12-01
Contact: * E-mail: xiaoguo@ustc.edu.cn

Abstract

Abstract: The online updating method (ONLINE) is an efficient analysis approach applied to big data. We prove the asymptotic properties and conduct statistical inference of the ONLINE models in kernel density and kernel regression. Several algorithms are proposed to solve the problems of the bandwidth selection in kernel density and regression respectively. We verify the asymptotic normality of the ONLINE density model in simulation and apply the ONLINE linear kernel regression to the Volatility Index (VIX) prediction. The empirical results show that the ONLINE linear kernel regression model achieves a comparable performance in continuously arriving option data streams prediction with significantly lower complexity than the classical local linear regression model.

Key words: bandwidth, kernel estimator, online updating approach, statistical inference, VIX prediction

CLC Number:

O212.7

CHEN Ziyu, GUO Xiao. Inference of online updating approach to nonparametric smoothing of big data[J]. Journal of University of Science and Technology of China, 2021, 51(5): 390-403.

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Inference of online updating approach to nonparametric smoothing of big data

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