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Title: Sampling-based Gaussian Mixture Regression for Big Data
This paper proposes a nonuniform subsampling method for finite mixtures of regression models to reduce large data computational tasks. A general estimator based on a subsample is investigated, and its asymptotic normality is established. We assign optimal subsampling probabilities to data points that minimize the asymptotic mean squared errors of the general estimator and linearly transformed estimators. Since the proposed probabilities depend on unknown parameters, an implementable algorithm is developed. We first approximate the optimal subsampling probabilities using a pilot sample. After that, we select a subsample using the approximated subsampling probabilities and compute estimates using the subsample. We evaluate the proposed method in a simulation study and present a real data example using appliance energy data.  more » « less
Award ID(s):
2105571
NSF-PAR ID:
10414005
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Journal of Data Science
ISSN:
1680-743X
Page Range / eLocation ID:
158 to 172
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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