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Optimal subsampling algorithm for mode regression model with lognormal big data
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DOI：10.1080/03610926.2026.2655322.png)
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Abstract
En 中文
With the advent of the big data era, data is growing exponentially, and subsampling algorithms have become an important method for data processing. Many economic, medical, health, and engineering data follow lognormal distribution, and the study of subsampling algorithms for lognormal big data is of great importance. In this paper, a mode regression model is established for lognormal big data for the first time, and the consistency and asymptotic normality of subsampling parameter estimation based on A-optimality criterion and L-optimality criterion are demonstrated. The results of Monte Carlo simulation and a real data example analysis show the effectiveness of the model and algorithm, and the optimal subsampling algorithm in this paper can improve the estimation accuracy and reduce the computational costs.
Keywords:
Lognormal big data
mode regression model
A-optimality criterion
L-optimality criterion
asymptotic normality
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0.8
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211
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0