Inversion-free subsampling Newton’s method for large sample logistic regression

J. Lars Kirkby (), Dang H. Nguyen (), Duy Nguyen () and Nhu N. Nguyen ()
Additional contact information
J. Lars Kirkby: Georgia Institute of Technology
Dang H. Nguyen: University of Alabama
Duy Nguyen: Marist College
Nhu N. Nguyen: University of Connecticut

Statistical Papers, 2022, vol. 63, issue 3, No 9, 943-963

Abstract: Abstract In this paper, we develop a subsampling Newton’s method to efficiently approximate the maximum likelihood estimate in logistic regression, which is especially useful for large-sample problems. One distinct feature of our algorithm is that matrix inversion is not explicitly performed. We propose two algorithms which are used to construct iteratively a sequence of matrices which converge to the Hessian of the maximum likelihood function on the subsample. We provide numerical examples to show that the proposed method is efficient and robust.

Keywords: Logistic regression; Massive data; Optimal subsampling; Newton’s method; Gradient descent; Stochastic gradient descent; 34D20; 60H10; 92D25; 93D05; 93D20 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s00362-021-01263-y

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