An efficient stochastic Newton algorithm for parameter estimation in logistic regressions

Abstract : Logistic regression is a well-known statistical model which is commonly used in the situation where the output is a binary random variable. It has a wide range of applications including machine learning, public health, social sciences, ecology and econometry. In order to estimate the unknown parameters of logistic regression with data streams arriving sequentially and at high speed, we focus our attention on a recursive stochastic algorithm. More precisely, we investigate the asymptotic behavior of a new stochastic Newton algorithm. It enables to easily update the estimates when the data arrive sequentially and to have research steps in all directions. We establish the almost sure convergence of our stochastic Newton algorithm as well as its asymptotic normality. All our theoretical results are illustrated by numerical experiments.
Document type :
Preprints, Working Papers, ...
Complete list of metadatas

Cited literature [4 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-02103041
Contributor : Antoine Godichon-Baggioni <>
Submitted on : Thursday, April 18, 2019 - 8:22:50 AM
Last modification on : Wednesday, May 15, 2019 - 7:19:09 AM

File

BGP-Newton.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-02103041, version 1
  • ARXIV : 1904.07908

Citation

Bernard Bercu, Antoine Godichon, Bruno Portier. An efficient stochastic Newton algorithm for parameter estimation in logistic regressions. 2019. ⟨hal-02103041⟩

Share

Metrics

Record views

19

Files downloads

14