Journal of Data Science ›› 2020, Vol. 18 ›› Issue (2): 238-256.doi: 10.6339/JDS.202004 18(2).0002

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Subsampled Data Based Alternative Regularized Estimators 

Subir Ghosh1, Gabriel Ruiz2, and Brandon Wales3   

  1. 1,2,3 Department of Statistics, University of California Riverside, USA.
  • Online:2020-04-15 Published:2020-05-10

Abstract: Subsampling the data is used in this paper as a learning method about the influence of the data points for drawing inference on the parameters of a fitted logistic regression model. The alternative, alternative regularized, alternative regularized lasso, and alternative regularized ridge estimators are proposed for the parameter estimation of logistic regression models and are then compared with the maximum likelihood estimators. The proposed alternative regularized estimators are obtained by using a tuning parameter but the proposed alternative estimators are not regularized. The proposed alternative regularized lasso estimators are the averaged standard lasso estimators and the alternative regularized ridge estimators are also the averaged standard ridge estimators over subsets of groups where the number of subsets could be smaller than the number of parameters. The values of the tuning parameters are obtained to make the alternative regularized estimators very close to the maximum likelihood estimators and the process is explained with two real data as well as a simulated study. The alternative and alternative regularized estimators always have the closed form expressions in terms of observations that the maximum likelihood estimators do not have. When the maximum likelihood estimators do not have the closed form expressions, the alternative regularized estimators thus obtained provide the approximate closed form expressions for them.

Key words: Item response, Lasso, Logistic regression, Maximum likelihood, Regularized, Ridge, Subsampling, Tuning parameter.