Performance of Some Ridge Parameters for Probit Regression: with Application on Swedish Job Search Data [PDF]
In ridge regression the estimation of the ridge parameter is an important issue. This paper generalizes some methods for estimating the ridge parameter for probit ridge regression (PRR) model based on the work of Kibria et al. (2011).
Locking, Håkan +2 more
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Online inference in high-dimensional generalized linear models with streaming data. [PDF]
Electron J Stat, 2023 Luo L, Han R, Lin Y, Huang J.
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A robust and efficient variable selection method for linear regression. [PDF]
J Appl Stat, 2022 Yang Z, Fu L, Wang YG, Dong Z, Jiang Y.
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Testability of high-dimensional linear models with nonsparse structures. [PDF]
Ann Stat, 2022 Bradic J, Fan J, Zhu Y.
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DOUBLY DEBIASED LASSO: HIGH-DIMENSIONAL INFERENCE UNDER HIDDEN CONFOUNDING. [PDF]
Ann Stat, 2022 Guo Z, Ćevid D, Bühlmann P.
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Shrinkage with shrunken shoulders: Gibbs sampling shrinkage model posteriors with guaranteed convergence rates. [PDF]
Bayesian Anal, 2023 Nishimura A, Suchard MA.
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A new linearized ridge Poisson estimator in the presence of multicollinearity. [PDF]
J Appl Stat, 2022 Jadhav NH.
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An empirical evaluation of small area estimators [PDF]
This paper investigates the comparative performance of five small area estimators. We use Monte Carlo simulation in the context of both theoretical and empirical populations.
Albert Satorra, Eva Ventura, Àlex Costa
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New Liu Estimators for the Poisson Regression Model: Method and Application [PDF]
A new shrinkage estimator for the Poisson model is introduced in this paper. This method is a generalization of the Liu (1993) estimator originally developed for the linear regression model and will be generalised here to be used instead of the classical
Kibria, B. M. Golam +3 more
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A new kind of stochastic restricted biased estimator for logistic regression model. [PDF]
J Appl Stat, 2021 Alheety MI, Månsson K, Golam Kibria BM.
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