More on the restricted almost unbiased Liu-estimator in Logistic regression

11/28/2017
by   Nagarajah Varathan, et al.
0

To address the problem of multicollinearity in the logistic regression model, in this paper we propose a new estimator called Stochastic restricted almost unbiased logistic Liu-estimator (SRAULLE) when the prior information is available in the form of stochastic linear restrictions. A Monte Carlo simulation study was carried out to compare the performance of the proposed estimator with some existing estimators in the scalar mean squared error (SMSE) sense. Finally, a real data example was given to appraise the performance of the estimators.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
12/09/2017

Stochastic Restricted Biased Estimators in misspecified regression model with incomplete prior information

In this article, the analysis of misspecification was extended to the re...
research
11/05/2021

Liu Estimator in the Multinomial Logistic Regression Model

This paper considers the Liu estimator in the multinomial logistic regre...
research
01/12/2021

Evaluation of Logistic Regression Applied to Respondent-Driven Samples: Simulated and Real Data

Objective: To investigate the impact of different logistic regression es...
research
07/07/2018

Robust Estimation for Two-Dimensional Autoregressive Processes Based on Bounded Innovation Propagation Representations

Robust methods have been a successful approach to deal with contaminatio...
research
05/24/2022

Robust and Sparse Multinomial Regression in High Dimensions

A robust and sparse estimator for multinomial regression is proposed for...
research
02/01/2018

Linearized Binary Regression

Probit regression was first proposed by Bliss in 1934 to study mortality...
research
05/13/2014

Adaptive Monte Carlo via Bandit Allocation

We consider the problem of sequentially choosing between a set of unbias...

Please sign up or login with your details

Forgot password? Click here to reset