Semiparametric empirical likelihood estimation for two-stage outcome-dependent sampling under the frame of generalized linear models

2014 ◽  
Vol 30 (3) ◽  
pp. 663-676 ◽  
Author(s):  
Jie-li Ding ◽  
Yan-yan Liu
Keyword(s):  
Empirical Likelihood ◽  
Generalized Linear Models ◽  
Linear Models ◽  
Likelihood Estimation ◽  
Two Stage

scholarly journals Catalytic prior distributions with application to generalized linear models

2020 ◽  
Vol 117 (22) ◽  
pp. 12004-12010
Author(s):  
Dongming Huang ◽  
Nathan Stein ◽  
Donald B. Rubin ◽  
S. C. Kou
Keyword(s):  
Generalized Linear Models ◽  
Linear Models ◽  
Synthetic Data ◽  
Interval Estimation ◽  
Likelihood Estimation ◽  
Predictive Distribution ◽  
Point Estimation ◽  
Tuning Parameter ◽  
Working Model ◽  
Coverage Accuracy

A catalytic prior distribution is designed to stabilize a high-dimensional “working model” by shrinking it toward a “simplified model.” The shrinkage is achieved by supplementing the observed data with a small amount of “synthetic data” generated from a predictive distribution under the simpler model. We apply this framework to generalized linear models, where we propose various strategies for the specification of a tuning parameter governing the degree of shrinkage and study resultant theoretical properties. In simulations, the resulting posterior estimation using such a catalytic prior outperforms maximum likelihood estimation from the working model and is generally comparable with or superior to existing competitive methods in terms of frequentist prediction accuracy of point estimation and coverage accuracy of interval estimation. The catalytic priors have simple interpretations and are easy to formulate.

scholarly journals Penalized Likelihood Estimation of Gamma Distributed Response Variable via Corrected Solution of Regression Coefficients

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Rasaki Olawale Olanrewaju
Keyword(s):  
Generalized Linear Models ◽  
Linear Models ◽  
Penalized Likelihood ◽  
Likelihood Estimation ◽  
Regression Coefficients ◽  
Response Variable ◽  
Penalized Likelihood Estimation ◽  
Selection Operator ◽  
Minimax Concave Penalty

A Gamma distributed response is subjected to regression penalized likelihood estimations of Least Absolute Shrinkage and Selection Operator (LASSO) and Minimax Concave Penalty via Generalized Linear Models (GLMs). The Gamma related disturbance controls the influence of skewness and spread in the corrected path solutions of the regression coefficients.

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