Liu estimation in generalized linear models: application on gamma distributed response variable

By Fikriye Kurtoglu, M. Revan Özkale. 2016

72 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/24aeedb63ff3bac7e6fea04af0ae917e6a6bc4a0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Management Science and Operations Research

1. Muhammad Amin
2. Muhammad Qasim
3. Muhammad Amanullah
4. Zakariya Yahya Algamal

In Statistics and Probability

1. Muhammad Qasim
2. Muhammad Amin
3. Muhammad Amanullah
4. Zakariya Yahya Algamal

Citations in the corpus

• Shrinkage estimators for gamma regression model (2018, methodology): “The response variable of n observations from gamma regression model is generated by Kurtoğlu and Özkale (2016) yi ∼ Gamma(θ2/var, var/θ) (35) where θ = exp(XTβ), var denotes θ2, β = (β1, ..., βp) with p∑ j=1 β2j = 1 and β1 = β2 = ... = βp (Kibria, 2003).”
• On the performance of some new Liu parameters for the gamma regression model (2018, methodology): “Only Kurtoğlu and Özkale [2] developed Liu estimation in theGLM, but they focused on comparing the first-order approximated Liu estimator with the first-order approximated ML estimator by the approximated mean squared error (MSE) criterion for the GR model.”
• Developing a ridge estimator for the gamma regression model (2018, methodology): “Given that γ = ν/θ, Equation (3) can reparameterized as a function of the mean (θ) and the shape (ν) parameters and written depending on the exponential function as f yið Þ ¼ EXP yi −1=θð Þ− log −1=θð Þ 1=ν þ c yi; νð Þ ; (4) where the canonical link function is −1/θ, the dispersion parameter is ϕ =”