On some beta ridge regression estimators: method, simulation and application
The classic statistical method for modelling the rates and proportions is the beta regression
model (BRM). The standard maximum likelihood estimator (MLE) is used to estimate the …
model (BRM). The standard maximum likelihood estimator (MLE) is used to estimate the …
On the estimation of Bell regression model using ridge estimator
The bell regression is used, when the response variable is in the form of counts with over
dispersion. The bell regression coefficients are generally estimated using the maximum …
dispersion. The bell regression coefficients are generally estimated using the maximum …
On the ridge estimation of the Conway‐Maxwell Poisson regression model with multicollinearity: Methods and applications
F Sami, M Amin, MM Butt - Concurrency and Computation …, 2022 - Wiley Online Library
In data analysis, count data modeling contributing a significant role. The Conway‐Maxwell
Poisson (COMP) is one of the flexible count data models to deal over and under dispersion …
Poisson (COMP) is one of the flexible count data models to deal over and under dispersion …
A new adjusted Liu estimator for the Poisson regression model
The Poisson regression model (PRM) is usually applied in the situations when the
dependent variable is in the form of count data. For estimating the unknown parameters of …
dependent variable is in the form of count data. For estimating the unknown parameters of …
A new class of efficient and debiased two-step shrinkage estimators: method and application
This paper introduces a new class of efficient and debiased two-step shrinkage estimators
for a linear regression model in the presence of multicollinearity. We derive the proposed …
for a linear regression model in the presence of multicollinearity. We derive the proposed …
Memory type control charts with inverse-Gaussian response: An application to yarn manufacturing industry
Control charts are commonly applied for monitoring and controlling the performance of the
manufacturing process. Usually, control charts are designed based on the main quality …
manufacturing process. Usually, control charts are designed based on the main quality …
Two-parameter estimator for the inverse Gaussian regression model
MN Akram, M Amin, M Amanullah - Communications in Statistics …, 2022 - Taylor & Francis
The inverse Gaussian regression model (IGRM) is frequently applied in the situations, when
the response variable is positively skewed and well fitted to the inverse Gaussian …
the response variable is positively skewed and well fitted to the inverse Gaussian …
James Stein estimator for the inverse Gaussian regression model
MN Akram, M Amin, M Amanullah - Iranian Journal of Science and …, 2021 - Springer
This paper considers the estimation of parameters for the inverse Gaussian regression
model in the presence of multicollinearity. To mitigate this issue, we propose the inverse …
model in the presence of multicollinearity. To mitigate this issue, we propose the inverse …
[PDF][PDF] A new modified ridge-type estimator for the beta regression model: simulation and application
A new modified ridge-type estimator for the beta regression model: simulation and application
Page 1 AIMS Mathematics, 7(1): 1035–1057. DOI: 10.3934/math.2022062 Received: 02 July …
Page 1 AIMS Mathematics, 7(1): 1035–1057. DOI: 10.3934/math.2022062 Received: 02 July …
[HTML][HTML] On the performance of some new ridge parameter estimators in the Poisson-inverse Gaussian ridge regression
Abstract The Poisson Inverse Gaussian Regression model (PIGRM) is used for modeling the
count datasets to deal with the issue of over-dispersion. Generally, the maximum likelihood …
count datasets to deal with the issue of over-dispersion. Generally, the maximum likelihood …