Diagnostics via partial residual plots in inverse Gaussian regression

2019 ◽  
Vol 34 (1) ◽  
Author(s):  
Muhammad Imran ◽  
Atif Akbar
Keyword(s):  
Inverse Gaussian ◽  
Gaussian Regression ◽  
Residual Plots

scholarly journals Parameter Estimation and Hypothesis Testing of Multivariate Poisson Inverse Gaussian Regression

Symmetry ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 1738
Author(s):  
Selvi Mardalena ◽  
Purhadi Purhadi ◽  
Jerry Dwi Trijoyo Purnomo ◽  
Dedy Dwi Prastyo
Keyword(s):  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Hypothesis Testing ◽  
Poisson Regression ◽  
Ratio Test ◽  
Inverse Gaussian ◽  
Count Response ◽  
The Mean ◽  
Gaussian Regression ◽  
Response Variables

Multivariate Poisson regression is used in order to model two or more count response variables. The Poisson regression has a strict assumption, that is the mean and the variance of response variables are equal (equidispersion). Practically, the variance can be larger than the mean (overdispersion). Thus, a suitable method for modelling these kind of data needs to be developed. One alternative model to overcome the overdispersion issue in the multi-count response variables is the Multivariate Poisson Inverse Gaussian Regression (MPIGR) model, which is extended with an exposure variable. Additionally, a modification of Bessel function that contain factorial functions is proposed in this work to make it computable. The objective of this study is to develop the parameter estimation and hypothesis testing of the MPIGR model. The parameter estimation uses the Maximum Likelihood Estimation (MLE) method, followed by the Newton–Raphson iteration. The hypothesis testing is constructed using the Maximum Likelihood Ratio Test (MLRT) method. The MPIGR model that has been developed is then applied to regress three response variables, i.e., the number of infant mortality, the number of under-five children mortality, and the number of maternal mortality on eight predictors. The unit observation is the cities and municipalities in Java Island, Indonesia. The empirical results show that three response variables that are previously mentioned are significantly affected by all predictors.

A mixed poisson-inverse-gaussian regression model

1989 ◽  
Vol 17 (2) ◽  
pp. 171-181 ◽  
Author(s):  
C. Dean ◽  
J. F. Lawless ◽  
G. E. Willmot
Keyword(s):  
Regression Model ◽  
Inverse Gaussian ◽  
Gaussian Regression

Calibration for inverse gaussian regression

1995 ◽  
Vol 24 (10) ◽  
pp. 2609-2620 ◽  
Author(s):  
Mammo Woldie ◽  
J. Leroy Folks
Keyword(s):  
Inverse Gaussian ◽  
Gaussian Regression

Modified ridge-type estimator for the inverse Gaussian regression model

2021 ◽  
pp. 1-19
Author(s):  
Muhammad Nauman Akram ◽  
Muhammad Amin ◽  
Muhammad Aman Ullah ◽  
Saima Afzal
Keyword(s):  
Regression Model ◽  
Inverse Gaussian ◽  
Gaussian Regression

POWER FUNCTION FOR INVERSE GAUSSIAN REGRESSION MODELS

2001 ◽  
Vol 30 (5) ◽  
pp. 787-797 ◽  
Author(s):  
Mammo Woldie ◽  
J. Leroy Folks ◽  
John P. Chandler
Keyword(s):  
Power Function ◽  
Regression Models ◽  
Inverse Gaussian ◽  
Gaussian Regression
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