Using the Variagraph to Test Lack of Fit of a Parametric Regression Model Without Replication

2003 ◽  
Vol 32 (3) ◽  
pp. 733-745 ◽  
Author(s):  
Andrew P. Robinson ◽  
Sanford Weisberg
Keyword(s):  
Regression Model ◽  
Parametric Regression ◽  
Lack Of Fit

scholarly journals A Semi-parametric Regression Model to Estimate Variability of NO2

2012 ◽  
Vol 2 (1) ◽  
Author(s):  
Mieczyslaw Szyszkowicz ◽  
Mamun Mahmud ◽  
Neil Tremblay
Keyword(s):  
Regression Model ◽  
Parametric Regression

A parametric regression model of tumor recurrence: An application to the analysis of clinical data on breast cancer

1996 ◽  
Vol 29 (3) ◽  
pp. 271-278 ◽  
Author(s):  
B. Asselain ◽  
A. Fourquet ◽  
T. Hoang ◽  
A.D. Tsodikov ◽  
A.Yu. Yakovlev
Keyword(s):  
Breast Cancer ◽  
Regression Model ◽  
Clinical Data ◽  
Tumor Recurrence ◽  
Parametric Regression

scholarly journals A note non Nonparametric Regression Modeling using a Density Function

2020 ◽  
Vol 7 (2) ◽  
pp. 993-1000
Author(s):  
Jakperik Dioggban
Keyword(s):  
Regression Model ◽  
Density Function ◽  
Nonparametric Regression ◽  
Model Simulation ◽  
Smoothing Splines ◽  
Bias Reduction ◽  
Simulation Studies ◽  
Lack Of Fit ◽  
Classical Regression ◽  
Better Than

The nonparametric regression offers alternative to classical regression analysis when the data are not well behaved or when the classical regression model shows significant lack of fit. In recent years, It has been applied using Kernel estimators and the smoothing splines, but these methods wields some bias of estimation. In this study, a semi-parametric multiplicative bias reduction density function was used to develop a non parametric regression model. Simulation studies conducted showed that the proposed estimator performs better than both the Kernel and the smoothing splines estimators especially with large samples

Parametric Survival Modelling of Risk Factor of Tuberculosis Patients under DOTS Program at Hawassa Town, Ethiopia

2015 ◽  
Vol 4 (1) ◽  
pp. 1-17 ◽  
Author(s):  
Fikadu Zawdie Chere ◽  
Yohannes Yebabe Tesfay ◽  
Fikre Enquoselassie
Keyword(s):  
Regression Model ◽  
Survival Data ◽  
Information Criterion ◽  
Major Health Problem ◽  
Weibull Regression ◽  
Tuberculosis Patients ◽  
Parametric Regression ◽  
Time To Death ◽  
Baseline Weight ◽  
Weibull Regression Model

Tuberculosis (TB) is a chronic infectious disease that has a major health problem over the centuries. This study assessed the risk factors associated with time to death among TB patients treated under directly observed short course treatment program in Health facilities in Hawassa city, Ethiopia. The authors analysed data from a cohort of 1604 TB patients recruited between September 2008 to September 2011. They apply the parametric regression model of survival data analysis. The best fitted parametric regression model is selected by using the Akaike information criterion (AIC). The AIC confirms that the Weibull regression model is found to be the best fit of the survival of tuberculosis patients under the DOTS program at Hawassa town, Ethiopia. The fit of the Weibull regression model result revealed that sex, age, baseline weight, HIV status, category of patients and year of enrolment are the significant factor for the survival of TB patients.

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