A Semi-parametric Regression Model to Estimate Variability of NO2

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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A Non-parametric Regression Model for Consumption of Urban Residents and Foreign Exchange Reserve

Modern Applied Science ◽

10.5539/mas.v3n11p59 ◽

2009 ◽

Vol 3 (11) ◽

Author(s):

Yi Ning ◽

Xiangdong Song

Keyword(s):

Regression Model ◽

Foreign Exchange ◽

Foreign Exchange Reserve ◽

Urban Residents ◽

Parametric Regression ◽

Non Parametric

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Shortest confidence interval of parameter semi parametric regression model using spline truncated for longitudinal data

10.1063/1.5139746 ◽

2019 ◽

Author(s):

I. Nyoman Budiantara ◽

Vita Ratnasari ◽

Erma Oktania Permatasari ◽

Dasty Dewi Prawanti

Keyword(s):

Confidence Interval ◽

Longitudinal Data ◽

Regression Model ◽

Parametric Regression

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COMPLETE MOMENT CONVERGENCE FOR ARRAYS OF ROWWISE NEGATIVELY ASSOCIATED RANDOM VARIABLES AND ITS APPLICATION IN NON-PARAMETRIC REGRESSION MODEL

Probability in the Engineering and Informational Sciences ◽

10.1017/s026996481600053x ◽

2017 ◽

Vol 32 (1) ◽

pp. 37-57 ◽

Cited By ~ 3

Author(s):

Yi Wu ◽

Xuejun Wang ◽

Soo Hak Sung

Keyword(s):

Numerical Simulation ◽

Regression Model ◽

Random Variables ◽

Complete Moment Convergence ◽

Parametric Regression ◽

Associated Random Variables ◽

Negatively Associated ◽

Complete Consistency ◽

Moment Convergence ◽

Non Parametric

In this paper, some results on the complete moment convergence for arrays of rowwise negatively associated (NA, for short) random variables are established. The results obtained in this paper correct the corresponding one obtained in Ko [13] and also improve and generalize the corresponding ones of Kuczmaszewska [14] and Ko [13]. As an application of the main results, we present a result on complete consistency for the estimator in a non-parametric regression model based on NA errors. Finally, we provide a numerical simulation to verify the validity of our result.

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