scholarly journals A flexible model for the mean and variance functions, with application to medical cost data

2013 ◽  
Vol 32 (24) ◽  
pp. 4306-4318 ◽  
Author(s):  
Jinsong Chen ◽  
Lei Liu ◽  
Daowen Zhang ◽  
Ya-Chen T. Shih
Keyword(s):  
Medical Cost ◽  
Cost Data ◽  
Variance Functions ◽  
Medical Cost Data ◽  
Mean And Variance ◽  
The Mean ◽  
Flexible Model

Modeling right-censored medical cost data in regression and the effects of covariates

2018 ◽  
Vol 28 (1) ◽  
pp. 143-155
Author(s):  
Lu Deng ◽  
Wendy Lou ◽  
Nicholas Mitsakakis
Keyword(s):  
Medical Cost ◽  
Cost Data ◽  
Medical Cost Data

scholarly journals EVALUASI NUMERIK PENDUGA FUNGSI NILAI HARAPAN DAN FUNGSI RAGAM PROSES POISSON MAJEMUK DENGAN INTENSITAS EKSPONENSIAL FUNGSI LINEAR

2018 ◽  
Vol 17 (2) ◽  
pp. 157
Author(s):  
S. UTAMI ◽  
I W. MANGKU ◽  
I G. P. PURNABA
Keyword(s):  
Monte Carlo ◽  
Confidence Intervals ◽  
Observation Interval ◽  
Compound Poisson Process ◽  
Intensity Function ◽  
Significance Level ◽  
Variance Functions ◽  
Mean And Variance ◽  
The Mean ◽  
Strongly Consistent

<em>Performances of estimators for the mean and variance functions of a compound Poisson process having intensity obtained as an exponential of linear function are investigated using Monte Carlo simulations. The intensity function of this process is assumed to be </em>𝑒𝑥𝑝(𝛼+𝛽𝑠) <em>with </em>0&lt;𝛽&lt;<em>∞</em>, <em>where </em>𝛽 <em>is assumed to be known. In [8], estimators of the mean and variance functions of this process have been constructed and have been proved to be unbiased, weakly and strongly consistent. The objectives of this research are to check distributions of these estimators using Monte Carlo simulation and to check the convergence to </em>1−𝛼 <em>of the probabilities that the parameters are contained in the confidence intervals constructed in [11]. Results of the research are as follows. Distribution of estimators for the mean and variance functions are approximately normal. For a given significance level </em>𝛼<em>, the larger the size of observation interval, the closer the probabilities that the parameters are contained in the confidence intervals to </em>1−𝛼<em>.</em>

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