scholarly journals Bayesian model averaging in the instrumental variable regression model

2012 ◽  
Vol 171 (2) ◽  
pp. 237-250 ◽  
Author(s):  
Gary Koop ◽  
Roberto Leon-Gonzalez ◽  
Rodney Strachan
Author(s):  
Harun Al Azies ◽  
Vivi Mentari Dewi

This study predicts the factors that influence life expectancy in East Java, Indonesia. In particular, this study compares the prediction results between the linear regression model and the Bayesian Model Averaging (BMA). The study used a 2015 data set from the Central Bureau of Statistics (BPS) of the province of East Java.The results of data exploration show that the life expectancy in East Java is 70.68 years, the Bondowoso regency is the region with the lowest life expectancy at 65.73 years and the city of Surabaya is the area with the highest life expectancy value in East Java, which is 73.85 years.The results of the inference study indicate that the factors that are expected to affect life expectancy in East Java are the infant mortality rate and the illiteracy rate of the population aged 10 and over.The results of the comparison between the BMA and the regression show that the BMA is a better model for predicting the factors that affect life expectancy in East Java than the regression model because the BMA model can estimate the parameters more efficiently by estimating the model parameters based on the standard error value.


Author(s):  
Lorenzo Bencivelli ◽  
Massimiliano Giuseppe Marcellino ◽  
Gianluca Moretti

Nutrients ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 1098
Author(s):  
Ewelina Łukaszyk ◽  
Katarzyna Bień-Barkowska ◽  
Barbara Bień

Identifying factors that affect mortality requires a robust statistical approach. This study’s objective is to assess an optimal set of variables that are independently associated with the mortality risk of 433 older comorbid adults that have been discharged from the geriatric ward. We used both the stepwise backward variable selection and the iterative Bayesian model averaging (BMA) approaches to the Cox proportional hazards models. Potential predictors of the mortality rate were based on a broad range of clinical data; functional and laboratory tests, including geriatric nutritional risk index (GNRI); lymphocyte count; vitamin D, and the age-weighted Charlson comorbidity index. The results of the multivariable analysis identified seven explanatory variables that are independently associated with the length of survival. The mortality rate was higher in males than in females; it increased with the comorbidity level and C-reactive proteins plasma level but was negatively affected by a person’s mobility, GNRI and lymphocyte count, as well as the vitamin D plasma level.


2015 ◽  
Vol 57 (3) ◽  
pp. 485-493 ◽  
Author(s):  
Yutaka Osada ◽  
Takeo Kuriyama ◽  
Masahiko Asada ◽  
Hiroyuki Yokomizo ◽  
Tadashi Miyashita

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