A weakly informative default prior distribution for logistic and other regression models
نویسندگان
چکیده
منابع مشابه
A Weakly Informative Default Prior Distribution for Logistic and Other Regression Models By
We propose a new prior distribution for classical (nonhierarchical) logistic regression models, constructed by first scaling all nonbinary variables to have mean 0 and standard deviation 0.5, and then placing independent Student-t prior distributions on the coefficients. As a default choice, we recommend the Cauchy distribution with center 0 and scale 2.5, which in the simplest setting is a lon...
متن کاملA weakly informative default prior distribution for logistic and other regression models
We propose a new prior distribution for classical (non-hierarchical) logistic regression models, constructed by first scaling all nonbinary variables to have mean 0 and standard deviation 0.5, and then placing independent Student-t prior distributions on the coefficients. As a default choice, we recommend the Cauchy distribution with center 0 and scale 2.5, which in the simplest setting is a lo...
متن کاملA default prior distribution for logistic and other regression models
We propose a new prior distribution for classical (non-hierarchical) logistic regression models, constructed by first scaling all nonbinary variables to have mean 0 and standard deviation 0.5, and then placing independent Student-t prior distributions on the coefficients. As a default choice, we recommend the Cauchy distribution with center 0 and scale 2.5, which in the simplest setting is a lo...
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In this paper, we used simulations to compare the performance of classical and Bayesian estimations in logistic regression models using small samples. In the performed simulations, conditions were varied, including the type of relationship between independent and dependent variable values (i.e., unrelated and related values), the type of variable (i.e., binary and continuous), and different Bin...
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ژورنال
عنوان ژورنال: The Annals of Applied Statistics
سال: 2008
ISSN: 1932-6157
DOI: 10.1214/08-aoas191