Description Usage Format References Examples
These data come from Venturini's (2015) study of hospital costs for patients with smoking and non-smoking diseases.
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A data frame with 9416 observations on the following 2 variables.
x
A binary indicator variable: 0 for non-smoking disease, 1 for smoking disease
y
The response variable: cost of a hospital stay, in dollars
Dominici, F., Cope, L., Naiman, D. Q., and Zeger, S. L. (2005), "Smooth quantile ratio estimation," Biometrika, 92, 543-557.
Dominici, F. and Zeger, S. L. (2005), "Smooth quantile ratio estimation with regression: estimating medical expenditures for smoking-attributable diseases," Biostatistics, 6, 505-519.
Johnson, E., Dominici, F., Griswold, M., and Zeger, S. L. (2003), "Disease cases and their medical costs attributable to smoking: an analysis of the national medical expenditure survey," Journal of Econometrics, 112, 135-151.
Venturini, S., Dominici, F., Parmigiani, G., et al. (2015), "Generalized quantile treatment effect: A flexible Bayesian approach using quantile ratio smoothing," Bayesian Analysis, 10, 523-552.
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