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Activity Number: 5
Type: Invited
Date/Time: Sunday, July 29, 2012 : 2:00 PM to 3:50 PM
Sponsor: Section on Nonparametric Statistics
Abstract - #303651
Title: The Quantile Approach to the Power Transformed Location-Scale Model
Author(s): Hyokyoung Grace Hong*+
Companies: City University of New York
Address: One Bernard Baruch Way Box 11-220, New York, NY, 10010,
Keywords: Location-scale model ; medical expenditure ; Power transformation ; Quantile regression
Abstract:

The growth of health care spending has become a major concern to policy makers, making the modeling of health care expenditure valuable in their decision-making processes. The challenging features in the analysis of health care expenditure are two folds: the exceptional skewness of its distribution - top 5% of the population accounted for almost half of all spending - and its heteroscedasticity. The quantile regression model with power transformation has been employed, among many others, to address these challenges, but at a price of the model complexity and analysis cost. In this article, we introduce a simpler quantile approach to the analysis of expenditure data by employing the location-scale model with an unknown link function to accommodate the heteroscedastic data with non-ignorable outliers. Specifically, in our approach a link function and the slope parameters in the location and scale parts do not depend on quantiles, and yet it effectively fits the data. This parsimonious feature of our model helps us induce the more intuitive and easily understood analysis for the whole distribution with the less computational steps. Thus, it can be more widely applicable in practice.


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