Non-parametric heteroscedastic transformation regression models for skewed data with an application to health care costs

• 成果类型: Article
• 署名作者: Zhou, Xiao-Hua; Lin, Huazhen; Johnson, Eric
• 署名单位: US Department of Veterans Affairs; Veterans Health Administration (VHA); Vet Affairs Puget Sound Health Care System; University of Washington; University of Washington Seattle; Sichuan University
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/j.1467-9868.2008.00669.x
• 发表日期: 2008
• 页码: 1029-1047
• 关键词: retransformation; ado

摘要

We develop a new non-parametric heteroscedastic transformation regression model for predicting the expected value of the outcome of a patient with given patient's covariates when the distribution of the outcome is highly skewed with a heteroscedastic variance. In our model, we allow both the transformation function and the error distribution function to be unknown. We show that under some regularity conditions the estimators for regression parameters, the expected value of the original outcome and the transformation function converge to their true values at the rate n(-1/2). In our simulation studies, we demonstrate that our proposed non-parametric method is robust with little loss of efficiency. Finally, we apply our model to a study on health care costs.