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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\u22121\/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.<\/jats:p>","DOI":"10.1111\/j.1467-9868.2008.00669.x","type":"journal-article","created":{"date-parts":[[2008,9,16]],"date-time":"2008-09-16T16:16:56Z","timestamp":1221581816000},"page":"1029-1047","source":"Crossref","is-referenced-by-count":30,"title":["Non-Parametric Heteroscedastic Transformation Regression Models for Skewed Data With an Application to Health Care Costs"],"prefix":"10.1093","volume":"70","author":[{"given":"Xiao-Hua","family":"Zhou","sequence":"first","affiliation":[{"name":"Veterans Affairs Puget Sound Health Care System , Seattle"},{"name":"University of Washington , Seattle , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huazhen","family":"Lin","sequence":"additional","affiliation":[{"name":"Veterans Affairs Puget Sound Health Care Systems , Seattle , USA"},{"name":"Sichuan University , Chengdu , People's Republic of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eric","family":"Johnson","sequence":"additional","affiliation":[{"name":"University of Washington , Seattle , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2008,9,16]]},"reference":[{"key":"2023040800091429700_","first-page":"7","article-title":"Using diagnoses to describe populations and predict costs","volume":"21","author":"Ash","year":"2000","journal-title":"Hlth Care Finan. 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