This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 87
Type: Contributed
Date/Time: Sunday, August 1, 2010 : 4:00 PM to 5:50 PM
Sponsor: Section on Nonparametric Statistics
Abstract - #307410
Title: A Novel Semiparametric Ratio Estimator: A Key to Predicting Long-Term Weight Loss in Obesity
Author(s): Deborah Weissman-Miller*+
Companies: Dwbus & Assoc. Inc.
Address: 26 Calm Court # 5017, Ellijay, GA, 30540, USA
Keywords: Semiparametric ratio estimator ; change point ; clinical trials ; obesity
Abstract:

Obesity is a condition of public health significance in the United States. A novel semiparametric survival model is introduced to predict long-term weight loss in obesity from short-term linear regression data. This model introduces the derivation of the highest or lowest F statistic from a linear regression of initial data to determine the location of semiparametric ratio point estimator for long-term predictions. The parametric kernel density function has the form of the cumulative Weibull distribution and is derived from a mechanics model representing human beings. The kernel is given as a ratio multiplied by a prior estimated outcome variable. The results are point estimates at each time-dependent outcome. A real application of this new estimator in clinical medicine and occupational therapy is to estimate long-term weight loss for a single-subject near the beginning of treatment.


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