کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1147811 | 957798 | 2010 | 10 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A semi-parametric approach to dual modeling when no replication exists
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
ریاضیات
ریاضیات کاربردی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
In many applications, it is of interest to simultaneously model the mean and variance of a response when no replication exists. Modeling the mean and variance simultaneously is commonly referred to as dual modeling. Parametric approaches to dual modeling are popular when the underlying mean and variance functions can be expressed explicitly. Quite often, however, nonparametric approaches are more appropriate due to the presence of unusual curvature in the underlying functions. In sparse data situations, nonparametric methods often fit the data too closely while parametric estimates exhibit problems with bias. We propose a semi-parametric dual modeling approach [Dual Model Robust Regression (DMRR)] for non-replicated data. DMRR combines parametric and nonparametric fits resulting in improved mean and variance estimation. The methodology is illustrated with a data set from the literature as well as via a simulation study.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Statistical Planning and Inference - Volume 140, Issue 10, October 2010, Pages 2860-2869
Journal: Journal of Statistical Planning and Inference - Volume 140, Issue 10, October 2010, Pages 2860-2869
نویسندگان
Timothy J. Robinson, Jeffrey B. Birch, B. Alden Starnes,