کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1150587 | 957960 | 2007 | 14 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Non-identifiable parametric probability models and reparametrization
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موضوعات مرتبط
مهندسی و علوم پایه
ریاضیات
ریاضیات کاربردی
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چکیده انگلیسی
Identifiability is a primary assumption in virtually all classical statistical theory. However, such an assumption may be violated in a variety of statistical models. We consider parametric models where the assumption of identifiability is violated, but otherwise satisfy standard assumptions. We propose an analytic method for constructing new parameters under which the model will be at least locally identifiable. This method is based on solving a system of linear partial differential equations involving the Fisher information matrix. Some consequences and valid inference procedures under non-identifiability have been discussed. The method of reparametrization is illustrated with an example.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Statistical Planning and Inference - Volume 137, Issue 11, 1 November 2007, Pages 3380–3393
Journal: Journal of Statistical Planning and Inference - Volume 137, Issue 11, 1 November 2007, Pages 3380–3393
نویسندگان
Abhijit Dasgupta, Steven G. Self, Somesh Das Gupta,