کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10525120 | 957902 | 2011 | 6 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Fitting probability forecasting models by scoring rules and maximum likelihood
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
ریاضیات
ریاضیات کاربردی
پیش نمایش صفحه اول مقاله
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چکیده انگلیسی
Probability forecasting models can be estimated using weighted score functions that (by definition) capture the performance of the estimated probabilities relative to arbitrary “baseline” probability assessments, such as those produced by another model, by a bookmaker or betting market, or by a human probability assessor. Maximum likelihood estimation (MLE) is interpretable as just one such method of optimum score estimation. We find that when MLE-based probabilities are themselves treated as the baseline, forecasting models estimated by optimizing any of the proven families of power and pseudospherical economic score functions yield the very same probabilities as MLE. The finding that probabilities estimated by optimum score estimation respond to MLE-baseline probabilities by mimicking them supports reliance on MLE as the default form of optimum score estimation.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Statistical Planning and Inference - Volume 141, Issue 5, May 2011, Pages 1832-1837
Journal: Journal of Statistical Planning and Inference - Volume 141, Issue 5, May 2011, Pages 1832-1837
نویسندگان
David Johnstone, Yan-Xia Lin,