Model selection when a key parameter is constrained to be in an interval

M. Z. Hossain, M. L. King

نتاج البحث: المساهمة في مجلةمراجعة النظراء

ملخص

This article considers model selection procedures based on choosing the model with the largest maximized log-likelihood minus a penalty, when key parameters are restricted to be in a closed interval. Its main emphasis is how these penalties might be chosen in small samples to give good properties of the resultant procedure. We illustrate two model selection problems in the context of Box-Cox transformations and their application to the linear regression model. Simulation results for both problems indicate that the new procedure clearly dominates existing procedures in terms of having higher probabilities of correctly selecting the true model.

اللغة الأصليةEnglish
الصفحات (من إلى)1270-1280
عدد الصفحات11
دوريةCommunications in Statistics: Simulation and Computation
مستوى الصوت37
رقم الإصدار7
المعرِّفات الرقمية للأشياء
حالة النشرPublished - أغسطس 2008
منشور خارجيًانعم

ASJC Scopus subject areas

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