Model Selection via Misspecified Cramér-Rao Bound Minimization.

Nadav E. Rosenthal, Joseph Tabrikian

نتاج البحث: فصل من :كتاب / تقرير / مؤتمرمنشور من مؤتمرمراجعة النظراء

ملخص

In many applications of estimation theory, the true data model is unknown, and a set of parameterized models are used to approximate it. This problem is encountered in learning systems, where the assumed model parameters are estimated using training data. One of the challenges in these problems is choosing the architecture used for the approximated model. Complex and high-order models with limited training data size may lead to overfitting, while simple and low-order models may lead to model misspecification. In this paper, we propose to use the misspecified Cramér-Rao bound (MCRB) as a criterion for model selection. The MCRB takes into account modeling errors due to both overfitting and model misspecification. The performance of the proposed approach is evaluated via simulations for model order selection in a linear regression problem. The proposed method outperforms the minimum description length and the Akaike information criterion.

اللغة الأصليةإنجليزيّة أمريكيّة
عنوان منشور المضيف2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings
الصفحات5762-5766
عدد الصفحات5
رقم المعيار الدولي للكتب (الإلكتروني)9781665405409
المعرِّفات الرقمية للأشياء
حالة النشرنُشِر - مايو 2022
الحدث47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Virtual, Online, سنغافورة
المدة: ٢٣ مايو ٢٠٢٢٢٧ مايو ٢٠٢٢

سلسلة المنشورات

الاسمICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
مستوى الصوت2022-May

!!Conference

!!Conference47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022
الدولة/الإقليمسنغافورة
المدينةVirtual, Online
المدة٢٣/٠٥/٢٢٢٧/٠٥/٢٢

All Science Journal Classification (ASJC) codes

  • !!Software
  • !!Signal Processing
  • !!Electrical and Electronic Engineering

بصمة

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