Academic Journal

Kernel methods for optimal change-points estimation in derivatives ∗

التفاصيل البيبلوغرافية
العنوان: Kernel methods for optimal change-points estimation in derivatives ∗
المؤلفون: Ming-yen Cheng, Marc Raimondo
المساهمون: The Pennsylvania State University CiteSeerX Archives
المصدر: http://www.homepages.ucl.ac.uk/~ucakmc0/edit_cheng/june07final.pdf.
سنة النشر: 2007
المجموعة: CiteSeerX
الوصف: In this paper we propose an implementation of the so-called zero-crossing-time detection technique specifically designed for estimating the location of jump-points in the first derivative (kinks) of a regression function f. Our algorithm relies on a new class of kernel functions having a second derivative with vanishing moments and an asymmetric first derivative steep enough near the origin. We provide a software package which, for a sample of size n, produces estimators with an accuracy of order, at least, O(n −2/5). This contrasts with current algorithms for kink estimation which at best provide an accuracy of order O(n −1/3). In the software, the kernel statistic is standardised and compared to the universal threshold to test the existence of a kink. A simulation study shows that our algorithm enjoys very good finite sample properties even for low sample sizes. The method reveals kink features in real data sets with high noise levels at places where traditional smoothers tend to oversmooth the data. 1
نوع الوثيقة: text
وصف الملف: application/pdf
اللغة: English
Relation: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.331.2516; http://www.homepages.ucl.ac.uk/~ucakmc0/edit_cheng/june07final.pdf
الاتاحة: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.331.2516
http://www.homepages.ucl.ac.uk/~ucakmc0/edit_cheng/june07final.pdf
Rights: Metadata may be used without restrictions as long as the oai identifier remains attached to it.
رقم الانضمام: edsbas.87C5B829
قاعدة البيانات: BASE