التفاصيل البيبلوغرافية
| العنوان: |
Asymptotic and bootstrap tests for subspace dimension |
| المؤلفون: |
Nordhausen, Klaus, Oja, Hannu, Tyler, David E. |
| بيانات النشر: |
Elsevier |
| سنة النشر: |
2022 |
| المجموعة: |
JYX - Jyväskylä University Digital Archive / Jyväskylän yliopiston julkaisuarkisto |
| مصطلحات موضوعية: |
Order determination, Principal component analysis, Sliced inverse regression, monimuuttujamenetelmät, riippumattomien komponenttien analyysi, estimointi |
| الوصف: |
Many linear dimension reduction methods proposed in the literature can be formulated using an appropriate pair of scatter matrices. The eigen-decomposition of one scatter matrix with respect to another is then often used to determine the dimension of the signal subspace and to separate signal and noise parts of the data. Three popular dimension reduction methods, namely principal component analysis (PCA), fourth order blind identification (FOBI) and sliced inverse regression (SIR) are considered in detail and the first two moments of subsets of the eigenvalues are used to test for the dimension of the signal space. The limiting null distributions of the test statistics are discussed and novel bootstrap strategies are suggested for the small sample cases. In all three cases, consistent test-based estimates of the signal subspace dimension are introduced as well. The asymptotic and bootstrap tests are illustrated in real data examples. ; peerReviewed |
| نوع الوثيقة: |
article in journal/newspaper |
| وصف الملف: |
application/pdf; fulltext |
| اللغة: |
English |
| تدمد: |
0047-259X |
| العلاقة: |
Journal of Multivariate Analysis; 188; Nordhausen, K., Oja, H., & Tyler, D. E. (2022). Asymptotic and bootstrap tests for subspace dimension. Journal of Multivariate Analysis , 188 , Article 104830. https://doi.org/10.1016/j.jmva.2021.104830Test; CONVID_101039114; URN:NBN:fi:jyu-202112206029; http://urn.fi/URN:NBN:fi:jyu-202112206029Test |
| الإتاحة: |
http://urn.fi/URN:NBN:fi:jyu-202112206029Test |
| حقوق: |
CC BY 4.0 ; © 2021 the Authors ; openAccess ; https://creativecommons.org/licenses/by/4.0Test/ |
| رقم الانضمام: |
edsbas.128B8920 |
| قاعدة البيانات: |
BASE |
