Asymptotic and bootstrap tests for subspace dimension
| العنوان: | Asymptotic and bootstrap tests for subspace dimension |
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| المؤلفون: | Tyler David E., Nordhausen Klaus, Oja Hannu |
| المساهمون: | matematiikka, Mathematics, 2606101 |
| بيانات النشر: | Academic Press Inc. United States Yhdysvallat (USA) US |
| سنة النشر: | 2022 |
| المجموعة: | University of Turku: UTUPub / Turun yliopisto |
| 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. |
| نوع الوثيقة: | other/unknown material |
| اللغة: | English |
| تدمد: | 1095-7243 0047-259X |
| العلاقة: | Journal of Multivariate Analysis; https://www.utupub.fi/handle/10024/164064Test; URN:NBN:fi-fe2021102752650 |
| الإتاحة: | https://www.utupub.fi/handle/10024/164064Test |
| رقم الانضمام: | edsbas.D8560047 |
| قاعدة البيانات: | BASE |
| تدمد: | 10957243 0047259X |
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