التفاصيل البيبلوغرافية
| العنوان: |
KSD Aggregated Goodness-of-fit Test |
| المؤلفون: |
Schrab, Antonin, Guedj, Benjamin, Gretton, Arthur |
| المساهمون: |
Gatsby Computational Neuroscience Unit, Department of Computer science University College of London UCL-CS, The Inria London Programme Inria-London, MOdel for Data Analysis and Learning MODAL, The Alan Turing Institute |
| سنة النشر: |
2025 |
| المجموعة: |
LillOA (Lille Open Archive - Université de Lille) |
| الوصف: |
We investigate properties of goodness-of-fit tests based on the Kernel Stein Discrepancy (KSD). We introduce a strategy to construct a test, called KSDAGG, which aggregates multiple tests with different kernels. KSDAGG avoids splitting the data to perform kernel selection (which leads to a loss in test power), and rather maximises the test power over a collection of kernels. We provide theoretical guarantees on the power of KSDAGG: we show it achieves the smallest uniform separation rate of the collection, up to a logarithmic term. KSDAGG can be computed exactly in practice as it relies either on a parametric bootstrap or on a wild bootstrap to estimate the quantiles and the level corrections. In particular, for the crucial choice of bandwidth of a fixed kernel, it avoids resorting to arbitrary heuristics (such as median or standard deviation) or to data splitting. We find on both synthetic and real-world data that KSDAGG outperforms other state-of-the-art adaptive KSDbased goodness-of-fit testing procedures. |
| نوع الوثيقة: |
other/unknown material |
| وصف الملف: |
application/octet-stream |
| اللغة: |
English |
| Relation: |
http://hdl.handle.net/20.500.12210/122447 |
| الاتاحة: |
https://hdl.handle.net/20.500.12210/122447 |
| Rights: |
info:eu-repo/semantics/openAccess |
| رقم الانضمام: |
edsbas.899C49F7 |
| قاعدة البيانات: |
BASE |