maverick
发表于 2025-3-23 13:12:21
https://doi.org/10.1007/978-981-10-8908-4ctedly large, leading to the need to update the current model. This model is based on very high-order spherical polynomials, and so we instead consider obtaining predictions under very mild assumptions. To this end, we propose a nonparametric approach based on sphere–sphere regression based on flexible (non-rigid) rotations.
AVID
发表于 2025-3-23 13:56:49
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chronology
发表于 2025-3-23 20:11:09
https://doi.org/10.1007/978-981-19-1044-9Directional Statistics; Multivariate Analysis; Regression Analysis; Big Data Analytics; Statistical Mach
悄悄移动
发表于 2025-3-23 22:47:57
978-981-19-1046-3The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
MEET
发表于 2025-3-24 05:43:00
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话
发表于 2025-3-24 07:28:24
Set-Membership Adaptive Filteringibility. Previous work on such mixtures has used an approximate maximum likelihood estimator for the parameters of a single component. However, the approximation causes problems when using the EM algorithm to estimate the parameters in a mixture model. Hence, the exact maximum likelihood estimator i
讨厌
发表于 2025-3-24 13:54:31
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regale
发表于 2025-3-24 16:13:27
Jay Farrell,Manu Sharma,Marios Polycarpoun ground space. Fundamental to this are distributional limit laws, and we derive a central limit theorem for the empirical OT distance of circular data. Our limit results require only mild assumptions in general and include prominent examples such as the von Mises or wrapped Cauchy family. Most nota
听写
发表于 2025-3-24 20:57:30
https://doi.org/10.1007/978-981-10-8908-4ctedly large, leading to the need to update the current model. This model is based on very high-order spherical polynomials, and so we instead consider obtaining predictions under very mild assumptions. To this end, we propose a nonparametric approach based on sphere–sphere regression based on flexi
airborne
发表于 2025-3-25 01:32:37
https://doi.org/10.1007/978-981-10-8908-4tribution models aim to capture not only location or concentration features, but also peakedness and skewness, and one may consider nonparametric approaches (such as kernel method) for that purpose. However, if there is also an interest in interpreting the aforementioned characteristics, then flexib