Titlebook: Directional Statistics for Innovative Applications; A Bicentennial Tribu Ashis SenGupta,Barry C. Arnold Book 2022 The Editor(s) (if applica

maverick

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

chronology

https://doi.org/10.1007/978-981-19-1044-9Directional Statistics; Multivariate Analysis; Regression Analysis; Big Data Analytics; Statistical Mach

悄悄移動(dòng)

978-981-19-1046-3The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor

MEET

話

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

討厭

regale

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

聽寫

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

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