The entropy based goodness of fit tests for generalized von Mises-Fisher distributions and beyond


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Leonenko N., Makogin V., ÇADIRCI M. S.

Electronic Journal of Statistics, cilt.15, sa.2, ss.6344-6381, 2021 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 15 Sayı: 2
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1214/21-ejs1946
  • Dergi Adı: Electronic Journal of Statistics
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, MathSciNet, zbMATH
  • Sayfa Sayıları: ss.6344-6381
  • Anahtar Kelimeler: Directional distribution, entropy estimation, generalized von Mises– Fisher distribution, goodness of fit test, maximum entropy principle, nearest neighbour estimator
  • Sivas Cumhuriyet Üniversitesi Adresli: Evet

Özet

We introduce some new classes of unimodal rotational invariant directional distributions, which generalize von Mises–Fisher distribution. We propose three types of distributions, one of which represents axial data. For each new type we provide formulae and short computational study of parameter estimators by the method of moments and the method of maximum likelihood. The main goal of the paper is to develop the goodness of fit test to detect that sample entries follow one of the introduced generalized von Mises–Fisher distribution based on the maximum entropy principle. We use kth nearest neighbour distances estimator of Shannon entropy and prove its L2-consistency. We examine the behaviour of the test statistics, find critical values and compute power of the test on simulated samples. We apply the goodness of fit test to local fiber directions in a glass fibre reinforced composite material and detect the samples which follow axial generalized von Mises–Fisher distribution.