Bayesian modelling for asymmetric multi-modal circular data

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Tezin Türü: Doktora

Tezin Yürütüldüğü Kurum: Orta Doğu Teknik Üniversitesi, Fen Edebiyat Fakültesi, İstatistik Bölümü, Türkiye

Tezin Onay Tarihi: 2015

Öğrenci: MUHAMMET BURAK KILIÇ

Danışman: ZEYNEP IŞIL KALAYLIOĞLU AKYILDIZ

Özet:

In this thesis, we propose a Bayesian methodology based on sampling importance re-sampling for asymmetric and bimodal circular data analysis. We adopt Dirichlet process (DP) mixture model approach to analyse multi-modal circular data where the number of components is not known. For the analysis of temporal circular data, such as hourly measured wind directions, we join DP mixture model approach with circular times series modelling. The approaches are illustrated with both simulated and real life data sets. Our Bayesian methodologies have been shown to have good statistical properties in multi-modal circular data analysis. Computational codes for DP mixture models are constructed in OpenBUGS and R.