Identication of representative regimes of wave height and direction under different wind conditions is complicated by issues that relate to the specication of the joint distribution of variables that are defined on linear and circular supports and the occurrence of missing values. We take a latent-class approach and jointly model wave and wind data by a finite mixture of conditionally independent Gamma and von Mises distributions. Maximum-likelihood estimates of parameters are obtained by exploiting a suitable EM algorithm that allows for missing data. The proposed model is validated on hourly marine data obtained from a buoy and two tide gauges in the Adriatic Sea.

LAGONA F, & PICONE M (2011). A latent-class model for clustering incomplete linear and circular data in marine studies. JOURNAL OF DATA SCIENCE, 9, 585-605.

A latent-class model for clustering incomplete linear and circular data in marine studies

LAGONA, Francesco;
2011

Abstract

Identication of representative regimes of wave height and direction under different wind conditions is complicated by issues that relate to the specication of the joint distribution of variables that are defined on linear and circular supports and the occurrence of missing values. We take a latent-class approach and jointly model wave and wind data by a finite mixture of conditionally independent Gamma and von Mises distributions. Maximum-likelihood estimates of parameters are obtained by exploiting a suitable EM algorithm that allows for missing data. The proposed model is validated on hourly marine data obtained from a buoy and two tide gauges in the Adriatic Sea.
LAGONA F, & PICONE M (2011). A latent-class model for clustering incomplete linear and circular data in marine studies. JOURNAL OF DATA SCIENCE, 9, 585-605.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/145906
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