B0218
Title: Poisson PCA for matrix count data
Authors: Joni Virta - University of Turku (Finland) [presenting]
Andreas Artemiou - University of Limassol (Cyprus)
Abstract: A dimension reduction framework is developed for data consisting of matrices of counts. The model is based on the assumption of the existence of a small amount of independent normal latent variables that drive the dependency structure of the observed data and can be seen as the exact discrete analogue of a contaminated low-rank matrix normal model. Estimators are derived for the model parameters and establish their limiting normality. An extension of a recent proposal from the literature is used to estimate the latent dimension of the model. The method is shown to outperform both its vectorization-based competitors and matrix methods assuming the continuity of the data distribution in analysing simulated data and real-world abundance data.
