Deviations from theoretical assumptions together with the presence of certain amount of
outlying observations are common in many practical statistical applications. This is also the …

Clustering is one of the most widely used unsupervised learning techniques. However, it is
well‐known that outliers can have a significantly adverse impact on commonly applied …

Longitudinal data is becoming increasingly common and various methods have been
developed to analyze this type of data. Profiles from time-course gene expression studies …

We introduce a maximum L q-likelihood estimation (ML q E) of mixture models using our
proposed expectation-maximization (EM) algorithm, namely the EM algorithm with L q …

Historical and recent developments in the field of robust clustering and their applications are
reviewed. The discussion focuses on different strategies that have been developed to …

This chapter introduces several fundamental models and algorithms for probabilistic
clustering, including mixture models, Expectation-Maximization (EM) algorithm, and …

Mixture models are often used to compactly represent samples from heterogeneous
sources. However, in real world, the samples generally contain an unknown fraction of …

This study traces coastal sand dune development processes in East Asia during the
Holocene to understand climate and environmental changes. We used optically stimulated …

A study on the codominant scoring of AFLP markers in association panels without prior
knowledge on genotype probabilities is described. Bands are scored codominantly by fitting …

Finite mixture models have been used extensively in clustering applications, where each
component of the mixture distribution is assumed to represent an individual cluster. The …