[BOOK][B] Introduction to clustering
In this chapter, the basic concepts of clustering are introduced. Moreover, the most relevant
decisions to be made for the practical application of clustering methods are listed and briefly …
decisions to be made for the practical application of clustering methods are listed and briefly …
A new look at the inverse Gaussian distribution with applications to insurance and economic data
A Punzo - Journal of Applied Statistics, 2019 - Taylor & Francis
Insurance and economic data are often positive, and we need to take into account this
peculiarity in choosing a statistical model for their distribution. An example is the inverse …
peculiarity in choosing a statistical model for their distribution. An example is the inverse …
Mixtures of matrix-variate contaminated normal distributions
Analysis of matrix-variate data is becoming ever more prevalent in the literature, especially
in the area of clustering and classification. Real data, including real matrix-variate data, are …
in the area of clustering and classification. Real data, including real matrix-variate data, are …
Fitting insurance and economic data with outliers: a flexible approach based on finite mixtures of contaminated gamma distributions
Insurance and economic data are frequently characterized by positivity, skewness,
leptokurtosis, and multi-modality; although many parametric models have been used in the …
leptokurtosis, and multi-modality; although many parametric models have been used in the …
Robust clustering in regression analysis via the contaminated Gaussian cluster-weighted model
The Gaussian cluster-weighted model (CWM) is a mixture of regression models with random
covariates that allows for flexible clustering of a random vector composed of response …
covariates that allows for flexible clustering of a random vector composed of response …
Robust model-based clustering with mild and gross outliers
We propose a model-based clustering procedure where each component can take into
account cluster-specific mild outliers through a flexible distributional assumption, and a …
account cluster-specific mild outliers through a flexible distributional assumption, and a …
Hidden Markov and semi-Markov models with multivariate leptokurtic-normal components for robust modeling of daily returns series
We introduce multivariate models for the analysis of stock market returns. Our models are
developed under hidden Markov and semi-Markov settings to describe the temporal …
developed under hidden Markov and semi-Markov settings to describe the temporal …
Mixtures of multivariate contaminated normal regression models
Mixtures of regression models (MRMs) are widely used to investigate the relationship
between variables coming from several unknown latent homogeneous groups. Usually, the …
between variables coming from several unknown latent homogeneous groups. Usually, the …
The multivariate tail-inflated normal distribution and its application in finance
The research objective of this paper is to handle situations where the empirical distribution
of multivariate real-valued data is elliptical and with heavy tails. Many statistical models …
of multivariate real-valued data is elliptical and with heavy tails. Many statistical models …
Missing values and directional outlier detection in model-based clustering
Abstract Model-based clustering tackles the task of uncovering heterogeneity in a data set to
extract valuable insights. Given the common presence of outliers in practice, robust methods …
extract valuable insights. Given the common presence of outliers in practice, robust methods …