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A unified approach to goodness-of-fit testing for spherical and hyperspherical data
We propose a general and relatively simple method to construct goodness-of-fit tests on the sphere and the hypersphere. The method is based on the...
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Model-based clustering and outlier detection with missing data
The use of the multivariate contaminated normal (MCN) distribution in model-based clustering is recommended to cluster data characterized by mild...
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The Test for Correlation with Metric, Ordinal, and Nominal Data
In Chap. 6 we explored the question of the relationship between two variables, i.e. how and whether two...
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Detecting and classifying outliers in big functional data
We propose two new outlier detection methods, for identifying and classifying different types of outliers in (big) functional data sets. The proposed...
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Natural-neighborhood based, label-specific undersampling for imbalanced, multi-label data
This work presents a novel undersampling scheme to tackle the imbalance problem in multi-label datasets. We use the principles of the natural nearest...
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Mixture Models for Spherical Data with Applications to Protein Bioinformatics
Finite mixture models are fitted to spherical data. Kent distributions are used for the components of the mixture because they allow considerable...
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Special Matrices and Operations Useful in Modeling and Data Science
In previous chapters, we encountered a number of special matrices, such as symmetric matrices, banded matrices, elementary operator matrices, and so...
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The Cosine Depth Distribution Classifier for Directional Data
Directions, rotations, axes, clock, or calendar measurements can be represented as angles or equivalently as unit vectors. As points lying on the...
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Estimating a Mixing Distribution on the Sphere Using Predictive Recursion
Mixture models are commonly used when data show signs of heterogeneity and, often, it is important to estimate the distribution of the latent...
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Student’s-t process with spatial deformation for spatio-temporal data
Many models for environmental data that are observed in time and space have been proposed in the literature. The main objective of these models is...
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Improving kernel-based nonparametric regression for circular–linear data
We discuss kernel-based nonparametric regression where a predictor has support on a circle and a responder has support on a real line. Nonparametric...
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Log-Linear and Log-Multiplicative Association Models for Categorical Data
The chapter reviews uni- and multidimensional association models (AMs) that provide a parsimonious modelling of interactions between categorical...
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Multitemporal Data Analysis with Marked Point Processes
In this chapter, we introduce new approaches for object-level dynamic scene modeling based on multitemporal measurements, by extending the...
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A multi-way analysis of similarity patterns in longevity improvements
With the increasing availability of temporal data, a researcher often analyzes information saved into matrices, in which entries are replicated in...
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Locating \(\gamma\)-ray sources on the celestial sphere via modal clustering
Sky surveys represent the fundamental data basis for detecting and locating as yet undiscovered celestial objects. Since 2008, the Fermi LAT...
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Directional bivariate quantiles: a robust approach based on the cumulative distribution function
The definition of multivariate quantiles has gained considerable attention in previous years as a tool for understanding the structure of a...
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Analyzing the Profitability and Efficiency in European Non-Life Insurance Industry
This paper proposes a new three-step approach to build an Optimal Production Plan for insurance companies. Two techniques are used compared to the...
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Bayesian Models for Analysis of Inventory and Monitoring Data with Non-ignorable Missingness
We describe the application of Bayesian hierarchical models to the analysis of data from long-term, environmental monitoring programs. The goal of...
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Data projections by skewness maximization under scale mixtures of skew-normal vectors
Multivariate scale mixtures of skew-normal distributions are flexible models that account for the non-normality of data by means of a tail weight...
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Geometric Properties of Non-parametric MLE and Numerical Solutions
In Chap. 6, our attention turns to numerical solutions for maximum likelihood estimation within the context of nonparametric mixture models. The...