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Modeling Transitivity in Local Structure Graph Models
Local Structure Graph Models (LSGMs) describe network data by modeling, and thereby controlling, the local structure of networks in a direct and...
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Sensitivity analysis for unobserved confounding in causal mediation analysis allowing for effect modification, censoring and truncation
Causal mediation analysis is used to decompose the total effect of an exposure on an outcome into an indirect effect, taking the path through an...
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The relationship between shape parameters and kurtosis in some relevant models
When a distributional model is chosen, the analytic relation between its shape parameters and the values taken by some kurtosis indexes, especially...
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Positive-definite modification of a covariance matrix by minimizing the matrix \(\ell_{\infty}\) norm with applications to portfolio optimization
The covariance matrix, which should be estimated from the data, plays an important role in many multivariate procedures, and its positive...
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Adjusted Inference for the Spatial Scan Statistic
A modification is proposed to the usual inference test of the Kulldorff’s spatial scan statistic, incorporating additional information about the size...
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Community Detection in Feature-Rich Networks Using Data Recovery Approach
The problem of community detection in a network with features at its nodes takes into account both the graph structure and node features. The goal is...
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Density Peak Clustering Using Grey Wolf Optimization Approach
Density peak clustering (DPC) finds the center of the cluster as the point with high density and a large distance from the center of the other...
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Characterizations and generalizations of the negative binomial distribution
In this paper, we give detailed descriptions of the Zero-Modified Negative Binomial distribution for analyzing count data. In particular, we study...
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A Modification of the IRT-Based Standard Setting Method
We present a modification of the IRT-based standard setting method proposed by GarcÃa, Abad, Olea & Aguado (Psicothema 25(2):238–244, 2013), which we...
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Fundamentals
This chapter presents the main mathematical foundations of the problems, concepts, and methods covered by the book. First, a formal description is...
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A Model-Based Approach to Assess Epidemic Risk
We study how international flights can facilitate the spread of an epidemic to a worldwide scale. We combine an infrastructure network of flight...
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Data Depth-Based Nonparametric Tests for Multivariate Scales
The problem of comparing the scales (dispersions) of multivariate samples has been well investigated in the literature, and several parametric and...
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An enhanced version of the SSA-HJ-biplot for time series with complex structure
HJ-biplots can be used with singular spectral analysis to visualize and identify patterns in univariate time series. Named SSA-HJ-biplots, these...
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FLOWER: Viewing Data Flow in ER Diagrams
In data science, data pre-processing and data exploration require various convoluted steps such as creating variables, merging data sets, filtering...
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Clustering large mixed-type data with ordinal variables
One of the most frequently used algorithms for clustering data with both numeric and categorical variables is the k-prototypes algorithm, an...
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I-RCD: an improved algorithm of repetitive causal discovery from data with latent confounders
Discovering causal relationships from data affected by latent confounders is an important and difficult task. Until recently, approaches based on...
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A Discrete Version of the Half-Logistic Distribution Based on the Mimicking of the Probability Density Function
We introduce a count distribution obtained as a discrete analogue of the continuous half-logistic distribution. It is derived by assigning to each...
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A positive-definiteness-assured block Gibbs sampler for Bayesian graphical models with shrinkage priors
Although the block Gibbs sampler for the Bayesian graphical LASSO proposed by Wang (
2012 ) has been widely applied and extended to various shrinkage...
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Bayesian generalized additive model selection including a fast variational option
We use Bayesian model selection paradigms, such as group least absolute shrinkage and selection operator priors, to facilitate generalized additive...
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An Item-Based Approach: Propertize
OCHRE provides a mechanism to create descriptive elements called Properties which are used to identify database items and ascribe qualities to them....