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Multinomial Restricted Unfolding
For supervised classification we propose to use restricted multidimensional unfolding in a multinomial logistic framework. Where previous research...
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Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components
We propose a semi-parametric clustering model assuming conditional independence given the component. One advantage is that this model can handle...
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Animal Density Estimation for Large Unmarked Populations Using a Spatially Explicit Model
Obtaining abundance and density estimates is a particularly important aspect within wildlife conservation and management. To monitor wildlife...
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Imputation Strategies for Clustering Mixed-Type Data with Missing Values
Incomplete data sets with different data types are difficult to handle, but regularly to be found in practical clustering tasks. Therefore in this...
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Efficient variable selection for high-dimensional multiplicative models: a novel LPRE-based approach
This paper explores a novel high-dimensional sparse multiplicative model, which deal with data with positive responses, particularly in economical...
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Hypothesis testing in Cox models when continuous covariates are dichotomized: bias analysis and bootstrap-based test
Hypothesis testing for the regression coefficient associated with a dichotomized continuous covariate in a Cox proportional hazards model has been...
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Variable selection in function-on-scalar single-index model via the alternating direction method of multipliers
We develop a new method for variable selection in a function-on-scalar single-index model. The proposed method goes beyond existing additive...
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Robust variable selection for additive coefficient models
Additive coefficient models generalize linear regression models by assuming that the relationship between the response and some covariates is linear,...
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Distributed quantile regression for longitudinal big data
Longitudinal data, measurements taken from the same subjects over time, appear routinely in many scientific fields, such as biomedical science,...
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Interactive graphics for visually diagnosing forest classifiers in R
This article describes structuring data and constructing plots to explore forest classification models interactively. A forest classifier is an...
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Causal Mediation Tree Model for Feature Identification on High-Dimensional Mediators
High-dimensional mediation analysis plays an important role in recent biomedical research as a large number of mediators, such as microbiome, could...
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Missing Values and Directional Outlier Detection in Model-Based Clustering
Model-based clustering tackles the task of uncovering heterogeneity in a data set to extract valuable insights. Given the common presence of outliers...
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Is EM really necessary here? Examples where it seems simpler not to use EM
If one is to judge by counts of citations of the fundamental paper (Dempster in JRSSB 39: 1–38, 1977), EM algorithms are a runaway success. But it is...
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A boosting first-hitting-time model for survival analysis in high-dimensional settings
In this paper we propose a boosting algorithm to extend the applicability of a first hitting time model to high-dimensional frameworks. Based on an...
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3D Point Cloud Semantic Segmentation Through Functional Data Analysis
Here, we propose a method for the semantic segmentation of 3D point clouds based on functional data analysis. For each point of a training set, a...
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Computing T-optimal designs via nested semi-infinite programming and twofold adaptive discretization
Modelling real processes often results in several suitable models. In order to be able to distinguish, or discriminate, which model best represents a...
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Single-Index Mixed-Effects Model for Asymmetric Bivariate Clustered Data
Studies/trials assessing status and progression of periodontal disease (PD) usually focus on quantifying the relationship between the clustered...
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Design of experiments and machine learning with application to industrial experiments
In the context of product innovation, there is an emerging trend to use Machine Learning (ML) models with the support of Design Of Experiments (DOE)....
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Model-based clustering via mixtures of unrestricted skew normal factor analyzers with complete and incomplete data
Mixtures of factor analyzers (MFA) based on the restricted skew normal distribution (rMSN) have emerged as a flexible tool to handle asymmetrical...
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Sequential support points
By minimizing the energy distance, the support points (SP) method can efficiently compact big training sample into a representative point set with...
