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Finite mixture of hidden Markov models for tensor-variate time series data
The need to model data with higher dimensions, such as a tensor-variate framework where each observation is considered a three-dimensional object,...
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A clustering procedure for three-way RNA sequencing data using data transformations and matrix-variate Gaussian mixture models
RNA sequencing of time-course experiments results in three-way count data where the dimensions are the genes, the time points and the biological...
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Understanding the effect of component proportions on disease control in two-component cultivar cereal mixtures using a pathogen dispersal scaling hypothesis
A field experiment was carried out to determine the importance of component cultivar proportions to spring barley mixture efficacy against...
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A data-driven reversible jump for estimating a finite mixture of regression models
We propose a data-driven reversible jump (DDRJ) method for selecting and estimating a mixture of regression models in a single run, which can also be...
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Compiling the milling load spectrum of a machining center based on an L-moment ratio diagram mixture distribution method
The load spectrum is an important component in the reliability testing of machining centers. The statistical characteristics of a load must be...
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Optimization of mixture models on time series networks encoded by visibility graphs: an analysis of the US electricity market
We propose a fully unsupervised network-based methodology for estimating Gaussian Mixture Models on financial time series by maximum likelihood using...
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Fitting Gaussian mixture models on incomplete data
BackgroundBioinformatics investigators often gain insights by combining information across multiple and disparate data sets. Merging data from...
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A unified Minorization-Maximization approach for estimation of general mixture models
The mixed distribution model is often used to extract information from heterogeneous data and perform modeling analysis. When the density function of...
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A model-based clustering via mixture of hierarchical models with covariate adjustment for detecting differentially expressed genes from paired design
The causes of many complex human diseases are still largely unknown. Genetics plays an important role in uncovering the molecular mechanisms of...
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Elastoplastic Characterization of a Two-Component Epoxy-Based Structural Adhesive
Two-component epoxy-based adhesives are frequently used to bond metals and composite materials in structural lightweight applications. Due to the...
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Finite Mixture of Censored Linear Mixed Models for Irregularly Observed Longitudinal Data
Linear mixed-effects models are commonly used when multiple correlated measurements are made for each unit of interest. Some inherent features of...
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Distributional Validation of Precipitation Data Products with Spatially Varying Mixture Models
The high mountain regions of Asia contain more glacial ice than anywhere on the planet outside of the polar regions. Because of the large population...
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Clustering and estimation of finite mixture models under bivariate ranked set sampling with application to a breast cancer study
In the literature on modeling heterogeneous data via mixture models, it is generally assumed that the samples are drawn from the underlying...
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Leveraging the histidine kinase-phosphatase duality to sculpt two-component signaling
Bacteria must constantly probe their environment for rapid adaptation, a crucial need most frequently served by two-component systems (TCS). As one...
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Penalized Estimation of a Finite Mixture of Linear Regression Models
Finite mixtures of linear regressions are often used in practice in order to classify a set of observations and/or explain an unobserved...
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Mixture modeling with normalizing flows for spherical density estimation
Normalizing flows are objects used for modeling complicated probability density functions, and have attracted considerable interest in recent years....
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Bounded Asymmetric Gaussian Mixture-Based Hidden Markov Models
Hidden Markov models (HMMs) have been widely applied in machine learning to model diversified and heterogeneous time series data. In this chapter,...
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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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Bias in Rank Correlation Under Mixture Models
This study investigates the behavior of two prominent measures of rank correlation—Kendall’s tau and Spearman’s rho—under mixture models,...
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Application of Gaussian mixture models to quantify the upper background threshold for perfluorooctane sulfonate (PFOS) in U.S. surface soil
Studies on the occurrence and environmental distribution of per- and polyfluoroalkyl substances (PFAS) have clearly demonstrated their ubiquity in...
