On a class of repulsive mixture models

Quinlan, José J.; Quintana, Fernando A.; Page, Garritt L.

doi:10.1007/s11749-020-00726-y

Cited by 5 publications

(1 citation statement)

References 25 publications

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“…In particular, it is possible to define such a process by specifying a density with respect to a Poisson point process. For instance, Quinlan et al (2020); Xie & Xu (2019); Beraha et al (2022) assume a pairwise interaction process whose density, with respect to a suitably defined Poisson process, is…”

Section: Bayesian Clustering Via Latent Mixturesmentioning

confidence: 99%

Bayesian clustering of high-dimensional data via latent repulsive mixtures

Ghilotti¹,

Beraha²,

Guglielmi³

2023

Preprint

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Model-based clustering of moderate or large dimensional data is notoriously difficult. We propose a model for simultaneous dimensionality reduction and clustering by assuming a mixture model for a set of latent scores, which are then linked to the observations via a Gaussian latent factor model. This approach was recently investigated by Chandra et al. (2020). The authors use a factor-analytic representation and assume a mixture model for the latent factors. However, performance can deteriorate in the presence of model misspecification. Assuming a repulsive point process prior for the component-specific means of the mixture for the latent scores is shown to yield a more robust model that outperforms the standard mixture model for the latent factors in several simulated scenarios. To favor well-separated clusters of data, the repulsive point process must be anisotropic, and its density should be tractable for efficient posterior inference. We address these issues by proposing a general construction for anisotropic determinantal point processes.

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Section: Bayesian Clustering Via Latent Mixturesmentioning

confidence: 99%

Bayesian clustering of high-dimensional data via latent repulsive mixtures

Ghilotti¹,

Beraha²,

Guglielmi³

2023

Preprint

View full text Add to dashboard Cite

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A hierarchical constrained density regression model for predicting cluster‐level dose‐response

Pennell,

Wheeler,

Auerbach

2024

Environmetrics

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With the advent of new alternative methods for rapid toxicity screening of chemicals comes the need for new statistical methodologies which appropriately synthesize the large amount of data collected. For example, transcriptomic assays can be used to assess the impact of a chemical on thousands of genes, but current approaches to analyzing the data treat each gene separately and do not allow sharing of information among genes within pathways. Furthermore, the methods employed are fully parametric and do not account for changes in distribution shape that may occur at high exposure levels. To address the limitations of these methods, we propose Constrained Logistic Density Regression (COLDER) to model expression data from different genes simultaneously. Under COLDER, the dose‐response function for each gene is assigned a prior via a discrete logistic stick‐breaking process (LSBP) whose weights depend on gene‐level characteristics (e.g., pathway membership) and atoms consist of different dose‐response functions subject to a shape constraint that ensures biological plausibility. The posterior distribution for the benchmark dose among genes within the same pathways can be estimated directly from the model, which is another advantage over current methods. The ability of COLDER to predict gene‐level dose‐response is evaluated in a simulation study and the method is illustrated with data from a National Toxicology Program study of Aflatoxin B1.

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