COTHROM applies a Potts Hamiltonian representation of constitutional mandates, MCMC/simulated annealing optimization, and Pareto/MCDA analysis to improve Irish constituency boundaries over existing legal ones in County Cork for proportionality and compactness across weightings.
(1994): Diagnostic plots for one-dimensional data
10 Pith papers cite this work. Polarity classification is still indexing.
10
Pith papers citing it
representative citing papers
A Bayesian model for multivariate conditional density estimation combines Gaussian copulas with Tucker tensor factorization and random partition models to flexibly capture covariate effects and perform coordinate-wise predictor selection.
MSFAST extends the FAST FPCA method to multivariate sparse data via Bayesian modeling with orthonormal splines, standardization, Procrustes alignment, and efficient computation, yielding valid inferences especially in low signal-to-noise settings.
Develops HDA and VDA methods plus adapted crossing-point asymptotics for locating and testing treatment-effect discontinuities in distributional treatment effects, illustrated on synthetic data and Mexico's PROGRESA program.
Composite-move Tabu search expands neighborhoods in redistricting optimization by moving minimal connected sets of units identified via graph articulation points, yielding better solutions and efficiency than standard Tabu search.
pandemonium is an R package that performs clustering in one space and links the resulting groups to visualizations in both predictor and response spaces via dimension reduction and tours.
Subgradient Langevin dynamics and certain discretizations are shown to be ergodic for strongly convex non-smooth potentials, with the discrete versions also satisfying the law of large numbers.
Introduces adaptive depth quantile functions (aDQF) for anomaly detection motivated by antimodes, with graphical visualization benefits in Euclidean and non-Euclidean data.
Bayesian meta-learner predicts individualized Alzheimer's disease progression distributions from MRI and trajectories, competitive on ADNI data and less overconfident for long-term scores than deterministic versions.
citing papers explorer
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Constituency Optimisation Through Hamiltonian Representation Of Mandates (COTHROM): Algorithmic Redistricting of Irish Election Boundaries
COTHROM applies a Potts Hamiltonian representation of constitutional mandates, MCMC/simulated annealing optimization, and Pareto/MCDA analysis to improve Irish constituency boundaries over existing legal ones in County Cork for proportionality and compactness across weightings.
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Bayesian Semiparametric Multivariate Density Regression with Coordinate-Wise Predictor Selection
A Bayesian model for multivariate conditional density estimation combines Gaussian copulas with Tucker tensor factorization and random partition models to flexibly capture covariate effects and perform coordinate-wise predictor selection.
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Bayesian Multivariate Sparse Functional Principal Components Analysis
MSFAST extends the FAST FPCA method to multivariate sparse data via Bayesian modeling with orthonormal splines, standardization, Procrustes alignment, and efficient computation, yielding valid inferences especially in low signal-to-noise settings.
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A Toolkit for the Study of Treatment-Effect Discontinuities
Develops HDA and VDA methods plus adapted crossing-point asymptotics for locating and testing treatment-effect discontinuities in distributional treatment effects, illustrated on synthetic data and Mexico's PROGRESA program.
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Fast and Effective Redistricting Optimization via Composite-Move Tabu Search
Composite-move Tabu search expands neighborhoods in redistricting optimization by moving minimal connected sets of units identified via graph articulation points, yielding better solutions and efficiency than standard Tabu search.
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`pandemonium`: High Dimensional Analysis in Linked Spaces
pandemonium is an R package that performs clustering in one space and links the resulting groups to visualizations in both predictor and response spaces via dimension reduction and tours.
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Ergodicity of Langevin Dynamics and its Discretizations for Non-smooth Potentials
Subgradient Langevin dynamics and certain discretizations are shown to be ergodic for strongly convex non-smooth potentials, with the discrete versions also satisfying the law of large numbers.
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Antimodes and Graphical Anomaly Exploration via Adaptive Depth Quantile Functions
Introduces adaptive depth quantile functions (aDQF) for anomaly detection motivated by antimodes, with graphical visualization benefits in Euclidean and non-Euclidean data.
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Bayesian meta-learning for modeling Alzheimer's disease progression
Bayesian meta-learner predicts individualized Alzheimer's disease progression distributions from MRI and trajectories, competitive on ADNI data and less overconfident for long-term scores than deterministic versions.
- Detection of Anomalous Network Nodes via Hierarchical Prediction and Extreme Value Theory