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Every paper Pith has read. Search by title, abstract, or pith.
377 papers in stat.CO · page 3
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R packages unify forecast reconciliation across three frameworks
FoReco and FoRecoML: A Unified Toolbox for Forecast Reconciliation in R
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Bridge sampling approximates martingale posteriors with O(Δ) bias
Martingale Posteriors for Discretely Observed Diffusions
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The paper extends the Minimum Covariance Determinant estimator to interval-valued data…
Minimum Covariance Determinant Estimator and Outlier Detection for Interval-valued Data
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The paper applies Variational Expectation Maximization to fit nonlinear mixed effects…
Fitting Large Nonlinear Mixed Effects Models Using Variational Expectation Maximization
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Wasserstein post-processing removes subsampling bias in GLMM sampling
Safe, Scalable, and Accurate Bayes Posterior Sampling for Large-Data Generalized Linear Mixed Models
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Skew-Laplace cuts Dirichlet mixture posterior error by ~30% vs Laplace
Laplace and skew-Laplace approximations for Dirichlet process mixture posterior density
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First-order bias bounds for stochastic gradient Langevin
Theoretical guarantees for stochastic gradient sampling methods via Gaussian convolution inequalities
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Quantum circuit propagates Fokker-Planck dynamics unitarily
Quantum Prediction of Transport Dynamics in Discretized State Spaces
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Kac's walk on rotations mixes in n² log n steps
Kac's walk on rotation matrices mixes in $n^2 \log n$ steps
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GPU single pass computes statistics for 10 billion rows
Building a GPU-Accelerated Multivariate Statistics Platform
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GPU workflow computes stats for 10 billion rows in one pass
Building a GPU-Accelerated Multivariate Statistics Platform
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MAEO ensemble matches or beats leading algorithms on benchmarks
MAEO: Multiobjective Animorphic Ensemble Optimization for Scalable Large-scale Engineering Applications
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Phase signals in density of states pinpoint optimal tempered posteriors
Using Statistical Mechanics to Improve Real-World Bayesian Inference: A New Method Combining Tempered Posteriors and Wang-Landau Sampling
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R package ragR matches Python RAGAS for RAG evaluation
ragR: Retrieval-Augmented Generation and RAG Assessment in R
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Anchoring at posterior mean speeds variational inference for sequential models with random
Anchored Variational Inference for Personalized Sequential Latent-State Models
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Online Newton with accelerated sketching converges globally
Inference of Online Newton Methods with Nesterov's Accelerated Sketching
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Running score averages enable low-variance non-Markovian sampling
Score-Repellent Monte Carlo: Toward Efficient Non-Markovian Sampler with Constant Memory in General State Spaces
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Super Learner gains finite-sample coverage via conformal prediction
Conformalized Super Learner
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Dual-thresholding boosts short CNA detection accuracy in noisy data
Tail-Greedy Unbalanced Haar Wavelet Segmentation for Copy Number Alteration Data
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Givens rotations turn Stiefel optimization into unconstrained search
BOOOM: Loss-Function-Agnostic Black-Box Optimization over Orthonormal Manifolds for Machine Learning and Statistical Inference
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Projection yields exact decomposition of NMA into study contributions
Contrast-Space Projection for Network Meta-Analysis: An Exact and Invariant Study-Based Decomposition of Direct and Indirect Contributions
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IPM utilities stabilize BOED against model and prior errors
Beyond Expected Information Gain: Stable Bayesian Optimal Experimental Design with Integral Probability Metrics and Plug-and-Play Extensions
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One model fit recovers full Bayes factor sensitivity curve
Efficient Bayes Factor Sensitivity Analysis via Posterior Density Ratios
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Constrained particle filters enforce compact state support
On a class of constrained particle filters for continuous-discrete state space models
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Symmetries let location-scale families recover exact means under forward KL
Even More Guarantees for Variational Inference in the Presence of Symmetries
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DC algorithm reaches global modes in Bayesian variable selection
Revisiting Bayesian Variable Selection via Optimization
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R package adds random effects to Bayesian profile regression
ProfileGLMM: a R Package Extending Bayesian Profile Regression using Generalised Linear Mixed Models
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Logarithmic norms bound finite-time instability in stochastic flight dynamics
Bounding Transient Instability in Sensor Data Injected Nonlinear Stochastic Flight Dynamics
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Geometric tempering yields exponential convergence except in Fisher-Rao flows
Properties and limitations of geometric tempering for gradient flow dynamics
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Annealed Langevin Monte Carlo yields low-variance flow ODE estimates
Annealed Langevin Monte Carlo for Flow ODE Sampling
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Compression reduces NPMLE cost to logarithmic in sample size
Fast computation and theoretical guarantees for the NPMLE in exponential family mixtures
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NN-graph cross-edges test high-dim MGGD fit
A Nonparametric Goodness-of-Fit Test for High-Dimensional Generalized Gaussian Distributions via Nearest-Neighbor Graphs
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Random rewards extend phase-type models to latent severity
Random Reward Phase-Type Distributions with Applications in Latent Severity Modeling
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Hybrid model forecasts steam generator clogging life from physics and data
Digital twin-based hybrid framework for steam generator clogging prognostics
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New mixture model makes large-scale spatial extreme analysis feasible
Spatial Extremes at Scale: A Case Study of Surface Skin Temperature and Heat Risk in the United States
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Simulations settle conflicting MANOVA error-rate reports
A simulation study to resolve conflicting evidence on the error rates from MANOVA group tests
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Gamma expansion yields first-passage densities for logistic harvesting
Gamma-Based Expansion for the First-Passage Time Distribution of Stochastic Logistic Models with Harvesting
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INLA hierarchy models non-linear BMI effects on mortality
Efficient Bayesian inference for non-linear association structures in joint models: A hierarchical approach via INLA
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Landing step makes constrained diffusion practical on nonconvex sets
Efficient Diffusion Models under Nonconvex Equality and Inequality constraints via Landing
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Simple formulas approximate quantiles for normal
Simple approximations of some statistical functions
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Custom estimators make subtractive mixtures usable for inference
How to Approximate Inference with Subtractive Mixture Models
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Markov embedding shrinks state space of ranked trees for exact means
Markov embedding of ranked unlabelled evolutionary trees and its applications
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Block aggregation yields fine-scale inferences from coarse data
Spatially continuous modelling of aggregated outcome data
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The paper proposes Theta-regularized Kriging
Theta-regularized Kriging: Modelling and Algorithms
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Decomposed subproblems let SMC scale to large clustering tasks
Scalable Model-Based Clustering with Sequential Monte Carlo
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Bayesian method links PCoA axes to driving taxa
Bayesian sparse principal coordinates analysis with delta-tolerant linear approximation for microbiome data
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Bulk measurements recover microstructure statistics
Distributional Inverse Homogenization
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Bulk measurements recover microstructure statistics
Distributional Inverse Homogenization
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Adaptive sparse group lasso delivers dual sparsity for quantile regression
Adaptive Sparse Group Lasso Penalized Quantile Regression via Dual ADMM
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Probabilistic algorithm recovers p-adic linear models from digit noise
$p$-adic Linear Regression for Random Sampling with Digitwise Noise