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377 papers in stat.CO · page 7
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Bayesian stacking matches full analysis on massive spatial data
Bayesian Transfer Learning for Artificially Intelligent Geospatial Systems: A Predictive Stacking Approach
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Algorithm recovers arbitrary Gaussians from unknown truncated samples
Efficient Statistics With Unknown Truncation, Polynomial Time Algorithms, Beyond Gaussians
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Statistical Taylor expansion yields path-independent results
Statistical Taylor Expansion: A New and Path-Independent Method for Uncertainty Analysis
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Deep splitting scheme converges for nonlinear filtering density
A convergent scheme for the Bayesian filtering problem based on the Fokker--Planck equation and deep splitting
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Neural twisting lowers variance for continuous-time particle filters
Guidance for twisted particle filter: a continuous-time perspective
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Tuned starts stabilize student groups in block model for language tests
Examining the robustness of a model selection procedure in the binary latent block model through a language placement test data set
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Standard discriminant analysis fails under outliers or label errors
Robust discriminant analysis
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Warped GP fuses skewed time series from uneven sources
Warped multifidelity Gaussian processes for data fusion of skewed environmental data
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Bayesian criteria extend optimal design to nonlinear models
Optimal experimental design: Formulations and computations
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Joint diffusion couples parameters and latents to target marginal MLE
Kinetic Interacting Particle Langevin Monte Carlo
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Online LASSO updates fit distributional models incrementally
Online Distributional Regression
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Random forests let ABC infer joint posteriors without summary statistics
Approximate Bayesian Computation sequential Monte Carlo via random forests
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Splicing iteration yields linear convergence for sparse optimization
Sparsity-Constraint Optimization via Splicing Iteration
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Unbounded DP distributions converge to bounded DP as n grows
Statistical Inference for Privatized Data with Unknown Sample Size
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European firms cluster by sustainability across borders
Multidimensional spatiotemporal clustering -- An application to environmental sustainability scores in Europe
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Lifted samplers cap asymptotic variance at twice base level
Theoretical guarantees for lifted samplers
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Fiducial method works for Cox models when MLE fails
Semiparametric fiducial inference for Cox models
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R package selects change-points for trial event forecasts
PWEXP: An R Package Using Piecewise Exponential Model for Study Design and Event/Timeline Prediction
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Hierarchical algorithm matches GLS estimator for census data
Least Squares Estimation For Hierarchical Data
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Extended negative binomial recovers Poisson parameters from count data
From Poisson Observations to Fitted Negative Binomial Distribution
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Nested Monte Carlo computes EIG on predictions for nonlinear design
Goal-Oriented Bayesian Optimal Experimental Design for Nonlinear Models using Markov Chain Monte Carlo
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Composite likelihood restores MLE efficiency for Poisson log-normal counts
Composite likelihood inference for the Poisson log-normal model
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Mixed-effects models structure regularised methods for big geospatial data
A review of regularised estimation methods and cross-validation in spatiotemporal statistics
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Particle filters forget initial state after O(log N) steps
On the Forgetting of Particle Filters
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Variational RL optimizes experiment sequences with info-gain lower bounds
Variational Sequential Optimal Experimental Design using Reinforcement Learning
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Monte Carlo study compares imputation for ARFIMA d
Estimation of Long-Range Dependent Models with Missing Data: to Impute or not to Impute?
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Sampling rates can match or exceed optimization rates
Convergence Rates for Non-Log-Concave Sampling and Log-Partition Estimation
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P-spline rates in SIR models recover COVID transmission patterns
Dynamic SIR/SEIR-like models comprising a time-dependent transmission rate: Hamiltonian Monte Carlo approach with applications to COVID-19
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One turbine's data yields load estimates for whole offshore wind farm
Farm-wide virtual load monitoring for offshore wind structures via Bayesian neural networks
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FCS converges with normal inverse-gamma priors in linear models
Joint distribution properties of Fully Conditional Specification under the normal linear model with normal inverse-gamma priors
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Posterior predictive checks validate imputation model congeniality
Graphical and numerical diagnostic tools to assess multiple imputation models by posterior predictive checking
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Covariance subspaces detect vector field anomalies without distribution assumptions
Distribution-Free Stochastic Analysis and Robust Multilevel Vector Field Anomaly Detection
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Adaptive depth functions detect anomalies by spotting antimodes
Antimodes and Graphical Anomaly Exploration via Adaptive Depth Quantile Functions
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ODE solvers compared for holonomic gradient method
Comparison of Numerical Solvers for Differential Equations for Holonomic Gradient Method in Statistics
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Rank statistic detects multiple location and scale shifts
Nonparametric Detection of Multiple Location-Scale Change Points via Wild Binary Segmentation
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Fat truncation enables efficient learning of Boolean products
Efficient Parameter Estimation of Truncated Boolean Product Distributions
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New noise estimator lifts change point detection in low-signal cases
Seeded intervals and noise level estimation in change point detection: A discussion of Fryzlewicz (2020)
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Core array concepts drive scientific Python computing
Array Programming with NumPy
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Low discrepancy preserved after transformation only if kernels compatible
Is a Transformed Low Discrepancy Design Also Low Discrepancy?
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Pattern search beats step-down for multi-category biomarker weights
Estimating the Optimal Linear Combination of Biomarkers using Spherically Constrained Optimization
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Rob-ULA samples from a distribution within ε_acc plus Õ(ε) of the clean posterior after…
Bayesian Robustness: A Nonasymptotic Viewpoint
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Bayesian PixelCNN uncertainty lifts low-label MRI classification
Bayesian Volumetric Autoregressive generative models for better semisupervised learning
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Laplace fields improve spatial predictions for spiky data
Bayesian Analysis of Spatial Generalized Linear Mixed Models with Laplace Random Fields
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R package BSL unifies methods for intractable likelihood models
BSL: An R Package for Efficient Parameter Estimation for Simulation-Based Models via Bayesian Synthetic Likelihood
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INLA package adds efficient tools for complex Bayesian models
New frontiers in Bayesian modeling using the INLA package in R
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Initializer helps skew-t MLE avoid poor local maxima
Some computational aspects of maximum likelihood estimation of the skew-$t$ distribution
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Conjugate updates enable exact Bayesian interpolation for millions of spatial points
Conjugate Nearest Neighbor Gaussian Process Models for Efficient Statistical Interpolation of Large Spatial Data
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Grid search on prediction error estimates lags in functional models
On estimation of the effect lag of predictors and prediction in functional linear model
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Lambert-W functions speed up Hawkes process simulation
On the simulation of the Hawkes process via Lambert-W functions
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Data scientists can audit each model stage to curb hidden bias
Conscientious Classification: A Data Scientist's Guide to Discrimination-Aware Classification