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Every paper Pith has read. Search by title, abstract, or pith.
1584 papers in stat.ME · page 11
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Nonlinear constraints reconciled in probabilistic forecasts
Nonlinear Probabilistic Forecast Reconciliation
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Bayesian model produces prediction curves for cause-specific risks over time
Principled Estimation and Prediction with Competing Risks: a Bayesian Nonparametric Approach
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Neural network classifier tops power in independence tests
Deep-testing: the case of dependence detection
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Rational time approximation yields convergent VARMA for fractional spatio-temporal models
ARMA approximation of a Non-separable Spatio-Temporal Model with Fractional Smoothnesses in Space and Time
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Calibration checks simplified to pass-fail for safety systems
Recipes for Calibration Checks in Safety-Critical Applications
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Split data to keep target trial inference valid
A simple strategy for valid inference in target trial emulations
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Stratified causal effects plus survival time better characterize death-truncated outcomes
Longitudinal Outcomes Truncated by Death: Causal Estimands and Bayesian Estimators
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New model attributes heatwaves as space-time events
A spatio-temporal statistical framework for heatwave attribution under climate change
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Two-stage design finds cycle-specific doses when toxicity falls over time
TWICEBEE: A Two-stage Intra-patient Curve-free Bayesian Decision-Theoretic Dose Escalation Design
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Binary verdicts fail to separate replicable from non-replicable science
The Difference Between "Replicable" and "Not replicable" is not Itself Scientifically Replicable
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Masked models give better polarity scores than spatial ones
A New Semisupervised Technique for Polarity Analysis using Masked Language Models
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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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Adaptive CIs in two-groups model scale as σ(n^{-1/4} + ε term)
Adaptive Confidence Intervals in Efron's Gaussian Two-Groups Model
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Diffusion models adapt to bounded continuous self-reports
Extending Evidence Accumulation Models to Bounded Continuous Self-report Data
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Diffusion models now fit bounded continuous self-reports and times
Extending Evidence Accumulation Models to Bounded Continuous Self-report Data
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Semiparametric model adds flexible spatial term to linear covariates
A semiparametric autorregresive spatial prediction model
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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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GARCH-X selection rule recovers true covariates consistently
Consistent Variable Selection for GARCH-X Models
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Small clean data purifies noisy labels for classification
Model-agnostic information transfer and fusion for classification with label noise
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Bayesian prior separates invariant predictors from spurious ones
Bayesian Environment Invariant Regression
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Parametric bootstrap tests linear combinations of variance components
Testing linear combinations of multiple variance components
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Adaptive networks let one MCMC sampler handle many similar structural updates
Adaptive Meta-Learning Stochastic Gradient Hamiltonian Monte Carlo Simulation for Bayesian Updating of Structural Dynamic Models
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New procedure locates frontier shifts at near-minimax accuracy
Detecting Changes in Production Frontiers
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New method balances seen and unseen covariates while adapting trial allocations
CBARA: Covariate-Balanced-and-Adjusted Response-Adaptive Randomization
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New randomization balances observed and unobserved covariates
CBARA: Covariate-Balanced-and-Adjusted Response-Adaptive Randomization
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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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PHIE turns HB weights into cross-tab credible intervals
Post-Hoc Inference of Cross-Classified Statistics from Hierarchical Bayes Survey Weights
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Wilcoxon test inflates false positives in IR comparisons
Stop Using the Wilcoxon Test: Myth, Misconception and Misuse in IR Research
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Bayesian method fuses and selects variables together
Variable Fusion and Selection via a Spike-and-Slab Approach with Nonlocal Priors
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Bayesian method pools non-concurrent data in platform SMART trials
Bayesian integration G-formula for platform SMART designs allowing for adding new treatments
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PCA estimator lowers error for population mean under multicollinearity
Principal Component Based Estimation of Finite Population Mean under Multicollinearity
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Context kernel separates neighborhood from fit in local regression
Generalized Local Polynomial Regression with Decomposed Context-Aware Kernels
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Regularized estimator removes truncation choice in functional autoregression
Functional Autoregression Without Truncation: A Continuous-Regularization Approach
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Tail allocation recovers shortest single-interval conformal predictors
Tail allocation for conformal prediction intervals
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Elite SVMs steer slacks toward trusted reference benchmarks
Elite-Driven Support Vector Machines for Classification
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Ensemble smoother infers causality from future observations
A Continuous-Time Ensemble Kalman-Bucy Smoother for Causal Inference and Model Discovery
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Autoregressive model yields optimal online rates for multilayer community recovery
Online Learning for Autoregressive Multilayer Stochastic Block Models under Stationarity and Non-Stationarity
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Latent model adapts FSC to maxima-nominated samples
Fractionally Supervised Classification with Maxima Nominated Samples
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Conformal sets predict future conflict sequences with coverage
Conflict Forecasting via Conformal Prediction for Markov Processes
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Latent factors uncover directed viral links from north to southeast
Density-valued VAR Models with Latent Factors
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Episode-specific eligibility preserves randomization in re-enrolling trials
Evolving Longitudinal Patient Histories and Re-enrollment in Master Protocol Trials
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Rectified Fisher-Bingham model fits compositional data with exact zeros
Rectified Fisher-Bingham Model for Compositional Data with Zeros
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Graph-fused IV regression recovers causal nodes in networks
Network-aware IV Regression for Causal Node Discovery and Estimation
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Transformer distills causal signals from non-stationary time series
TTCD:Transformer Integrated Temporal Causal Discovery from Non-Stationary Time Series Data
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Mixtures of Gaussians on hyperbolic space admit EM algorithms
Finite Mixture Modeling with Riemannian Gaussian Distributions on Hyperbolic Space
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IV inference valid without exact structural equations
Instrumental Variable Analysis Without Structural Equations
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Adaptive sampling beats uniform for precision at modest sizes
Benefits and Costs of Adaptive Sampling
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Model recovers larger pure lifts from overlapping marketing journeys
Hierarchical Causal Uplift Modeling in Overlapping Customer Journeys
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Approximate Bayesian sampler valid for large privatized data
Large-Sample Bayesian Approximations for Privatized Data
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Empirical Bayes shrinks fixed and random effects together in mixed models
Combined shrinkage of fixed and random effects in linear mixed models using empirical Bayes