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Every paper Pith has read. Search by title, abstract, or pith.
1584 papers in stat.ME · page 9
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Causal roadmap unifies external control trial methods
Externally Controlled Trials: A Review of Design and Borrowing Through a Causal Lens
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Right interventions separate latent contexts from causal mechanisms
Partially Observed Structural Causal Models
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Kernel discrepancy defines intrinsic ESS for manifold MCMC
Intrinsic effective sample size for manifold-valued Markov chain Monte Carlo via kernel discrepancy
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Quantile scales calibrate spatial-sign tests for dependent elliptical data
High-Dimensional Two-Sample Test for Elliptical Symmetry Distribution
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Simplified test-release adds less noise yet keeps optimal rates
Efficient Proposal-Test-Release for Minimax Optimal Estimation
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Entropic transport loss avoids local optima in clustering
On Model-Based Clustering With Entropic Optimal Transport
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Low-rank smoothing recovers peer effects from noisy networks
Estimating peer effects in noisy, low-rank networks via network smoothing
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Log-rank power turns non-monotonic with longer follow-up when both groups have survivors
A comparative study of two-sample hypothesis tests in the presence of long-term survivors
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Directed trees recovered from zero-inflated compositional nodes
Structure Learning for Directed Trees with Zero-Inflated Compositional Nodes
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Method generates non-Gaussian spatio-temporal fields scalably
Scalable generative modeling of non-Gaussian spatio-temporal fields via autoregressive Gaussian processes
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Adaptive perturbation removes bias in ML subgroup tests
In-Sample Evaluation of Subgroups Identified by Generic Machine Learning
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Framework lets users set confidence in Bayesian uncertainty sources
Bayesian inference with sources of uncertainty: from confidence modelling to sparse estimation
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Greenium gaps imply time to low-carbon transition
Market-implied time to transition to a low-carbon economy: a stochastic modelling and inference framework
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Vine copulas spot higher-order time-varying links in neural data
Dynamic Vine Copulas: Detecting and Quantifying Time-Varying Higher-Order Interactions
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Sign-flipping individual score contributions yields asymptotically valid confidence…
Robust confidence intervals for generalized linear models
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Tweedie kernels unify estimation of zero-inflated densities
Tweedie-based nonparametric estimation for semicontinuous mixed densities
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CV-TMLE adaptive weights lift power in rare-disease endpoint trials
A CV-TMLE global test approach to improve power in rare disease clinical studies with multiple-component endpoints
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Optimized sampling and surrogate cut leading error in value estimates
First-Order Efficiency for Probabilistic Value Estimation via A Statistical Viewpoint
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Bayesian reflex unifies online AI learning like autonomic nervous system
The Bayesian Reflex: Online Learning as the Autonomic Nervous System of Modern and Future AI
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Fréchet algorithm gives consistent random effects for metric-space objects
Random-Effects Algorithm for Random Objects in Metric Spaces
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PMM estimators outperform OLS for skewed or heavy-tailed errors
EstemPMM: Polynomial Maximization Method for Non-Gaussian Regression and Time Series in R
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Ancestor Hawkes process ties event impact to cluster origin
The Ancestor Hawkes Process with an Application to Group Chat Data
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This paper introduces Bayesian methods with new priors on correlation matrices and a…
Prior elicitation for Bayesian estimation of single-subject connectivity networks
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Few linear contrasts recover exact GP conditionals
Fast and accurate conditioning for large-scale and online Gaussian process prediction problems
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Conditional bootstrap keeps data structure intact for mixed models
Conditional bootstrap for non-linear mixed effects models
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Optimal test-and-roll sample size is one third of the population
Prior-Free Sample Size Design for Test-and-Roll Experiments
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Jittering adapts prediction errors to categorical models
Development and performance of npd for the evaluation of models with ordinal data
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Imputing censored events extends npde to joint models
Evaluation of the npde performance for the evaluation of joint model with longitudinal and TTE data: an application in metastatic hormono-resistant prostate cancer
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Convex hull projection denoises manifold data with finite-sample bounds
Denoising data using convex relaxations
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Sequential binary data support only practical boundary probabilities
Practical Boundary Degeneracy and Reverse-Martingale Limits in Sequential Binary Models
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Uniform representation yields simultaneous confidence regions for time series
Simultaneous Inference for Nonlinear Time Series, a Sieve M-regression Approach
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Doubly stable selection guards features against design noise
2D Stability Selection: Design Jittering for Doubly Stable Feature Selection
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Surrogates yield efficient estimator for transported quantile effects
Efficient Transported Distributional and Quantile Treatment Effects with Surrogate-Assisted Missing Primary Outcomes
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Kernel weights extend fast recursion to density regression
Fast Semiparametric Density Regression with Weight-localized Predictive Recursion
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The paper develops a new variance estimator for coefficients in relative sparsity models…
An adaptive variance estimator for relative sparsity
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Neural networks recover conditional distributions from energy distance to noise
Neural Generative Distributional Regression
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Mixtures of experts based on shifted asymmetric Laplace experts handle asymmetric and…
Shifted asymmetric Laplace mixtures of experts
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Extreme value theory enables extrapolation beyond training data
Extrapolation in Statistical Learning with Extreme Value Theory
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Estimator recovers latent clusters and matches oracle rates
Adaptive Estimation and Inference in Semi-parametric Heterogeneous Clustered Multitask Learning via Neyman Orthogonality
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Sharp bounds quantify how necessary a treatment was for a continuous outcome
Probabilities of Causation for Continuous Outcomes: Bounds and Identification
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Stable blankets extend to hidden variables and cycles
Stable Blanket with Hidden Variables and Cycles
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Semi-supervised kernel test uses covariates for higher power
A Semi-Supervised Kernel Two-Sample Test
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Matrix-variate Heckman model corrects selection bias in matrix data
Multiple Heckman Selection Model
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Adaptive CATE model stabilizes policy-value estimates under weak overlap
Adaptive Targeted Maximum Likelihood Estimation of the Mean Potential Outcome under a Treatment Rule
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Learned per-edge trust lets causal discovery use imperfect priors safely
PRCD-MAP: Learning How Much to Trust Imperfect Priors in Causal Discovery
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Exact analytical expressions for the Gauss-Cauchy convolution density enable stable…
Exact Likelihood Inference and Robust Filtering for Gauss-Cauchy Convolution Models
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Maxima-nominated sets lower variance in spatial exceedance estimates
Threshold Exceedance Estimation in Spatially Correlated Areal Data Using Maxima-Nominated Sampling
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Rule-based selection picks wrong models in time series forecasts
Why Model Selection Fails in Time Series Forecasting: An Empirical Study of Instability Across Data Regimes