Scene-agnostic ALS boresight self-calibration via point-to-point correspondences from overlapping strips, with parametric and factor-graph formulations evaluated on operational flights.
Journal of the American Statistical Association , volume =
20 Pith papers cite this work. Polarity classification is still indexing.
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Establishes statistical and computational optimality thresholds for common subspace estimation and inference under varying SNR regimes, including an impossibility result for adaptive confidence intervals below strong inference SNR.
A linked Tucker tensor factorization enables a joint individualized hurdle-ordinal regression model that uncovers spatially heterogeneous effects of fluoride and diet on paired caries and fluorosis outcomes.
For exchangeable hypotheses the optimal FWER-controlling multiple-testing procedure is computed via elementary symmetric polynomials on likelihood ratios plus a monotonicity theorem that enables an efficient bisection coordinate-descent algorithm.
A landmarking approach using latent class mixed models for dynamic prediction of time-to-event data that accounts for latent heterogeneity in longitudinal biomarker trajectories.
A hierarchical Bayesian framework pools information across sparse dynamical system datasets via a shared population distribution to improve parameter inference and prediction over unpooled approaches.
A framework redefines visualization components for random variable inputs to obey the continuous mapping theorem and is implemented in the ggdibbler ggplot2 extension.
A deep learning method amortizes probabilistic XCO2 retrieval from OCO-2 spectra via Laplace approximations and normalizing flows, trained on simulations with model errors to achieve faster inference and better-calibrated uncertainties than operational solvers.
A new test statistic and bootstrap for independence testing of high-dimensional nonstationary time series that avoids whitening by removing temporal dependence bias under the null.
PliableBVS is a new Bayesian hierarchical spike-and-slab model for simultaneous selection of high-dimensional main effects and interactions under an asymmetric weak hierarchical constraint, shown to outperform pliable lasso in simulations.
An intrinsic spherical kernel ridge regression framework is introduced for non-linear responses on spheres, reducing infinite-dimensional estimation to finite via the representer theorem with convergence rates shown.
A functional central limit theorem for pattern frequencies in 2D samples enables nonparametric goodness-of-fit, two-sample, and symmetry tests for copulas, with bootstrap critical values and parametric examples.
A new geographically weighted penalized compositional regression model with pairwise fusion penalty is proposed to handle spatial heterogeneity and compositional covariates, demonstrated on U.S. income and COPD data.
A penalized likelihood estimator for GEV parameters, weighted by generalized random forest weights, is introduced for extreme quantile regression to improve tail extrapolation and handle many predictors.
PDE-STRIDE applies stability-based model selection to sparse regression for robust, parameter-free recovery of PDEs from noisy data.
A unified Bayesian framework constructs simultaneous credible bands for univariate polynomial regression that achieve exact posterior coverage and asymptotic frequentist coverage under mild regularity conditions.
An R package uses computer vision to predict visual signal strength for automating residual plot assessment in linear model diagnostics.
A note that flags an oversight in RLT convergence proofs for polynomial optimization and recovers correctness via one extra natural assumption.
Ensemble voting strategies for change point detection improve F1-score by 11% over Mozilla's T-test method on a new ground-truth dataset of 174 performance time series annotated by practitioners.
Develops SICS and RCRS screening methods for consistent selection of sparse active predictors and change points in high-dimensional structural break predictive regressions that may involve stationary or cointegrated series.
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A note on the convergence guarantees of RLT-based algorithms for polynomial optimization
A note that flags an oversight in RLT convergence proofs for polynomial optimization and recovers correctness via one extra natural assumption.