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 =
21 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 new nonparametric distribution estimator replaces empirical masses with those on estimated expected order statistics, yielding finite-sample properties and asymptotic convergence results controlled by the empirical distribution's L1 error.
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.
citing papers explorer
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Scene-agnostic ALS boresight self-calibration
Scene-agnostic ALS boresight self-calibration via point-to-point correspondences from overlapping strips, with parametric and factor-graph formulations evaluated on operational flights.
-
Statistically and Computationally Optimal Estimation and Inference of Common Subspaces
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.
-
Linked-Tucker Factorized Individualized Regression for Paired Multivariate Categorical Outcomes
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.
-
Optimal multiple testing under family-wise error control: elementary symmetric polynomials and a scalable algorithm
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.
-
Landmarking with Latent Class Mixed Models for Dynamic Prediction of Time-to-event Data with Heterogeneous Biomarker Trajectories
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.
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Learning Dynamical Systems from Multiple Sparse Datasets: A Hierarchical Bayesian Modeling Approach
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.
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A Mathematical Framework and Software Implementation for Uncertainty Visualisation
A framework redefines visualization components for random variable inputs to obey the continuous mapping theorem and is implemented in the ggdibbler ggplot2 extension.
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Amortized Probabilistic Retrieval of Atmospheric CO2 from OCO-2 Spectra Using Deep Learning with Laplace Approximations and Normalizing Flows
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.
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Tests for Independence of High-Dimensional Nonstationary Time Series
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: A flexible Bayesian variable selection method for modeling interactions with mandatory modifying variables
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.
-
Infinite-Dimensional Spherical Kernel ridge Regression
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.
-
Nonparametric Estimation via Expected Order Statistics
A new nonparametric distribution estimator replaces empirical masses with those on estimated expected order statistics, yielding finite-sample properties and asymptotic convergence results controlled by the empirical distribution's L1 error.
-
Pattern-based tests for two-dimensional copulas
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.
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Linking COPD Prevalence with Income Distribution: A Spatial Heterogeneous Compositional Regression via Geographically Weighted Penalized Approach
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.
-
Penalized estimation of GEV parameters for extreme quantile regression
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.
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Stability selection enables robust learning of partial differential equations from limited noisy data
PDE-STRIDE applies stability-based model selection to sparse regression for robust, parameter-free recovery of PDEs from noisy data.
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Bayesian Simultaneous Credible Bands for Polynomial Regression
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.
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Automated Residual Plot Assessment With the R Package autovi and the Shiny Application autovi.web
An R package uses computer vision to predict visual signal strength for automating residual plot assessment in linear model diagnostics.
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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.
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Exploring Statistical Change Point Detection Techniques for Performance Anomaly Detection at Mozilla
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.
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Feature Screening for High-Dimensional Structural Break Predictive Regression
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.