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.
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Survival Regression with Accelerated Failure Time Model in XGBoost
16 Pith papers cite this work. Polarity classification is still indexing.
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StarTime uses a hierarchical temporal tree to enable sparse or aggregated coefficient selection in high-order autoregressions and mixed-frequency regressions, with new error bounds and simulation improvements over benchmarks.
MIBoost extends gradient boosting to multiple imputation by defining a single loss function that produces one set of selected variables across all imputed datasets.
A framework redefines visualization components for random variable inputs to obey the continuous mapping theorem and is implemented in the ggdibbler ggplot2 extension.
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 Dirichlet process mixture model for marked Poisson point processes with squared-link intensities and Laplace variational inference jointly infers clusters, cluster count, and continuous mark-specific intensity surfaces.
A Bayesian model for multi-feature contact matrices that uses tensor structures and contingency table theory to satisfy structural constraints and impute missing contact features, validated on simulations and US/German survey data.
Principal Nested Cones is a nonlinear dimension reduction technique that projects cone-structured data onto nested lower-dimensional cones to jointly represent size and shape variation.
A Bayesian mixed Hawkes process with Weibull baseline intensity and random effects is developed to model seizure clustering and heterogeneity in focal epilepsy from the Human Epilepsy Project data.
A variational inference-based framework for multi-output Gaussian process latent variable models on tails-up spatio-temporal stream networks using stream distance and process convolution.
An importance sampling correction is added to integrated Laplace approximation so that the approximate posterior for latent Gaussian models converges to the true posterior as the number of samples grows.
Large-scale neutral benchmark of survival models on low-dimensional right-censored data finds Cox PH performs comparably to more complex methods across discrimination, calibration, and predictive metrics.
Reviews asymptotic normality conditions for counting-process REMs under varying limits of n and T, with simulations illustrating effects of modeling choices like windowing and log transforms on Cox-type models.
A survey of existing measures and models for quantifying and generating higher-order homophily and heterophily in hypergraphs.
A review paper that identifies the outlier sensitivity of classical discriminant analysis and summarizes robust versions based on resistant location and scatter estimators plus diagnostic graphics.
A review summarizing definitions, canonical forms, exact and approximate distributions, numerical methods, applications, and open problems for quadratic forms in real and complex Gaussian variables, including multiforms and ratios.
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