arXiv Statistics @arxiv_stats@qoto.org

Shrinkage-based random local clocks with scalable inference. (arXiv:2105.07119v1 [stat.ME]) http://arxiv.org/abs/2105.07119

Sparsity Likelihood for Sparse Signal and Change-point Detection. (arXiv:2105.07137v1 [math.ST]) http://arxiv.org/abs/2105.07137

Singularity for bifractional and trifractional Brownian motions based on their Hurst indices. (arXiv:2105.07156v1 [math.PR]) http://arxiv.org/abs/2105.07156

Spatial Statistics. (arXiv:2105.07216v1 [stat.ME]) http://arxiv.org/abs/2105.07216

On the Distributional Properties of Adaptive Gradients. (arXiv:2105.07222v1 [cs.LG]) http://arxiv.org/abs/2105.07222

Calibrating sufficiently. (arXiv:2105.07283v1 [stat.ML]) http://arxiv.org/abs/2105.07283

Disagreement Concerning Effect-Measure Modification. (arXiv:2105.07285v1 [stat.ME]) http://arxiv.org/abs/2105.07285

Improved Algorithms for Agnostic Pool-based Active Classification. (arXiv:2105.06499v1 [cs.LG]) http://arxiv.org/abs/2105.06499

On the Bahadur representation of sample quantiles for score functionals. (arXiv:2105.06500v1 [math.ST]) http://arxiv.org/abs/2105.06500

Deep Neural Networks Guided Ensemble Learning for Point Estimation in Finite Samples. (arXiv:2105.06523v1 [stat.ME]) http://arxiv.org/abs/2105.06523

Brazilian Obstetric Observatory. (arXiv:2105.06534v1 [stat.AP]) http://arxiv.org/abs/2105.06534

Bias, Fairness, and Accountability with AI and ML Algorithms. (arXiv:2105.06558v1 [stat.ML]) http://arxiv.org/abs/2105.06558

Extending Models Via Gradient Boosting: An Application to Mendelian Models. (arXiv:2105.06559v1 [stat.AP]) http://arxiv.org/abs/2105.06559

The cross-sectional distribution of portfolio returns and applications. (arXiv:2105.06573v1 [cs.CE]) http://arxiv.org/abs/2105.06573

Empirical Evaluation of Biased Methods for Alpha Divergence Minimization. (arXiv:2105.06587v1 [cs.LG]) http://arxiv.org/abs/2105.06587

Learning Gaussian Graphical Models with Latent Confounders. (arXiv:2105.06600v1 [stat.ME]) http://arxiv.org/abs/2105.06600

How to effectively use machine learning models to predict the solutions for optimization problems: lessons from loss function. (arXiv:2105.06618v1 [cs.LG]) http://arxiv.org/abs/2105.06618

A Path Model to Infer Mathematics Performance: The Interrelated Impact of Motivation, Attitude, Learning Style and Teaching Strategies Variables. (arXiv:2105.05850v1 [stat.AP]) http://arxiv.org/abs/2105.05850

Calculating Expected Value of Sample Information Adjusting for Imperfect Implementation. (arXiv:2105.05901v1 [stat.ME]) http://arxiv.org/abs/2105.05901

A new characterization of discrete decomposable models. (arXiv:2105.05907v1 [math.ST]) http://arxiv.org/abs/2105.05907