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04/12/2023
Possibility-theoretic statistical inference offers performance and probativeness assurances
Statisticians are largely focused on developing methods that perform wel...
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03/30/2023
A possibility-theoretic solution to Basu's Bayesian–frequentist via media
Basu's via media is what he referred to as the middle road between the B...
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03/15/2023
Fiducial inference viewed through a possibility-theoretic inferential model lens
Fisher's fiducial argument is widely viewed as a failed version of Neyma...
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03/14/2023
Empirical Bayes inference in sparse high-dimensional generalized linear models
High-dimensional linear models have been extensively studied in the rece...
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01/12/2023
Elucidating Inferential Models with the Cauchy Distribution
Statistical inference as a formal scientific method to covert experience...
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11/26/2022
Valid and efficient imprecise-probabilistic inference with partial priors, II. General framework
Bayesian inference requires specification of a single, precise prior dis...
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08/26/2022
Generalized Bayes inference on a linear personalized minimum clinically important difference
Inference on the minimum clinically important difference, or MCID, is an...
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07/18/2022
Turning the information-sharing dial: efficient inference from different data sources
A fundamental aspect of statistics is the integration of data from diffe...
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04/04/2022
A PRticle filter algorithm for nonparametric estimation of multivariate mixing distributions
Predictive recursion (PR) is a fast, recursive algorithm that gives a sm...
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03/17/2022
Direct Gibbs posterior inference on risk minimizers: construction, concentration, and calibration
Real-world problems, often couched as machine learning applications, inv...
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03/13/2022
Valid and efficient imprecise-probabilistic inference across a spectrum of partial prior information
Bayesian inference quantifies uncertainty directly and formally using cl...
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12/25/2021
Inferential models and the decision-theoretic implications of the validity property
Inferential models (IMs) are data-dependent, probability-like structures...
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12/20/2021
An imprecise-probabilistic characterization of frequentist statistical inference
Between the two dominant schools of thought in statistics, namely, Bayes...
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12/19/2021
Valid inferential models for prediction in supervised learning problems
Prediction, where observed data is used to quantify uncertainty about a ...
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12/19/2021
Approximately valid probabilistic inference on a class of statistical functionals
Existing frameworks for probabilistic inference assume the inferential t...
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10/06/2021
Revisiting consistency of a recursive estimator of mixing distributions
Estimation of the mixing distribution under a general mixture model is a...
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09/14/2021
Gibbs posterior inference on a Levy density under discrete sampling
In mathematical finance, Levy processes are widely used for their abilit...
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07/04/2021
Calibrating generalized predictive distributions
In prediction problems, it is common to model the data-generating proces...
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03/03/2021
Stochastic optimization for numerical evaluation of imprecise probabilities
In applications of imprecise probability, analysts must compute lower (o...
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01/08/2021
Asymptotically optimal inference in sparse sequence models with a simple data-dependent measure
For high-dimensional inference problems, statisticians have a number of ...
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12/21/2020
A comparison of learning rate selection methods in generalized Bayesian inference
Generalized Bayes posterior distributions are formed by putting a fracti...
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12/08/2020
Gibbs posterior concentration rates under sub-exponential type losses
Bayesian posterior distributions are widely used for inference, but thei...
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10/20/2020
Estimating a mixing distribution on the sphere using predictive recursion
Mixture models are commonly used when data show signs of heterogeneity a...
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08/16/2020
Inferential models and possibility measures
The inferential model (IM) framework produces data-dependent, non-additi...
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07/31/2020
Variational approximations of empirical Bayes posteriors in high-dimensional linear models
In high-dimensions, the prior tails can have a significant effect on bot...
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06/28/2020
Valid model-free spatial prediction
Predicting the response at an unobserved location is a fundamental probl...
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02/03/2020
Gibbs posterior inference on multivariate quantiles
Bayesian and other likelihood-based methods require specification of a s...
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01/24/2020
Valid distribution-free inferential models for prediction
A fundamental problem in statistics and machine learning is that of usin...
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12/09/2019
An empirical G-Wishart prior for sparse high-dimensional Gaussian graphical models
In Gaussian graphical models, the zero entries in the precision matrix d...
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11/29/2019
Generalized inferential models for censored data
Inferential challenges that arise when data are censored have been exten...
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10/01/2019
Generalized inferential models for meta-analyses based on few studies
Meta-analysis based on only a few studies remains a challenging problem,...
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06/19/2019
Model-free posterior inference on the area under the receiver operating characteristic curve
The area under the receiver operating characteristic curve (AUC) serves ...
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06/12/2019
Permutation-based uncertainty quantification about a mixing distribution
Nonparametric estimation of a mixing distribution based on data coming f...
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05/13/2019
Variational approximations using Fisher divergence
Modern applications of Bayesian inference involve models that are suffic...
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03/03/2019
Empirical priors for prediction in sparse high-dimensional linear regression
Often the primary goal of fitting a regression model is prediction, but ...
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12/05/2018
Empirical priors and coverage of posterior credible sets in a sparse normal mean model
Bayesian methods provide a natural means for uncertainty quantification,...
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12/05/2018
On nonparametric estimation of a mixing density via the predictive recursion algorithm
Nonparametric estimation of a mixing density based on observations from ...
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10/01/2018
Bayesian inference in high-dimensional linear models using an empirical correlation-adaptive prior
In the context of a high-dimensional linear regression model, we propose...
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02/27/2018
On optimal designs for non-regular models
Classically, Fisher information is the relevant object in defining optim...
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12/11/2017
Fast nonparametric near-maximum likelihood estimation of a mixing density
Mixture models are regularly used in density estimation applications, bu...
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12/11/2017
Asymptotically optimal empirical Bayes inference in a piecewise constant sequence model
Inference on high-dimensional parameters in structured linear models is ...
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12/06/2017
On overfitting and post-selection uncertainty assessments
In a regression context, when the relevant subset of explanatory variabl...
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09/08/2017
