Statisticians are largely focused on developing methods that perform wel...
Basu's via media is what he referred to as the middle road between the
B...
Fisher's fiducial argument is widely viewed as a failed version of Neyma...
High-dimensional linear models have been extensively studied in the rece...
Statistical inference as a formal scientific method to covert experience...
Bayesian inference requires specification of a single, precise prior
dis...
Inference on the minimum clinically important difference, or MCID, is an...
A fundamental aspect of statistics is the integration of data from diffe...
Predictive recursion (PR) is a fast, recursive algorithm that gives a sm...
Real-world problems, often couched as machine learning applications, inv...
Bayesian inference quantifies uncertainty directly and formally using
cl...
Inferential models (IMs) are data-dependent, probability-like structures...
Between the two dominant schools of thought in statistics, namely, Bayes...
Prediction, where observed data is used to quantify uncertainty about a
...
Existing frameworks for probabilistic inference assume the inferential t...
Estimation of the mixing distribution under a general mixture model is a...
In mathematical finance, Levy processes are widely used for their abilit...
In prediction problems, it is common to model the data-generating proces...
In applications of imprecise probability, analysts must compute lower (o...
For high-dimensional inference problems, statisticians have a number of
...
Generalized Bayes posterior distributions are formed by putting a fracti...
Bayesian posterior distributions are widely used for inference, but thei...
Mixture models are commonly used when data show signs of heterogeneity a...
The inferential model (IM) framework produces data-dependent, non-additi...
In high-dimensions, the prior tails can have a significant effect on bot...
Predicting the response at an unobserved location is a fundamental probl...
Bayesian and other likelihood-based methods require specification of a
s...
A fundamental problem in statistics and machine learning is that of usin...
In Gaussian graphical models, the zero entries in the precision matrix
d...
Inferential challenges that arise when data are censored have been
exten...
Meta-analysis based on only a few studies remains a challenging problem,...
The area under the receiver operating characteristic curve (AUC) serves ...
Nonparametric estimation of a mixing distribution based on data coming f...
Modern applications of Bayesian inference involve models that are
suffic...
Often the primary goal of fitting a regression model is prediction, but ...
Bayesian methods provide a natural means for uncertainty quantification,...
Nonparametric estimation of a mixing density based on observations from ...
In the context of a high-dimensional linear regression model, we propose...
Classically, Fisher information is the relevant object in defining optim...
Mixture models are regularly used in density estimation applications, bu...
Inference on high-dimensional parameters in structured linear models is ...
In a regression context, when the relevant subset of explanatory variabl...
In this paper we use an iterative algorithm for solving Fredholm equatio...