An observational block design has I blocks matched for covariates and J individuals per block, but treatments were not randomly assigned to individuals within blocks, as would have been done in an ...

Asymptotic goodness-of-fit methods in contingency table analysis can struggle with sparse data, especially in multi-way tables where it can be infeasible to meet sample size requirements for a robu...

Distance covariance (Székely et al. 2007) is a fascinating recent notion, which is popular as a test for dependence of any type between random variables X and Y. This approach deserves to be touche...

Despite the fact that most police departments in the U.S. serve jurisdictions with fewer than 10,000 residents, policing practices in small towns are understudied. This is due in part to data limit...

“The use of health care claims datasets often encounters criticism due to the pervasive issues of omitted variables and inaccuracies or mis-measurements in available confounders. Ultimately, the tr...

A full parametric and linear specification may be insufficient to capture complicated patterns in studies exploring complex features, such as those investigating age-related changes in brain functi...

In recent years, there has been an explosion in the growth of undergraduate statistics and data science programs across the US. Simultaneously, there has been clear guidance written on curriculum d...

The Second Generation P-Value (SGPV) measures the overlap between an estimated interval and a composite hypothesis of parameter values. We develop a sequential monitoring scheme of the SGPV (SeqSGP...

The Kolmogorov–Smirnov (KS) test is widely employed to assess the goodness-of-fit of a hypothesized continuous distribution to a sample. Despite its popularity, the test is frequently misused in th...

In Bayesian analysis, the selection of a prior distribution is typically done by considering each parameter in the model. While this can be convenient, in many scenarios it may be desirable to plac...

The best time to play the lottery is when the jackpot has rolled over several times and grown large, but not so large that you must share the prize if you win. We examine maximizing the expected va...

This article describes a simple and fast algorithm for generating correlation matrices ( R) with a known average correlation. The algorithm should be useful for researchers desiring plausible R m...

Confidence interval performance is typically assessed in terms of two criteria: coverage probability and interval width (or margin of error). In this paper, we assess the performance of four common...

Squared 2 × 2 tables with binary data from matched pairs are typically analysed using Cochran-Mantel-Haenszel methodology, conditional logistic regression, or random intercepts logistic regression....

Published in The American Statistician (Ahead of Print, 2024)

Published in The American Statistician (Vol. 78, No. 2, 2024)

Published in The American Statistician (Vol. 78, No. 2, 2024)

Let X1,…,Xn be a random sample from the gamma distribution with density f(x)=λαxα−1e−λx/Γ(α), x > 0, where both α>0 (the shape parameter) and λ>0 (the reciprocal scale parameter) are unknown. The m...

Published in The American Statistician (Vol. 78, No. 3, 2024)

Published in The American Statistician (Vol. 78, No. 3, 2024)

Probability predictions are essential to inform decision making across many fields. Ideally, probability predictions are (i) well calibrated, (ii) accurate, and (iii) bold, i.e., spread out enough ...

In this paper, I propose a tractable approach to Bayesian inference in a simple linear regression model for which the standard exogeneity assumption does not hold. By specifying a beta prior for th...

I present the moments of the nonnegative adjusted estimator of the squared multiple correlation ρ2, the coefficient of determination for random-predictor regression. This estimator, first proposed...

There is no consensus on the meaning of the term “randomization test.” Contradictory uses of the term are leading to confusion, misunderstandings and indeed invalid data analyses. A main source of ...

Estimating a covariance matrix is central to high-dimensional data analysis. Empirical analyses of high-dimensional biomedical data, including genomics, proteomics, microbiome, and neuroimaging, am...

A gap remains between developing risk prediction models and deploying models to support real-world decision making, especially in high-stakes situations. Human-experts’ reasoning abilities remain c...

Adversarial Risk Analysis (ARA) allows for much more realistic modeling of game theoretical decision problems than Bayesian game theory. While ARA solutions for various applications have been discu...

While log-Gaussian Cox process regression models are useful tools for modeling point patterns, they can be technically difficult to fit and require users to learn/adopt bespoke software. We show th...

Observational studies of treatment effects require adjustment for confounding variables. However, causal inference methods typically cannot deliver perfect adjustment on all measured baseline varia...

Low-frequency earthquakes (LFEs) are small magnitude earthquakes with frequencies of 1–10 Hertz which often occur in overlapping sequence forming persistent seismic tremors. They provide insights i...

Published in The American Statistician (Vol. 78, No. 1, 2024)

This paper advocates proximal Markov Chain Monte Carlo (ProxMCMC) as a flexible and general Bayesian inference framework for constrained or regularized estimation. Originally introduced in the Baye...

Alon and Malinovsky recently proved that it takes on average 2.42849… rolls of fair six-sided dice until the first time the total sum of all rolls arrives at a prime. Naturally, one may extend the ...

Despite its drawbacks, the complete case analysis is commonly used in regression models with incomplete covariates. Understanding when the complete case analysis will lead to consistent parameter e...

The COVID-19 pandemic was responsible for the cancellation of both the men’s and women’s 2020 National Collegiate Athletic Association (NCAA) Division I basketball tournaments. Starting from the po...

A special-topics undergraduate course about the history of statistics which was taught in Spring 2023 at the University of South Carolina is described. We review other similar courses (past and cur...

The problems of generalization and transportation of treatment effect estimates from a study sample to a target population are central to empirical research and statistical methodology. In both ran...

This article introduces a new kind of histogram-based representation for univariate random variables, named the phistogram because of its perceptual qualities. The technique relies on shifted group...

Imputation of missing data in high-dimensional datasets with more variables P than samples N, P≫N, is hampered by the data dimensionality. For multivariate imputation, the covariance matrix is ill ...

When does inspecting a certain graphical plot allow for an investigator to reach the right statistical conclusion? Visualizations are commonly used for various tasks in statistics—including model d...

Monte Carlo integration is a commonly used technique to compute intractable integrals and is typically thought to perform poorly for very high-dimensional integrals. To show that this is not always...

The average causal effect can often be best understood in the context of its variation. We demonstrate with two sets of four graphs, all of which represent the same average effect but with much dif...

Published in The American Statistician (Vol. 77, No. 4, 2023)

Published in The American Statistician (Vol. 77, No. 4, 2023)

Published in The American Statistician (Vol. 77, No. 4, 2023)

Published in The American Statistician (Just accepted, 2023)

In practice, the ultimate outcome of many important discrimination cases, for example, the Wal-Mart, Nike and Goldman-Sachs equal pay cases, is determined at the stage when the plaintiffs request t...

The rise of internet-based services and products in the late 1990s brought about an unprecedented opportunity for online businesses to engage in large scale data-driven decision making. Over the pa...

Melded confidence intervals were proposed as a way to combine two independent one-sample confidence intervals to obtain a two-sample confidence interval for a quantity like a difference or a ratio....

United States law bars using peremptory strikes during jury selection because of prospective juror race, ethnicity, sex, or membership in certain other cognizable classes. Here, we extend a Bayesia...

Data stewards and analysts can promote transparent and trustworthy science and policy-making by facilitating assessments of the sensitivity of published results to alternate analysis choices. For e...

An assessment of the causal treatment effect in the development and progression of certain diseases is important in clinical trials and biomedical studies. However, it is not possible to infer a ca...

This article compares distribution functions among pairs of locations in their domains, in contrast to the typical approach of univariate comparison across individual locations. This bivariate appr...

Zero-inflated count data models are widely used in various fields such as ecology, epidemiology, and transportation, where count data with a large proportion of zeros is prevalent. Despite their wi...

Epidemiologic screening programs often make use of tests with small, but nonzero probabilities of misdiagnosis. In this article, we assume the target population is finite with a fixed number of tru...

In American football, a pass rush is an attempt by the defensive team to disrupt the offense and prevent the quarterback (QB) from completing a pass. Existing metrics for assessing pass rush perfor...

Neural Bayes estimators are neural networks that approximate Bayes estimators. They are fast, likelihood-free, and amenable to rapid bootstrap-based uncertainty quantification. In this article, we ...

M. I. Yadrenko discovered that the expectation of the minimum number N1 of independent and identically distributed uniform random variables on (0, 1) that have to be added to exceed 1 is e. For any...

Published in The American Statistician (Vol. 77, No. 3, 2023)

Probabilistic models such as logistic regression, Bayesian classification, neural networks, and models for natural language processing, are increasingly more present in both undergraduate and gradu...