Nehman Lauder
Oct 1, 2005; 23:191-192
Case Studies

An Iowa man who received a postcard from his sister said he was surprised to note the card had been mailed in 1987.

Joseph Henson
Jan 1, 2016; 39:130-138
IDF-ADA Translational Symposium

Despite worrying economic projections, business leaders in Latin America remain optimistic about post-pandemic economy.

As the COVID-19 pandemic begins to take root in Africa, experts say that digitizing financial services could help keep the continent safe

Kirstine J. Bell
Jun 1, 2015; 38:1008-1015
Type 1 Diabetes at a Crossroads

The Connecticut Foundation for Dental Outreach has postponed the Connecticut Mission of Mercy Free Dental Clinic scheduled for March 20-21 in Danbury, Connecticut, "out of abundant caution" amid the coronavirus disease outbreak.

The ADA is recommending that dentists nationwide postpone elective procedures in response to the spread of the coronavirus, COVID-19, across the country.

In a statement issued April 1, the ADA issued an interim recommendation that “dentists keep their offices closed to all but urgent and emergency procedures until April 30 at the earliest.”

Washington — The Office for Civil Rights released a statement April 3 warning of an individual posing as an OCR investigator. The imposter has been calling entities covered by the Health Insurance Portability and Accountability Act of 1996 in an attempt to get patient information.

The American Dental Association and Florida Dental Association have postponed registration for the ADA FDC Annual Meeting from April 22 to early summer, in light of the COVID-19 pandemic.

There is no playbook for a pandemic. The global COVID-19 crisis is unlike anything we have seen in our lifetimes. There are very few, if any, practical models for us to follow. Now, more than ever, each of us is acutely attuned to the plight of our neighbors and communities.

OBJECTIVE

To examine the association between manganese intake and the risk of type 2 diabetes in postmenopausal women and determine whether this association is mediated by circulating markers of inflammation.

I would like to apologize to my readers for the leave of absense over the last few months. The past few months have been excessively busy for me, and School Psychologist Files has been put on the back burner. Now I'm back and have some new ideas for posts and updates. Look for new information in the coming months and weeks. Thanks for being patient with me.

This policy brief traces the successes and failures of the 1986 Immigration Reform and Control Act, which represented the first and most comprehensive legislation to take on the issue of illegal immigration to the United States. The brief makes the case that IRCA's major flaws were rooted in statutory design more than regulatory challenges and implementation by the administrative agencies.

Poland announced a delay of its Sunday presidential election, Europe's first planned vote during the coronavirus pandemic.

Resilience and strength can often be attained through unexpected routes

-- Read more on ScientificAmerican.com

With immigrants and their U.S.-born children poised to be the main source of labor-force growth, these adults are an important target for efforts to build the skills of the U.S. workforce to meet the knowledge-based economy of tomorrow. This fact sheet and state data snapshots explore the characteristics of adults without an academic degree or professional credential, by immigrant generation, race/ethnicity, and more.

OBJECTIVE

Impaired insulin-stimulated myocardial glucose uptake has occurred in patients with type 2 diabetes with or without coronary artery disease. Whether cardiac insulin resistance is present remains uncertain in subjects at risk for type 2 diabetes, such as individuals with impaired glucose tolerance (IGT) or those with normal glucose tolerance (NGT) and 1-h postload glucose ≥155 mg/dL during an oral glucose tolerance test (NGT 1-h high). This issue was examined in this study.

A brief but comprehensive look at Imposter Syndrome.

President Donald Trump has tapped Mark Schultz, a deputy commissioner of education in Nebraska, to serve as commissioner of the rehabilitation services administration at the Education Department.

New Jersey is one of many states that have experienced problems with the online administering of standardized testing this year.

Utah science teacher Skipper Coates asked her students to complete the following sentence: "What my parents don't know about social media is..."

Atlanta's top school official has resigned and is fighting to keep a report investigating his background secret.

Mike Matthews wrote several sexist and racist blog posts a decade ago that were recently unearthed.

Whites -- Race identity.

19th century.

Leipzig : J.B. Hirschfeld, 1869.

Paris : O. Doin, 1884.

The legislature sent a proposal last week to Gov. Jim Justice's desk to shutter the state's advisory education and the arts department, leaving the Gayle Manchin and her staff in the lurch.

The Bruins, Predators, Avalanche, and Blue Jackets were set to play games in Mannheim, Bern, Prague, and Helsinki.

The Boston Bruins had planned on opening up their 2020-21 NHL regular season overseas against the Nashville Predators in Prague as part of the NHL Global Series, but that plan to visit the Czech Republic has now been postponed.

The NHL announces it is postponing the Global Series games that were scheduled to be held in the Czech Republic and Finland in the 2020-21 season.

[Place of publication not identified] : [publisher not identified], [1989?]

Wei Li, Subhashis Ghosal.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 1707--1743.

Abstract:
A filament consists of local maximizers of a smooth function $f$ when moving in a certain direction. A filamentary structure is an important feature of the shape of an object and is also considered as an important lower dimensional characterization of multivariate data. There have been some recent theoretical studies of filaments in the nonparametric kernel density estimation context. This paper supplements the current literature in two ways. First, we provide a Bayesian approach to the filament estimation in regression context and study the posterior contraction rates using a finite random series of B-splines basis. Compared with the kernel-estimation method, this has a theoretical advantage as the bias can be better controlled when the function is smoother, which allows obtaining better rates. Assuming that $f:mathbb{R}^{2}mapsto mathbb{R}$ belongs to an isotropic Hölder class of order $alpha geq 4$, with the optimal choice of smoothing parameters, the posterior contraction rates for the filament points on some appropriately defined integral curves and for the Hausdorff distance of the filament are both $(n/log n)^{(2-alpha )/(2(1+alpha ))}$. Secondly, we provide a way to construct a credible set with sufficient frequentist coverage for the filaments. We demonstrate the success of our proposed method in simulations and one application to earthquake data.

François Bachoc, David Preinerstorfer, Lukas Steinberger.

Source: The Annals of Statistics, Volume 48, Number 1, 440--463.

Abstract:
We suggest general methods to construct asymptotically uniformly valid confidence intervals post-model-selection. The constructions are based on principles recently proposed by Berk et al. ( Ann. Statist. 41 (2013) 802–837). In particular, the candidate models used can be misspecified, the target of inference is model-specific, and coverage is guaranteed for any data-driven model selection procedure. After developing a general theory, we apply our methods to practically important situations where the candidate set of models, from which a working model is selected, consists of fixed design homoskedastic or heteroskedastic linear models, or of binary regression models with general link functions. In an extensive simulation study, we find that the proposed confidence intervals perform remarkably well, even when compared to existing methods that are tailored only for specific model selection procedures.

Kyoungjae Lee, Jaeyong Lee, Lizhen Lin.

Source: The Annals of Statistics, Volume 47, Number 6, 3413--3437.

Abstract:
In this paper we study the high-dimensional sparse directed acyclic graph (DAG) models under the empirical sparse Cholesky prior. Among our results, strong model selection consistency or graph selection consistency is obtained under more general conditions than those in the existing literature. Compared to Cao, Khare and Ghosh [ Ann. Statist. (2019) 47 319–348], the required conditions are weakened in terms of the dimensionality, sparsity and lower bound of the nonzero elements in the Cholesky factor. Furthermore, our result does not require the irrepresentable condition, which is necessary for Lasso-type methods. We also derive the posterior convergence rates for precision matrices and Cholesky factors with respect to various matrix norms. The obtained posterior convergence rates are the fastest among those of the existing Bayesian approaches. In particular, we prove that our posterior convergence rates for Cholesky factors are the minimax or at least nearly minimax depending on the relative size of true sparseness for the entire dimension. The simulation study confirms that the proposed method outperforms the competing methods.

Lin Liu, Yuqi Qiu, Loki Natarajan, Karen Messer.

Source: The Annals of Applied Statistics, Volume 13, Number 3, 1370--1396.

Abstract:
It is common to encounter missing data among the potential predictor variables in the setting of model selection. For example, in a recent study we attempted to improve the US guidelines for risk stratification after screening colonoscopy ( Cancer Causes Control 27 (2016) 1175–1185), with the aim to help reduce both overuse and underuse of follow-on surveillance colonoscopy. The goal was to incorporate selected additional informative variables into a neoplasia risk-prediction model, going beyond the three currently established risk factors, using a large dataset pooled from seven different prospective studies in North America. Unfortunately, not all candidate variables were collected in all studies, so that one or more important potential predictors were missing on over half of the subjects. Thus, while variable selection was a main focus of the study, it was necessary to address the substantial amount of missing data. Multiple imputation can effectively address missing data, and there are also good approaches to incorporate the variable selection process into model-based confidence intervals. However, there is not consensus on appropriate methods of inference which address both issues simultaneously. Our goal here is to study the properties of model-based confidence intervals in the setting of imputation for missing data followed by variable selection. We use both simulation and theory to compare three approaches to such post-imputation-selection inference: a multiple-imputation approach based on Rubin’s Rules for variance estimation ( Comput. Statist. Data Anal. 71 (2014) 758–770); a single imputation-selection followed by bootstrap percentile confidence intervals; and a new bootstrap model-averaging approach presented here, following Efron ( J. Amer. Statist. Assoc. 109 (2014) 991–1007). We investigate relative strengths and weaknesses of each method. The “Rubin’s Rules” multiple imputation estimator can have severe undercoverage, and is not recommended. The imputation-selection estimator with bootstrap percentile confidence intervals works well. The bootstrap-model-averaged estimator, with the “Efron’s Rules” estimated variance, may be preferred if the true effect sizes are moderate. We apply these results to the colorectal neoplasia risk-prediction problem which motivated the present work.

Xuan Cao, Kshitij Khare, Malay Ghosh.

Source: Bayesian Analysis, Volume 15, Number 1, 241--262.

Abstract:
The choice of tuning parameters in Bayesian variable selection is a critical problem in modern statistics. In particular, for Bayesian linear regression with non-local priors, the scale parameter in the non-local prior density is an important tuning parameter which reflects the dispersion of the non-local prior density around zero, and implicitly determines the size of the regression coefficients that will be shrunk to zero. Current approaches treat the scale parameter as given, and suggest choices based on prior coverage/asymptotic considerations. In this paper, we consider the fully Bayesian approach introduced in (Wu, 2016) with the pMOM non-local prior and an appropriate Inverse-Gamma prior on the tuning parameter to analyze the underlying theoretical property. Under standard regularity assumptions, we establish strong model selection consistency in a high-dimensional setting, where $p$ is allowed to increase at a polynomial rate with $n$ or even at a sub-exponential rate with $n$ . Through simulation studies, we demonstrate that our model selection procedure can outperform other Bayesian methods which treat the scale parameter as given, and commonly used penalized likelihood methods, in a range of simulation settings.

Amir Bashir, Carlos M. Carvalho, P. Richard Hahn, M. Beatrix Jones.

Source: Bayesian Analysis, Volume 14, Number 4, 1075--1090.

Abstract:
A variety of computationally efficient Bayesian models for the covariance matrix of a multivariate Gaussian distribution are available. However, all produce a relatively dense estimate of the precision matrix, and are therefore unsatisfactory when one wishes to use the precision matrix to consider the conditional independence structure of the data. This paper considers the posterior predictive distribution of model fit for these covariance models. We then undertake post-processing of the Bayes point estimate for the precision matrix to produce a sparse model whose expected fit lies within the upper 95% of the posterior predictive distribution of fit. The impact of the method for selecting the zero elements of the precision matrix is evaluated. Good results were obtained using models that encouraged a sparse posterior (G-Wishart, Bayesian adaptive graphical lasso) and selection using credible intervals. We also find that this approach is easily extended to the problem of finding a sparse set of elements that differ across a set of precision matrices, a natural summary when a common set of variables is observed under multiple conditions. We illustrate our findings with moderate dimensional data examples from finance and metabolomics.

Łukasz Rajkowski.

Source: Bayesian Analysis, Volume 14, Number 2, 477--494.

Abstract:
Mixture models are a natural choice in many applications, but it can be difficult to place an a priori upper bound on the number of components. To circumvent this, investigators are turning increasingly to Dirichlet process mixture models (DPMMs). It is therefore important to develop an understanding of the strengths and weaknesses of this approach. This work considers the MAP (maximum a posteriori) clustering for the Gaussian DPMM (where the cluster means have Gaussian distribution and, for each cluster, the observations within the cluster have Gaussian distribution). Some desirable properties of the MAP partition are proved: ‘almost disjointness’ of the convex hulls of clusters (they may have at most one point in common) and (with natural assumptions) the comparability of sizes of those clusters that intersect any fixed ball with the number of observations (as the latter goes to infinity). Consequently, the number of such clusters remains bounded. Furthermore, if the data arises from independent identically distributed sampling from a given distribution with bounded support then the asymptotic MAP partition of the observation space maximises a function which has a straightforward expression, which depends only on the within-group covariance parameter. As the operator norm of this covariance parameter decreases, the number of clusters in the MAP partition becomes arbitrarily large, which may lead to the overestimation of the number of mixture components.