OBJECTIVE

To evaluate the respective contributions of short-term glycemic variability and mean daily glucose (MDG) concentration to the risk of hypoglycemia in type 1 diabetes.

Caribbean immigrants represent 10 percent of the 44.5 million immigrants in the United States, with the vast majority coming from just five countries: Cuba, the Dominican Republic, Jamaica, Haiti, and Trinidad and Tobago. Depending on their origin country and period of arrival, immigrants from the Caribbean have varying skill levels, racial composition, language background, and motivations for migration, as this article explores.

The international group SNOMED International honored ADA member dentist Dr. Mark Jurkovich with the Award of Excellence for spearheading the important work the ADA has done on SNODENT, the group announced Jan. 16.

Dr. Leo M. Sreebny, Ph.D., who had a long and distinguished career in academia as a professor of dentistry and researcher, particularly in issues related to saliva and dry mouth, died April 5 from complications of COVID-19 at age 98.

OBJECTIVE

In patients with type 2 diabetes (T2D) and critical limb ischemia (CLI), migration of circulating CD34⁺ cells predicted cardiovascular mortality at 18 months after revascularization. This study aimed to provide long-term validation and mechanistic understanding of the biomarker.

South Dakota Gov. Kristi Noem has ordered two Sioux tribes to remove checkpoints designed to curb the coronavirus on tribal lands.

During a time when the world is focused on the current COVID-19 pandemic, a physical health crisis, and all those treating the physical symptoms of this pandemic, we must not...

There are two ways to think about the staggering amounts of money given by special interest groups to politicians -- the type of contributions that were detailed for the last quarter in reports filed yesterday by presidential candidates and members of Congress.

I have never watched Hidden Figures. Was that a bad way to start off an article concerning the late Katherine Johnson, the NASA legend whose persistence, precision, and proclivity for mathematics sent America to space in the 1960s? Maybe it was. … Continue reading →

WASHINGTON – Even as governments in Latin America and the Caribbean have taken generous and innovative steps to address forced displacement from Venezuela and more recently Nicaragua, the warm welcome has cooled in places amid the vast scale of the inflows, strains on public services and growing public concern.

WASHINGTON – A pesar de que los gobiernos de América Latina y el Caribe han tomado medidas generosas e innovadoras para lidiar con el desplazamiento forzado desde Venezuela y más recientemente desde Nicaragua, la cálida bienvenida se ha enfriado en algunos lugares a medida que el número de entradas, la presión sobre los servicios públicos y la preocupación del público aumenta.

This recipe for Kansas City ribs was featured on Foodie Tuesday, a weekly segment with Raf Epstein on Drive, 774 ABC Melbourne, 3.30PM courtesy of Lance Rosen. Lance's new book is called "Temples of BBQ".

Delicious weekend dish.

This webinar marks the release of MPI's Latin American and Caribbean Migration Portal that offers up-to-date, authoritative research and data on migration trends and policies, and a report examining the policy responses of 11 countries in Latin America and the Caribbean to increased Venezuelan and Nicaraguan migration.

MPI llevó a cabo un seminario en línea para marcar el lanzamiento de: Un portal sobre Migración en América Latina y el Caribe; y un informe que examina los efectos de las políticas migratorias y de integración en 11 países en América Latina y el Caribe ante el aumento de la migración venezolana y nicaragüense.

Large-scale displacement from Venezuela and Nicaragua is reshaping the migration landscape in much of Latin America and the Caribbean. This report, accompanied by the launch of a new Migration Portal offering research and analysis on the region, examines the immigration and integration policy responses of 11 countries, including pathways to legal status and measures to integrate newcomers into schools, health-care systems, and labor markets.

El gran desplazamiento forzado de personas en Venezuela y Nicaragua está transformando el panorama migratorio en gran parte de América Latina y el Caribe. Este informe examina las respuestas de las políticas de inmigración e integración de once países, incluyendo vías de regularización y medidas para integrar a los recién llegados en las escuelas y mercados laborales. Este informe acompaña el lanzamiento de un Portal Sobre Migración que ofrece investigación y análisis actualizados sobre tendencias y políticas de inmigración en la región.

You have mail! Inoreader now allows you to subscribe to Email Newsletters just as regular RSS feeds. By creating a…

Fleeing crisis, nearly 4 million Venezuelans have moved to other Latin American and Caribbean countries over the past few years. This webinar marked the launch of a Latin American and Caribbean Migration Portal, and a report examining the migration and integration policy responses in the region. 

MPI llevó a cabo un seminario en línea para marcar el lanzamiento de: Un portal sobre Migración en América Latina y el Caribe; y un informe que examina los efectos de las políticas migratorias y de integración en 11 países en América Latina y el Caribe ante el aumento de la migración venezolana y nicaragüense.

New report on Oregon's tribal students show they start out behind, miss more school, and are more likely to drop out.

Reconnecting with mentors, friends, coaches and professors, these football greats reflected on the experiences that they carried with them well beyond their college years. While they all share different stories, their journeys to the NFL were each met with adversity, and whether on or off the field, these obstacles served as tests for each individual athlete to overcome. These challenges made them stronger, earning them valuable lessons and helping them create special connections with the university they will always call ‘home.’

Oral history -- Methodology.

Aboriginal Australians -- Land tenure.

Carlsruhae : D.R. Marx, 1819.

London : Longmans, Green, 1890-1893.

London : Longmans, Green, 1912-1913.

London : Longmans, Green, 1865-1869.

Leipzig : P. Hobbing, 1890.

Edinburgh : Young J. Pentland, 1889.

Paris : O. Doin, 1879.

Glasgow : Hedderwick, 1822.

London : Bailliere, Tindall and Cox, 1886.

[Rome], [1725]

[Rome], [1725]

[Dresden? Leipzig?] : Verlag und Eigentum von A.E. Payne, [1848?] (Printed by H. Boulton)

Calcutta, India : Cleanair, Campaign for Smoke-free Environment, [198-?]

Annalisa Fabretti, Samantha Leorato.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 1611--1647.

Abstract:
We propose a method to generate random distributions with known quantile distribution, or, more generally, with known distribution for some form of generalized quantile. The method takes inspiration from the random Sequential Barycenter Array distributions (SBA) proposed by Hill and Monticino (1998) which generates a Random Probability Measure (RPM) with known expected value. We define the Sequential Quantile Array (SQA) and show how to generate a random SQA from which we can derive RPMs. The distribution of the generated SQA-RPM can have full support and the RPMs can be both discrete, continuous and differentiable. We face also the problem of the efficient implementation of the procedure that ensures that the approximation of the SQA-RPM by a finite number of steps stays close to the SQA-RPM obtained theoretically by the procedure. Finally, we compare SQA-RPMs with similar approaches as Polya Tree.

Karl Ewald, Ulrike Schneider.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 944--969.

Abstract:
We derive expressions for the finite-sample distribution of the Lasso estimator in the context of a linear regression model in low as well as in high dimensions by exploiting the structure of the optimization problem defining the estimator. In low dimensions, we assume full rank of the regressor matrix and present expressions for the cumulative distribution function as well as the densities of the absolutely continuous parts of the estimator. Our results are presented for the case of normally distributed errors, but do not hinge on this assumption and can easily be generalized. Additionally, we establish an explicit formula for the correspondence between the Lasso and the least-squares estimator. We derive analogous results for the distribution in less explicit form in high dimensions where we make no assumptions on the regressor matrix at all. In this setting, we also investigate the model selection properties of the Lasso and show that possibly only a subset of models might be selected by the estimator, completely independently of the observed response vector. Finally, we present a condition for uniqueness of the estimator that is necessary as well as sufficient.

Feature screening is a powerful tool in processing high-dimensional data. When the sample size N and the number of features p are both large, the implementation of classic screening methods can be numerically challenging. In this paper, we propose a distributed screening framework for big data setup. In the spirit of 'divide-and-conquer', the proposed framework expresses a correlation measure as a function of several component parameters, each of which can be distributively estimated using a natural U-statistic from data segments. With the component estimates aggregated, we obtain a final correlation estimate that can be readily used for screening features. This framework enables distributed storage and parallel computing and thus is computationally attractive. Due to the unbiased distributive estimation of the component parameters, the final aggregated estimate achieves a high accuracy that is insensitive to the number of data segments m. Under mild conditions, we show that the aggregated correlation estimator is as efficient as the centralized estimator in terms of the probability convergence bound and the mean squared error rate; the corresponding screening procedure enjoys sure screening property for a wide range of correlation measures. The promising performances of the new method are supported by extensive numerical examples.

We propose graph-dependent implicit regularisation strategies for synchronised distributed stochastic subgradient descent (Distributed SGD) for convex problems in multi-agent learning. Under the standard assumptions of convexity, Lipschitz continuity, and smoothness, we establish statistical learning rates that retain, up to logarithmic terms, single-machine serial statistical guarantees through implicit regularisation (step size tuning and early stopping) with appropriate dependence on the graph topology. Our approach avoids the need for explicit regularisation in decentralised learning problems, such as adding constraints to the empirical risk minimisation rule. Particularly for distributed methods, the use of implicit regularisation allows the algorithm to remain simple, without projections or dual methods. To prove our results, we establish graph-independent generalisation bounds for Distributed SGD that match the single-machine serial SGD setting (using algorithmic stability), and we establish graph-dependent optimisation bounds that are of independent interest. We present numerical experiments to show that the qualitative nature of the upper bounds we derive can be representative of real behaviours.

In many areas, practitioners need to analyze large data sets that challenge conventional single-machine computing. To scale up data analysis, distributed and parallel computing approaches are increasingly needed. Here we study a fundamental and highly important problem in this area: How to do ridge regression in a distributed computing environment? Ridge regression is an extremely popular method for supervised learning, and has several optimality properties, thus it is important to study. We study one-shot methods that construct weighted combinations of ridge regression estimators computed on each machine. By analyzing the mean squared error in a high-dimensional random-effects model where each predictor has a small effect, we discover several new phenomena. Infinite-worker limit: The distributed estimator works well for very large numbers of machines, a phenomenon we call 'infinite-worker limit'. Optimal weights: The optimal weights for combining local estimators sum to more than unity, due to the downward bias of ridge. Thus, all averaging methods are suboptimal. We also propose a new Weighted ONe-shot DistributEd Ridge regression algorithm (WONDER). We test WONDER in simulation studies and using the Million Song Dataset as an example. There it can save at least 100x in computation time, while nearly preserving test accuracy.

When the data is distributed across multiple servers, lowering the communication cost between the servers (or workers) while solving the distributed learning problem is an important problem and is the focus of this paper. In particular, we propose a fast, and communication-efficient decentralized framework to solve the distributed machine learning (DML) problem. The proposed algorithm, Group Alternating Direction Method of Multipliers (GADMM) is based on the Alternating Direction Method of Multipliers (ADMM) framework. The key novelty in GADMM is that it solves the problem in a decentralized topology where at most half of the workers are competing for the limited communication resources at any given time. Moreover, each worker exchanges the locally trained model only with two neighboring workers, thereby training a global model with a lower amount of communication overhead in each exchange. We prove that GADMM converges to the optimal solution for convex loss functions, and numerically show that it converges faster and more communication-efficient than the state-of-the-art communication-efficient algorithms such as the Lazily Aggregated Gradient (LAG) and dual averaging, in linear and logistic regression tasks on synthetic and real datasets. Furthermore, we propose Dynamic GADMM (D-GADMM), a variant of GADMM, and prove its convergence under the time-varying network topology of the workers.

Raj Kamal Maurya, Yogesh Mani Tripathi.

Source: Brazilian Journal of Probability and Statistics, Volume 34, Number 2, 345--369.

Abstract:
We consider estimation of the multicomponent stress-strength reliability under progressive Type II censoring under the assumption that stress and strength variables follow Burr XII distributions with a common shape parameter. Maximum likelihood estimates of the reliability are obtained along with asymptotic intervals when common shape parameter may be known or unknown. Bayes estimates are also derived under the squared error loss function using different approximation methods. Further, we obtain exact Bayes and uniformly minimum variance unbiased estimates of the reliability for the case common shape parameter is known. The highest posterior density intervals are also obtained. We perform Monte Carlo simulations to compare the performance of proposed estimates and present a discussion based on this study. Finally, two real data sets are analyzed for illustration purposes.

Fredy Castellares, Artur J. Lemonte, Marcos A. C. Santos.

Source: Brazilian Journal of Probability and Statistics, Volume 34, Number 1, 90--111.

Abstract:
We introduce a two-parameter discrete distribution that may have a zero vertex and can be useful for modeling overdispersion. The discrete Nielsen distribution generalizes the Fisher logarithmic (i.e., logarithmic series) and Stirling type I distributions in the sense that both can be considered displacements of the Nielsen distribution. We provide a comprehensive account of the structural properties of the new discrete distribution. We also show that the Nielsen distribution is infinitely divisible. We discuss maximum likelihood estimation of the model parameters and provide a simple method to find them numerically. The usefulness of the proposed distribution is illustrated by means of three real data sets to prove its versatility in practical applications.

Abbas Pak, M. E. Ghitany, Mohammad Reza Mahmoudi.

Source: Brazilian Journal of Probability and Statistics, Volume 33, Number 4, 894--914.

Abstract:
This paper focuses on Bayesian estimation of the parameters and reliability function of the power Lindley distribution by using various symmetric and asymmetric loss functions. Assuming suitable priors on the parameters, Bayes estimates are derived by using squared error, linear exponential (linex) and general entropy loss functions. Since, under these loss functions, Bayes estimates of the parameters do not have closed forms we use lindley’s approximation technique to calculate the Bayes estimates. Moreover, we obtain the Bayes estimates of the parameters using a Markov Chain Monte Carlo (MCMC) method. Simulation studies are conducted in order to evaluate the performances of the proposed estimators under the considered loss functions. Finally, analysis of a real data set is presented for illustrative purposes.