London (No. 32 Clipstone Street, Fitzroy Square) : Publish'd ... by S. Morgan, May 1st. 1802.

[London?]

London : [Published not identified], 1876.

A school leader outlines how research findings on reducing achievement gaps are reflected in practice at her New Mexico school.

Back is Martin Luther King Jr.'s "I Have a Dream" speech. Back is Roe v. Wade. Back is climate change. and the word "democratic." A new proposal incorporating those revisions was presented to the Michigan board of education last week.

A federal investigation of Chicago's failures to respond to sexual violence in schools raises troubling questions for school districts nationwide.

The Boston Bruins didn't take long to put the painful memory of the 2010 Stanley Cup Playoffs behind them.

Five of the top NHL prospects took part in a video call with Gretzky and were given advice as they prepare to take the next step in their careers.

Old habits die hard, and for Hall of Fame goalie Gerry Cheevers, the Bruins-Canadiens rivalry manifested when Cheesy took a shot a Montreal's Carey Price during a Zoom town hall with B's season-ticket holders on Thursday.

The Washington Capitals have placed former Winterhawks wing Brendan Leipsic on unconditional waivers with the intention of his contract being terminated after private messages revealed misogynistic comments. > The Washington Capitals have placed Brendan Leipsic on unconditional waivers for purposes of terminating his contract.https://t.co/UnADibu2yQ

[United Kingdom] : [Darcy Leigh], 2019.

Rockville, Maryland : National Institute on Drug Abuse, [1983]

[United States] : National Technical Information Service, United States Department of Commerce, 1984.

[Manchester] : School of Nursing Studies, University of Manchester, [1995?]

São Paulo : Governo do Estado de Sao Paulo, Secretaria da Segurança Pública, 1975.

Helsinki : Department of Pharmacology and Toxicology, University of Helsinki, 1985.

[Indiana] : Integrated Research Services, Inc., 1986.

UCLA guard Japreece Dean is primed to shine at the next level as she heads to the WNBA Draft in April. The do-it-all point-woman was an All-Pac-12 honoree last season, and one of only seven D-1 hoopers with at least 13 points and 5.5 assists per game.

Ruthy Hebard and Sabrina Ionescu have had a remarkable four years together in Eugene, rewriting the history books and pushing the Ducks into the national spotlight. Catch the debut of "Our Stories Unfinished Business: Sabrina Ionescu and Ruthy Hebard" at Wednesday, April 15 at 7 p.m. PT/ 8 p.m. MT on Pac-12 Network.

Wanli Qiao.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 302--344.

Abstract:
Bandwidth selection is crucial in the kernel estimation of density level sets. A risk based on the symmetric difference between the estimated and true level sets is usually used to measure their proximity. In this paper we provide an asymptotic $L^{p}$ approximation to this risk, where $p$ is characterized by the weight function in the risk. In particular the excess risk corresponds to an $L^{2}$ type of risk, and is adopted to derive an optimal bandwidth for nonparametric level set estimation of $d$-dimensional density functions ($dgeq 1$). A direct plug-in bandwidth selector is developed for kernel density level set estimation and its efficacy is verified in numerical studies.

We study the connections between spectral clustering and the problems of maximum margin clustering, and estimation of the components of level sets of a density function. Specifically, we obtain bounds on the eigenvectors of graph Laplacian matrices in terms of the between cluster separation, and within cluster connectivity. These bounds ensure that the spectral clustering solution converges to the maximum margin clustering solution as the scaling parameter is reduced towards zero. The sensitivity of maximum margin clustering solutions to outlying points is well known, but can be mitigated by first removing such outliers, and applying maximum margin clustering to the remaining points. If outliers are identified using an estimate of the underlying probability density, then the remaining points may be seen as an estimate of a level set of this density function. We show that such an approach can be used to consistently estimate the components of the level sets of a density function under very mild assumptions.

Israel Martínez-Hernández, Marc G. Genton.

Source: Brazilian Journal of Probability and Statistics, Volume 34, Number 2, 204--229.

Abstract:
As high-dimensional and high-frequency data are being collected on a large scale, the development of new statistical models is being pushed forward. Functional data analysis provides the required statistical methods to deal with large-scale and complex data by assuming that data are continuous functions, for example, realizations of a continuous process (curves) or continuous random field (surfaces), and that each curve or surface is considered as a single observation. Here, we provide an overview of functional data analysis when data are complex and spatially correlated. We provide definitions and estimators of the first and second moments of the corresponding functional random variable. We present two main approaches: The first assumes that data are realizations of a functional random field, that is, each observation is a curve with a spatial component. We call them spatial functional data . The second approach assumes that data are continuous deterministic fields observed over time. In this case, one observation is a surface or manifold, and we call them surface time series . For these two approaches, we describe software available for the statistical analysis. We also present a data illustration, using a high-resolution wind speed simulated dataset, as an example of the two approaches. The functional data approach offers a new paradigm of data analysis, where the continuous processes or random fields are considered as a single entity. We consider this approach to be very valuable in the context of big data.

Bertrand Clarke, Jennifer Clarke

Source: Statist. Surv., Volume 6, 1--73.

Abstract:
We review predictive techniques from several traditional branches of statistics. Starting with prediction based on the normal model and on the empirical distribution function, we proceed to techniques for various forms of regression and classification. Then, we turn to time series, longitudinal data, and survival analysis. Our focus throughout is on the mechanics of prediction more than on the properties of predictors.

In a public-private partnership to develop housing, resident services and hope for 102 working families in Haster Orangewood community, Jamboree Housing Corporation and the City of Anaheim announce...

(PRWeb April 27, 2020)

Read the full story at https://www.prweb.com/releases/jamboree_begins_construction_on_capstone_development_to_change_trajectory_of_neighborhood_in_anaheim_ca/prweb17073166.htm

PMA, a premier full-service provider of comprehensive financial and investment advisory services to municipalities, school districts, local government pools, insurance companies and other...

(PRWeb May 04, 2020)

Read the full story at https://www.prweb.com/releases/pma_reveals_new_logo_and_brand_identity/prweb17090459.htm

Wenjie Wang, Robert Aseltine, Kun Chen, Jun Yan.

Source: The Annals of Applied Statistics, Volume 14, Number 1, 51--73.

Abstract:
The concept of integrating data from disparate sources to accelerate scientific discovery has generated tremendous excitement in many fields. The potential benefits from data integration, however, may be compromised by the uncertainty due to incomplete/imperfect record linkage. Motivated by a suicide risk study, we propose an approach for analyzing survival data with uncertain event times arising from data integration. Specifically, in our problem deaths identified from the hospital discharge records together with reported suicidal deaths determined by the Office of Medical Examiner may still not include all the death events of patients, and the missing deaths can be recovered from a complete database of death records. Since the hospital discharge data can only be linked to the death record data by matching basic patient characteristics, a patient with a censored death time from the first dataset could be linked to multiple potential event records in the second dataset. We develop an integrative Cox proportional hazards regression in which the uncertainty in the matched event times is modeled probabilistically. The estimation procedure combines the ideas of profile likelihood and the expectation conditional maximization algorithm (ECM). Simulation studies demonstrate that under realistic settings of imperfect data linkage the proposed method outperforms several competing approaches including multiple imputation. A marginal screening analysis using the proposed integrative Cox model is performed to identify risk factors associated with death following suicide-related hospitalization in Connecticut. The identified diagnostics codes are consistent with existing literature and provide several new insights on suicide risk, prediction and prevention.

Seth Flaxman, Michael Chirico, Pau Pereira, Charles Loeffler.

Source: The Annals of Applied Statistics, Volume 13, Number 4, 2564--2585.

Abstract:
We propose a generic spatiotemporal event forecasting method which we developed for the National Institute of Justice’s (NIJ) Real-Time Crime Forecasting Challenge (National Institute of Justice (2017)). Our method is a spatiotemporal forecasting model combining scalable randomized Reproducing Kernel Hilbert Space (RKHS) methods for approximating Gaussian processes with autoregressive smoothing kernels in a regularized supervised learning framework. While the smoothing kernels capture the two main approaches in current use in the field of crime forecasting, kernel density estimation (KDE) and self-exciting point process (SEPP) models, the RKHS component of the model can be understood as an approximation to the popular log-Gaussian Cox Process model. For inference, we discretize the spatiotemporal point pattern and learn a log-intensity function using the Poisson likelihood and highly efficient gradient-based optimization methods. Model hyperparameters including quality of RKHS approximation, spatial and temporal kernel lengthscales, number of autoregressive lags and bandwidths for smoothing kernels as well as cell shape, size and rotation, were learned using cross validation. Resulting predictions significantly exceeded baseline KDE estimates and SEPP models for sparse events.

Eric Beutner, Laurent Bordes, Laurent Doyen.

Source: Bernoulli, Volume 26, Number 1, 557--586.

Abstract:
Virtual age models are very useful to analyse recurrent events. Among the strengths of these models is their ability to account for treatment (or intervention) effects after an event occurrence. Despite their flexibility for modeling recurrent events, the number of applications is limited. This seems to be a result of the fact that in the semiparametric setting all the existing results assume the virtual age function that describes the treatment (or intervention) effects to be known. This shortcoming can be overcome by considering semiparametric virtual age models with parametrically specified virtual age functions. Yet, fitting such a model is a difficult task. Indeed, it has recently been shown that for these models the standard profile likelihood method fails to lead to consistent estimators. Here we show that consistent estimators can be constructed by smoothing the profile log-likelihood function appropriately. We show that our general result can be applied to most of the relevant virtual age models of the literature. Our approach shows that empirical process techniques may be a worthwhile alternative to martingale methods for studying asymptotic properties of these inference methods. A simulation study is provided to illustrate our consistency results together with an application to real data.

Daws, Revell.

The Office of Special Counsel is recommending that ousted vaccine official Dr. Rick Bright be reinstated while it investigates his case, his lawyers announced Friday.Bright while leading coronavirus vaccine development was recently removed from his position as the director of the Department of Health and Human Services' Biomedical Advanced Research and Development Authority, and he alleges it was because he insisted congressional funding not go toward "drugs, vaccines, and other technologies that lack scientific merit" and limited the "broad use" of hydroxychloroquine after it was touted by President Trump. In a whistleblower complaint, he alleged "cronyism" at HHS. He has also alleged he was "pressured to ignore or dismiss expert scientific recommendations and instead to award lucrative contracts based on political connections."On Friday, Bright's lawyers said that the Office of Special Counsel has determined there are "reasonable grounds to believe" his firing was retaliation, The New York Times reports. The federal watchdog also recommended he be reinstated for 45 days to give the office "sufficient time to complete its investigation of Bright's allegations," CNN reports. The decision on whether to do so falls on Secretary of Health and Human Services Alex Azar, and Office of Special Counsel recommendations are "not binding," the Times notes. More stories from theweek.com Outed CIA agent Valerie Plame is running for Congress, and her launch video looks like a spy movie trailer 7 scathing cartoons about America's rush to reopen Trump says he couldn't have exposed WWII vets to COVID-19 because the wind was blowing the wrong way

Boeing CEO Dave Calhoun said the company was aiming to resume production this month, despite the ongoing grounding and coronavirus pandemic.

The Democracy Fund + UCLA Nationscape Project found some misinformation about the coronavirus is more widespread that you might think.