Members Event

Event participants

Professor Sarah Cleveland, Louis Henkin Professor of Human and Constitutional Rights; Faculty Co-Director, Human Rights Institute, Columbia Law School

Amal Clooney, Barrister, Doughty Street Chambers

The Honourable Irwin Cotler, Chair, Raoul Wallenberg Centre for Human Rights; Minister of Justice and Attorney-General of Canada (2003-06)

Baroness Helena Kennedy QC, Director, International Bar Association’s Human Rights Institute

Lord Neuberger, President, Supreme Court of the United Kingdom (2012-17)

Maria Ressa, CEO, Rappler Online News Network

Chair: Elizabeth Wilmshurst CMG, Distinguished Fellow, International Law Programme, Chatham House

IN AN effort to alleviate misconceptions and misinterpretations of our current COVID-19 reality, and to create the idea that human beings deserve respect, especially when they are ill, ListenMi News has collaborated with the International...

Who do the Braves have in the pipeline? Get scouting reports, video, stats, projected ETAs and more for Atlanta's Top 30 Prospects on MLBPipeline.com's Prospect Watch.

Who do the Orioles have in the pipeline? Get scouting reports, video, stats, projected ETAs and more for Baltimore's Top 30 Prospects on MLBPipeline.com's Prospect Watch.

Who do the Astros have in the pipeline? Get scouting reports, video, stats, projected ETAs and more for Houston's Top 30 Prospects on MLB Pipeline's Prospect Watch.

BMJ Voices is a collection of readers’ experiences of working in the NHS. For this, The BMJ is seeking short audio submissions from UK listeners. These submissions will be published on thebmj.com. Patrick Keating, a GP from Enfield, is concerned that small practices are under pressure to increase list size, but aren't able to muster resources to...

The same piece of evidence may reach you via a journalist, or via your doctor - but the way in which that evidence is communicated is changed by your relationship between that person. Julia Beluz from Vox and Victor Montori from the Mayo Clinic join us to discuss if it's possible to reconcile those competing points of view.

That’s Jo Shapiro is a surgeon and manager in Brigham and Women’s hospital, she’s also director of the Center for Professionalism and Peer Support, and has written an editorial for The BMJ on tackling unprofessional behaviour. In this discussion, she and I talked about what she thinks (beyond the illegal) are the most damaging behaviours seen...

At Evidence Live this year, the focus of the conference was on communication of evidence - both academically, and to the public. And part of that is the role that investigative journalism has to play in that. At the BMJ we’ve used investigative journalistic techniques to try and expose wrong doing on the part of government and industry - always...

Who do the Reds have in the pipeline? Get scouting reports, video, stats, projected ETAs and more for Cincy's Top 30 Prospects on MLB Pipeline's Prospect Watch.

Who do the D-backs have in the pipeline? Get scouting reports, video, stats, projected ETAs and more for Arizona's Top 30 Prospects on MLBPipeline.com's Prospect Watch.

South Korea's military tested a ballistic missile capable of carrying a 2-ton warhead in March, according to local reports on Thursday.

The Department of Health and Human Services released Nov. 25 a five-minute video explaining how to search its list of excluded individuals and entities, called LEIE.

Washington — The Environmental Protection Agency announced March 13 that it was adding nearly 200 additional products that have qualified for use against SARS-CoV-2, the novel coronavirus that causes the disease COVID-19.

The Trump administration has released a list of hardline immigration demands—including border wall funding, restrictions on federal grants to “sanctuary” cities, and cuts to legal immigration—in exchange for legislation protecting DREAMers. This article examines the prospects for these proposals and more broadly for a legislative fix to resolve the status of unauthorized immigrants brought to the United States as children.

Televangelist Jim Bakker is recovering at his North Carolina home after having a stroke, his wife said Friday.

Originally published in June 1899

-- Read more on ScientificAmerican.com

As long-simmering passions related to federal immigration policies have come to a full boil, less noted but no less important debates are taking place at state and local levels with regards to policies affecting immigrants and their children. As states are increasingly diverging in their responses, this report examines how some of the key policies and programs that support long-term integration success are faring in this volatile era.

CALL FOR PAPERSINTERNATIONAL WORKSHOP Shaping the ‘Socialist Self’? The Role of Psy-Sciences in Communist States of the Eastern Bloc (1948–1989) Date: 6 November 2020 Venue: Prague, Czech Republic Deadline for applications: 30 June 2020 Organizing institutions: CEFRES (French Research Center in Humanities and Social Sciences in Prague) Institute of Contemporary History of the Czech Academy of Sciences Collegium Carolinum … Continue reading CfP: Shaping the ‘Socialist Self’? The Role of Psy-Sciences in Communist States of the Eastern Bloc (1948–1989) →

Officials want to hear from parents, students and educators in the wake of a newspaper series that said Texas is suppressing special education enrollment.

The assistant principal wrote a children's book featuring Pepe the Frog, a cartoon character that has been adopted by the alt-right.

Mike Locksley and the Terrapins are making a push for local five-star Caleb Williams.

Environmental economics.

Blainey, Geoffrey, 1930-

Cambridge : printed by C.J. Clay at the University Press, 1887.

London : Bemrose, 1899.

London : printed for Longman, Orme, Brown, Green, and Longmans, 1840.

If we want to work towards true inclusivity, we must show that perpetuating oppressive beliefs, intentional or not, has a consequence on something or someone other than those oppressed.

[Manchester] : [James Mudd], [between 1800 and 1899]

[between 1700 and 1799]

[London] : [Thomas. Bowles] : [John Bowles], [1728]

2017

Pac-12 Networks' Ashley Adamson speaks with former Arizona State women's basketball player Michelle Tom, who is now a doctor treating COVID-19 patients in Winslow, Arizona.

Principal component analysis (PCA) is a well-established tool in machine learning and data processing. The principal axes in PCA were shown to be equivalent to the maximum marginal likelihood estimator of the factor loading matrix in a latent factor model for the observed data, assuming that the latent factors are independently distributed as standard normal distributions. However, the independence assumption may be unrealistic for many scenarios such as modeling multiple time series, spatial processes, and functional data, where the outcomes are correlated. In this paper, we introduce the generalized probabilistic principal component analysis (GPPCA) to study the latent factor model for multiple correlated outcomes, where each factor is modeled by a Gaussian process. Our method generalizes the previous probabilistic formulation of PCA (PPCA) by providing the closed-form maximum marginal likelihood estimator of the factor loadings and other parameters. Based on the explicit expression of the precision matrix in the marginal likelihood that we derived, the number of the computational operations is linear to the number of output variables. Furthermore, we also provide the closed-form expression of the marginal likelihood when other covariates are included in the mean structure. We highlight the advantage of GPPCA in terms of the practical relevance, estimation accuracy and computational convenience. Numerical studies of simulated and real data confirm the excellent finite-sample performance of the proposed approach.

Arctic sea ice extent (SIE) in September 2019 ranked second-to-lowest in history and is trending downward. The understanding of how internal variability amplifies the effects of external $ ext{CO}_2$ forcing is still limited. We propose the VARCTIC, which is a Vector Autoregression (VAR) designed to capture and extrapolate Arctic feedback loops. VARs are dynamic simultaneous systems of equations, routinely estimated to predict and understand the interactions of multiple macroeconomic time series. Hence, the VARCTIC is a parsimonious compromise between fullblown climate models and purely statistical approaches that usually offer little explanation of the underlying mechanism. Our "business as usual" completely unconditional forecast has SIE hitting 0 in September by the 2060s. Impulse response functions reveal that anthropogenic $ ext{CO}_2$ emission shocks have a permanent effect on SIE - a property shared by no other shock. Further, we find Albedo- and Thickness-based feedbacks to be the main amplification channels through which $ ext{CO}_2$ anomalies impact SIE in the short/medium run. Conditional forecast analyses reveal that the future path of SIE crucially depends on the evolution of $ ext{CO}_2$ emissions, with outcomes ranging from recovering SIE to it reaching 0 in the 2050s. Finally, Albedo and Thickness feedbacks are shown to play an important role in accelerating the speed at which predicted SIE is heading towards 0.

In the field of numerical weather prediction (NWP), the probabilistic distribution of the future state of the atmosphere is sampled with Monte-Carlo-like simulations, called ensembles. These ensembles have deficiencies (such as conditional biases) that can be corrected thanks to statistical post-processing methods. Several ensembles exist and may be corrected with different statistiscal methods. A further step is to combine these raw or post-processed ensembles. The theory of prediction with expert advice allows us to build combination algorithms with theoretical guarantees on the forecast performance. This article adapts this theory to the case of probabilistic forecasts issued as step-wise cumulative distribution functions (CDF). The theory is applied to wind speed forecasting, by combining several raw or post-processed ensembles, considered as CDFs. The second goal of this study is to explore the use of two forecast performance criteria: the Continous ranked probability score (CRPS) and the Jolliffe-Primo test. Comparing the results obtained with both criteria leads to reconsidering the usual way to build skillful probabilistic forecasts, based on the minimization of the CRPS. Minimizing the CRPS does not necessarily produce reliable forecasts according to the Jolliffe-Primo test. The Jolliffe-Primo test generally selects reliable forecasts, but could lead to issuing suboptimal forecasts in terms of CRPS. It is proposed to use both criterion to achieve reliable and skillful probabilistic forecasts.

Tsuyoshi Kunihama, Zehang Richard Li, Samuel J. Clark, Tyler H. McCormick.

Source: The Annals of Applied Statistics, Volume 14, Number 1, 241--256.

Abstract:
The distribution of deaths by cause provides crucial information for public health planning, response and evaluation. About 60% of deaths globally are not registered or given a cause, limiting our ability to understand disease epidemiology. Verbal autopsy (VA) surveys are increasingly used in such settings to collect information on the signs, symptoms and medical history of people who have recently died. This article develops a novel Bayesian method for estimation of population distributions of deaths by cause using verbal autopsy data. The proposed approach is based on a multivariate probit model where associations among items in questionnaires are flexibly induced by latent factors. Using the Population Health Metrics Research Consortium labeled data that include both VA and medically certified causes of death, we assess performance of the proposed method. Further, we estimate important questionnaire items that are highly associated with causes of death. This framework provides insights that will simplify future data

Francisco J. R. Ruiz, Susan Athey, David M. Blei.

Source: The Annals of Applied Statistics, Volume 14, Number 1, 1--27.

Abstract:
We develop SHOPPER, a sequential probabilistic model of shopping data. SHOPPER uses interpretable components to model the forces that drive how a customer chooses products; in particular, we designed SHOPPER to capture how items interact with other items. We develop an efficient posterior inference algorithm to estimate these forces from large-scale data, and we analyze a large dataset from a major chain grocery store. We are interested in answering counterfactual queries about changes in prices. We found that SHOPPER provides accurate predictions even under price interventions, and that it helps identify complementary and substitutable pairs of products.

Huiping Xu, Xiaochun Li, Changyu Shen, Siu L. Hui, Shaun Grannis.

Source: The Annals of Applied Statistics, Volume 13, Number 3, 1753--1790.

Abstract:
The conditional independence assumption of the Felligi and Sunter (FS) model in probabilistic record linkage is often violated when matching real-world data. Ignoring conditional dependence has been shown to seriously bias parameter estimates. However, in record linkage, the ultimate goal is to inform the match status of record pairs and therefore, record linkage algorithms should be evaluated in terms of matching accuracy. In the literature, more flexible models have been proposed to relax the conditional independence assumption, but few studies have assessed whether such accommodations improve matching accuracy. In this paper, we show that incorporating the conditional dependence appropriately yields comparable or improved matching accuracy than the FS model using three real-world data linkage examples. Through a simulation study, we further investigate when conditional dependence models provide improved matching accuracy. Our study shows that the FS model is generally robust to the conditional independence assumption and provides comparable matching accuracy as the more complex conditional dependence models. However, when the match prevalence approaches 0% or 100% and conditional dependence exists in the dominating class, it is necessary to address conditional dependence as the FS model produces suboptimal matching accuracy. The need to address conditional dependence becomes less important when highly discriminating fields are used. Our simulation study also shows that conditional dependence models with misspecified dependence structure could produce less accurate record matching than the FS model and therefore we caution against the blind use of conditional dependence models.

Lisa Schlosser, Torsten Hothorn, Reto Stauffer, Achim Zeileis.

Source: The Annals of Applied Statistics, Volume 13, Number 3, 1564--1589.

Abstract:
To obtain a probabilistic model for a dependent variable based on some set of explanatory variables, a distributional approach is often adopted where the parameters of the distribution are linked to regressors. In many classical models this only captures the location of the distribution but over the last decade there has been increasing interest in distributional regression approaches modeling all parameters including location, scale and shape. Notably, so-called nonhomogeneous Gaussian regression (NGR) models both mean and variance of a Gaussian response and is particularly popular in weather forecasting. Moreover, generalized additive models for location, scale and shape (GAMLSS) provide a framework where each distribution parameter is modeled separately capturing smooth linear or nonlinear effects. However, when variable selection is required and/or there are nonsmooth dependencies or interactions (especially unknown or of high-order), it is challenging to establish a good GAMLSS. A natural alternative in these situations would be the application of regression trees or random forests but, so far, no general distributional framework is available for these. Therefore, a framework for distributional regression trees and forests is proposed that blends regression trees and random forests with classical distributions from the GAMLSS framework as well as their censored or truncated counterparts. To illustrate these novel approaches in practice, they are employed to obtain probabilistic precipitation forecasts at numerous sites in a mountainous region (Tyrol, Austria) based on a large number of numerical weather prediction quantities. It is shown that the novel distributional regression forests automatically select variables and interactions, performing on par or often even better than GAMLSS specified either through prior meteorological knowledge or a computationally more demanding boosting approach.

Family histories -- South Australia -- Bibliography.

Delta said it is making the move to protect employees amid the coronavirus pandemic, but planes have been flying near empty

Roger Koenker, Jiaying Gu.

Source: Statistical Science, Volume 34, Number 2, 209--213.

Abstract:
Efron’s elegant approach to $g$-modeling for empirical Bayes problems is contrasted with an implementation of the Kiefer–Wolfowitz nonparametric maximum likelihood estimator for mixture models for several examples. The latter approach has the advantage that it is free of tuning parameters and consequently provides a relatively simple complementary method.