The very first case of corona-virus illness was recorded on 30 January 2020, in India and the number of infected cases, including the death toll, continues to rise. In this paper, we present short-term forecasts of COVID-19 for 28 Indian states and five union territories using real-time data from 30 January to 21 April 2020. Applying Holt's second-order exponential smoothing method and autoregressive integrated moving average (ARIMA) model, we generate 10-day ahead forecasts of the likely number of infected cases and deaths in India for 22 April to 1 May 2020. Our results show that the number of cumulative cases in India will rise to 36335.63 [PI 95% (30884.56, 42918.87)], concurrently the number of deaths may increase to 1099.38 [PI 95% (959.77, 1553.76)] by 1 May 2020. Further, we have divided the country into severity zones based on the cumulative cases. According to this analysis, Maharashtra is likely to be the most affected states with around 9787.24 [PI 95% (6949.81, 13757.06)] cumulative cases by 1 May 2020. However, Kerala and Karnataka are likely to shift from the red zone (i.e. highly affected) to the lesser affected region. On the other hand, Gujarat and Madhya Pradesh will move to the red zone. These results mark the states where lockdown by 3 May 2020, can be loosened.

Introducing common shocks is a popular dependence modelling approach, with some recent applications in loss reserving. The main advantage of this approach is the ability to capture structural dependence coming from known relationships. In addition, it helps with the parsimonious construction of correlation matrices of large dimensions. However, complications arise in the presence of "unbalanced data", that is, when (expected) magnitude of observations over a single triangle, or between triangles, can vary substantially. Specifically, if a single common shock is applied to all of these cells, it can contribute insignificantly to the larger values and/or swamp the smaller ones, unless careful adjustments are made. This problem is further complicated in applications involving negative claim amounts. In this paper, we address this problem in the loss reserving context using a common shock Tweedie approach for unbalanced data. We show that the solution not only provides a much better balance of the common shock proportions relative to the unbalanced data, but it is also parsimonious. Finally, the common shock Tweedie model also provides distributional tractability.

A free manuscripts workshop for PhD students at Wellcome Collection, 01 June 2018 Engaging with an artefact from the past is often a powerful experience, eliciting emotional and sensory, as well as analytical, responses. Researchers in the library at Wellcome… Continue reading

John Goes, Gilad Lerman, Boaz Nadler.

Source: The Annals of Statistics, Volume 48, Number 1, 86--110.

Abstract:
Estimating a high-dimensional sparse covariance matrix from a limited number of samples is a fundamental task in contemporary data analysis. Most proposals to date, however, are not robust to outliers or heavy tails. Toward bridging this gap, in this work we consider estimating a sparse shape matrix from $n$ samples following a possibly heavy-tailed elliptical distribution. We propose estimators based on thresholding either Tyler’s M-estimator or its regularized variant. We prove that in the joint limit as the dimension $p$ and the sample size $n$ tend to infinity with $p/n ogamma>0$, our estimators are minimax rate optimal. Results on simulated data support our theoretical analysis.

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.

Ahmed Aziz Ezzat, Mikyoung Jun, Yu Ding.

Source: The Annals of Applied Statistics, Volume 13, Number 3, 1484--1510.

Abstract:
Accurate short-term forecasts are indispensable for the integration of wind energy in power grids. On a wind farm, local wind conditions exhibit sizeable variations at a fine temporal resolution. Existing statistical models may capture the in-sample variations in wind behavior, but are often shortsighted to those occurring in the near future, that is, in the forecast horizon. The calibrated regime-switching method proposed in this paper introduces an action of regime dependent calibration on the predictand (here the wind speed variable), which helps correct the bias resulting from out-of-sample variations in wind behavior. This is achieved by modeling the calibration as a function of two elements: the wind regime at the time of the forecast (and the calibration is therefore regime dependent), and the runlength, which is the time elapsed since the last observed regime change. In addition to regime-switching dynamics, the proposed model also accounts for other features of wind fields: spatio-temporal dependencies, transport effect of wind and nonstationarity. Using one year of turbine-specific wind data, we show that the calibrated regime-switching method can offer a wide margin of improvement over existing forecasting methods in terms of both wind speed and power.

The New York City police department (NYPD) is conducting an internal investigation into a May 2 incident involving the violent arrests of multiple people, allegedly members of a group who were not social distancing

Georgia is one of four states that doesn't have a hate crime law. Arbery's killing has reignited calls for legislation.

The Justice Department announced Thursday that it is dropping its criminal case against President Trump's first national security adviser Michael Flynn. Flynn twice admitted in court he lied to the FBI about his conversations with Russia's U.S. ambassador, and then cooperated in Special Counsel Robert Mueller's investigation. It was an unusual move by the Justice Department, and CNN's legal and political analysts smelled a rat."Attorney General [William] Barr is already being accused of creating a special justice system just for President Trump's friends," and this will only feed that perception, CNN's Jake Tapper suggested. Political correspondent Sara Murray agreed, noting that the prosecutor in the case, Brandon Van Grack, withdrew right before the Justice Department submitted its filing, just like when Barr intervened to request a reduced sentence for Roger Stone.National security correspondent Jim Sciutto laid out several reason why the substance of Flynn's admitted lie was a big deal, and chief legal analyst Jeffrey Toobin was appalled. "It is one of the most incredible legal documents I have read, and certainly something that I never expected to see from the United States Department of Justice," Toobin said. "The idea that the Justice Department would invent an argument -- an argument that the judge in this case has already rejected -- and say that's a basis for dropping a case where a defendant admitted his guilt shows that this is a case where the fix was in."Barr told CBS News' Cathrine Herridge on Thursday that dropping Flynn's case actually "sends the message that there is one standard of justice in this country." Herridge told Barr he would take flak for this, asking: "When history looks back on this decision, how do you think it will be written?" Barr laughed: "Well, history's written by the winners. So it largely depends on who's writing the history." Watch below. 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

Qingpo Cai, Jian Kang, Tianwei Yu.

Source: Bayesian Analysis, Volume 15, Number 1, 79--102.

Abstract:
Selecting informative nodes over large-scale networks becomes increasingly important in many research areas. Most existing methods focus on the local network structure and incur heavy computational costs for the large-scale problem. In this work, we propose a novel prior model for Bayesian network marker selection in the generalized linear model (GLM) framework: the Thresholded Graph Laplacian Gaussian (TGLG) prior, which adopts the graph Laplacian matrix to characterize the conditional dependence between neighboring markers accounting for the global network structure. Under mild conditions, we show the proposed model enjoys the posterior consistency with a diverging number of edges and nodes in the network. We also develop a Metropolis-adjusted Langevin algorithm (MALA) for efficient posterior computation, which is scalable to large-scale networks. We illustrate the superiorities of the proposed method compared with existing alternatives via extensive simulation studies and an analysis of the breast cancer gene expression dataset in the Cancer Genome Atlas (TCGA).

Anindya Bhadra, Jyotishka Datta, Nicholas G. Polson, Brandon Willard.

Source: Statistical Science, Volume 34, Number 3, 405--427.

Abstract:
The goal of this paper is to contrast and survey the major advances in two of the most commonly used high-dimensional techniques, namely, the Lasso and horseshoe regularization. Lasso is a gold standard for predictor selection while horseshoe is a state-of-the-art Bayesian estimator for sparse signals. Lasso is fast and scalable and uses convex optimization whilst the horseshoe is nonconvex. Our novel perspective focuses on three aspects: (i) theoretical optimality in high-dimensional inference for the Gaussian sparse model and beyond, (ii) efficiency and scalability of computation and (iii) methodological development and performance.

Ehud Karavani, Tal El-Hay, Yishai Shimoni, Chen Yanover.

Source: Statistical Science, Volume 34, Number 1, 86--89.

Abstract:
Data competitions proved to be highly beneficial to the field of machine learning, and thus expected to provide similar advantages in the field of causal inference. As participants in the 2016 and 2017 Atlantic Causal Inference Conference (ACIC) data competitions and co-organizers of the 2018 competition, we discuss the strengths of simulation-based competitions and suggest potential extensions to address their limitations. These suggested augmentations aim at making the data generating processes more realistic and gradually increase in complexity, allowing thorough investigations of algorithms’ performance. We further outline a community-wide competition framework to evaluate an end-to-end causal inference pipeline, beginning with a causal question and a database, and ending with causal estimates.

The reaction on social media has been fierce.

And they're on sale for Black Friday. 

This retro style is making a comeback for a reason.

And they’re available in 19 fun prints.

Peter Klivenyi
Jan 1, 2000; 20:1-7
Cellular

Some of the troubling reports of corporate responses to COVID-19 include forced work in unsafe areas, not enough -- or any -- protection gear, massive layoffs and furloughs, and the sense that a critical mass of well-paid CEOs and politicians don't get that many people live paycheck to paycheck. There are exceptions though, and HP stands out, thanks to HR VP Tracy Keogh and CEO Enrique Lores.

Merchants soon will be able to sell products on Google Shopping at no charge. Previously, they had to pay per click, but the cost was not fixed. There was no minimum, but they had to set a maximum for ad spend and Google would stop displaying their ads once the maximum was reached. Starting next week, search results on the Google Shopping tab will consist primarily of free product listings.

As FAO launches dedicated webpages on post-2015, here are seven things to know about the process and how FAO is playing its part. 7 - Post-2015 development agenda - The name refers to the process through which Member States agree on a new global development framework to succeed the Millennium Development Goals (MDGs), eight goals that followed the UN Millennium Declaration [...]

Biodiversity for food and agriculture is among the earth’s most important resources. Biodiversity is indispensable: be it the insects that pollinate plants, the microscopic bacteria used for making cheese, the diverse livestock breeds used to make a living in harsh environments, the thousands species of fish, and other aquatic species in our lakes, rivers and oceans, or the thousands of [...]

Feeding the world’s growing population, which is expected to go beyond 9 billion by 2050, is one of the world’s biggest challenges. Some of the highest rates of population growth are predicted to occur in areas that are highly dependent on the agriculture sector (i.e. crops, livestock, forests and fisheries). Sustainable agricultural growth is one of the most effective means [...]

We see them each and every day: at the grocery store, the farmer’s market and as side orders served with your favourite dish. In many countries, they are part of the cultural heritage and are consumed on a regular basis. In other parts of the world, they hardly garner a mention except when served as soup on a cold winter’s [...]

Banana split, banana muffins, banana bread, banana pudding, banana pancakes – whether plain, cooked, baked or fried, bananas are among the most widely consumed fruits on the planet. However, how much do we really know about this most produced and exported fruit? Here are 11 interesting facts you should know about bananas: Based on written references discovered in Sanskrit around the year [...]

FAO plays an important and unique role as a neutral forum, offering unbiased, high-quality information across all areas related to food, agriculture and sustainable natural resources management. With over 500 new publications a year, FAO provides robust technical knowledge and global statistics. By broadly disseminating timely, accurate and compelling information, FAO informs the work of practitioners, researchers and policy-makers, while raising [...]

The cats also cross the road an average of 4.5 times in six days, putting themselves in danger

But will that keep susceptible populations safe?

Traces of lead pollution frozen in a glacier confirm that British lead production waned just before the death of Thomas Becket

Select Andrew Lloyd Webber productions will stream on YouTube for 48 hours at a time

Though not as plentiful as the Perseids in summer, the Lyrids can serve up some serious fireballs

We’re really, really sorry

The snazzy search is part of the telescope’s 30th anniversary celebration

How do you select one image to represent half a century of photography and art? You don’t

“Care Package” showcases Asian American and Pacific Islander artists, writers and scholars as sources of solace during the Covid-19 pandemic

Taken during the Banpo fountain light show

This praying mantis looks completely shocked and like it was caught doing something wrong. I had to laugh while taking this shot, as mantis seem to be very expressive sometimes.

In the hours after disaster struck Anchorage, an unexpected figure named Genie Chance came to the rescue