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

Fangzheng Xie, Yanxun Xu.

Source: Bayesian Analysis, Volume 15, Number 1, 159--186.

Abstract:
We propose a kernel mixture of polynomials prior for Bayesian nonparametric regression. The regression function is modeled by local averages of polynomials with kernel mixture weights. We obtain the minimax-optimal contraction rate of the full posterior distribution up to a logarithmic factor by estimating metric entropies of certain function classes. Under the assumption that the degree of the polynomials is larger than the unknown smoothness level of the true function, the posterior contraction behavior can adapt to this smoothness level provided an upper bound is known. We also provide a frequentist sieve maximum likelihood estimator with a near-optimal convergence rate. We further investigate the application of the kernel mixture of polynomials to partial linear models and obtain both the near-optimal rate of contraction for the nonparametric component and the Bernstein-von Mises limit (i.e., asymptotic normality) of the parametric component. The proposed method is illustrated with numerical examples and shows superior performance in terms of computational efficiency, accuracy, and uncertainty quantification compared to the local polynomial regression, DiceKriging, and the robust Gaussian stochastic process.

Muteb Alharthi, Theodore Kypraios, Philip D. O’Neill.

Source: Bayesian Analysis, Volume 14, Number 3, 927--956.

Abstract:
We consider the problem of model choice for stochastic epidemic models given partial observation of a disease outbreak through time. Our main focus is on the use of Bayes factors. Although Bayes factors have appeared in the epidemic modelling literature before, they can be hard to compute and little attention has been given to fundamental questions concerning their utility. In this paper we derive analytic expressions for Bayes factors given complete observation through time, which suggest practical guidelines for model choice problems. We adapt the power posterior method for computing Bayes factors so as to account for missing data and apply this approach to partially observed epidemics. For comparison, we also explore the use of a deviance information criterion for missing data scenarios. The methods are illustrated via examples involving both simulated and real data.

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.

Jerald F. Lawless.

Source: Statistical Science, Volume 34, Number 4, 569--571.

Abstract:
I comment on the papers Models as Approximations I and II, by A. Buja, R. Berk, L. Brown, E. George, E. Pitkin, M. Traskin, L. Zhao and K. Zhang.

Nicole Bohme Carnegie.

Source: Statistical Science, Volume 34, Number 1, 90--93.

Abstract:
With a thorough exposition of the methods and results of the 2016 Atlantic Causal Inference Competition, Dorie et al. have set a new standard for reproducibility and comparability of evaluations of causal inference methods. In particular, the open-source R package aciccomp2016, which permits reproduction of all datasets used in the competition, will be an invaluable resource for evaluation of future methodological developments. Building upon results from Dorie et al., we examine whether a set of potential modifications to Bayesian Additive Regression Trees (BART)—multiple chains in model fitting, using the propensity score as a covariate, targeted maximum likelihood estimation (TMLE), and computing symmetric confidence intervals—have a stronger impact on bias, RMSE, and confidence interval coverage in combination than they do alone. We find that bias in the estimate of SATT is minimal, regardless of the BART formulation. For purposes of CI coverage, however, all proposed modifications are beneficial—alone and in combination—but use of TMLE is least beneficial for coverage and results in considerably wider confidence intervals.

London (33 Stillness Road, London SE23 1NG) : Cleanair, Campaign for a Smoke-free Environment, [1989?]

London : Cleanair, Smoke-free Environment (33 Stillness Rd, London, SE23 1NG), [198-?]

London : Cleanair (33 Stillness Rd, London, SE23 1NG), [1989?]

London : Cleanair (33 Stillness Rd, London, SE23 1NG), [198-?]

London : Cleanair (33 Stillness Rd, London, SE23 1NG), [198-?]

London : Cleanair (33 Stillness Rd, London, SE23 1NG), [198-?]

London : Cleanair (33 Stillness Rd, London, SE23 1NG), [198-?]

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More than any other factor, it is our asset that determines our adversary. For most of us, our asset is the corpus of sensitive personal details used for online transactions. This all comes down to how much data an adversary can glean from you, and how thoroughly it can analyze it. If your data passes through some software or hardware, its developer or maintainer enjoys some measure of control.

Like conventional criminals, criminal hackers choose easy, lucrative targets. One group in the crosshairs is made up of companies that have data on millions of users, such as private sector entities with a Web presence. Why go after one user at a time when data is collected in one place? Criminal hackers also like to hunt small organizations that have modest capital but weak information security.

On-the-record remarks of the September 2019 Quarterly Review media briefing by Mr Claudio Borio, Head of the Monetary and Economic Department, and Mr Hyun Song Shin, Economic Adviser and Head of Research, 20 September 2019.

On-the-record remarks of the December 2019 Quarterly Review media briefing by Mr Claudio Borio, Head of the Monetary and Economic Department, and Mr Hyun Song Shin, Economic Adviser and Head of Research, 6 December 2019.

On-the-record remarks of the March 2020 Quarterly Review media briefing by Mr Claudio Borio, Head of the Monetary and Economic Department, and Mr Hyun Song Shin, Economic Adviser and Head of Research, 28 February 2020.

The United Nations launched the 2014 International Year of Family Farming to stress the vast potential family farmers have to eradicate hunger and preserve natural resources. In both developed and developing countries, more than 500 million, or nine out of ten, farms are managed by families, making family farms the predominant form of agriculture. They not only produce about 80% [...]

Last year, Ashmita Thapa’s husband left their hometown in southern Nepal to find work in Saudi Arabia. He had been working as a farmer and used to be able to grow enough food for the family. But now, Ashmita explains, the yields are poorer and poorer. “This is a part of climate change,” she adds. “There isn’t as much rain as [...]

Four years into the 2030 Agenda, there is still a large gap in data to understand where the world stands in achieving its shared goals, the SDGs. To support [...]

11 and 18 December 2019, Rome –A Farmer’s Market at FAO’s premises will take place on Wednesday, 11 December and on Wednesday 18 December 2019 from 12.00 [...]

The farmers will offer seasonal fresh fruits and vegetables to around 3000 people - including employees, contractors, delegates and visitors - that enter the FAO headquarters every day.

Centro Agroalimentare [...]

As of the start of the New Year, the Farmers’ Market will be back at FAO’s premises – Atrium - on January 29^th  from 12.00- 16.00 hours.

All of [...]

Buy fresh and seasonal produce at the Farmers’ Market on
Wednesday 26 February from 12.00 – 16.00 hours, and be sure to visit the [...]