[Place of publication not identified] : [publisher not identified], [18--?]

[Place of publication not identified] : [publisher not identified], [18--?]

[Place of publication not identified] : [publisher not identified], [18--?]

Stuttgart : Franz Steiner Verlag, 2019.

Washington, D.C. : Department of Transportation, Federal Railroad Administration, 1976.

Sylvia Frühwirth-Schnatter.

Source: Brazilian Journal of Probability and Statistics, Volume 33, Number 4, 706--733.

Abstract:
Finite mixture models and their extensions to Markov mixture and mixture of experts models are very popular in analysing data of various kind. A challenge for these models is choosing the number of components based on marginal likelihoods. The present paper suggests two innovative, generic bridge sampling estimators of the marginal likelihood that are based on constructing balanced importance densities from the conditional densities arising during Gibbs sampling. The full permutation bridge sampling estimator is derived from considering all possible permutations of the mixture labels for a subset of these densities. For the double random permutation bridge sampling estimator, two levels of random permutations are applied, first to permute the labels of the MCMC draws and second to randomly permute the labels of the conditional densities arising during Gibbs sampling. Various applications show very good performance of these estimators in comparison to importance and to reciprocal importance sampling estimators derived from the same importance densities.

Salem M. Al-Gezeri, Robert G. Aykroyd.

Source: Brazilian Journal of Probability and Statistics, Volume 33, Number 3, 498--519.

Abstract:
The use of Markov random field (MRF) models has proven to be a fruitful approach in a wide range of image processing applications. It allows local texture information to be incorporated in a systematic and unified way and allows statistical inference theory to be applied giving rise to novel output summaries and enhanced image interpretation. A great advantage of such low-level approaches is that they lead to flexible models, which can be applied to a wide range of imaging problems without the need for significant modification. This paper proposes and explores the use of conditional MRF models for situations where multiple images are to be processed simultaneously, or where only a single image is to be reconstructed and a sequential approach is taken. Although the coupling of image intensity values is a special case of our approach, the main extension over previous proposals is to allow the direct coupling of other properties, such as smoothness or texture. This is achieved using a local modulating function which adjusts the influence of global smoothing without the need for a fully inhomogeneous prior model. Several modulating functions are considered and a detailed simulation study, motivated by remote sensing applications in archaeological geophysics, of conditional reconstruction is presented. The results demonstrate that a substantial improvement in the quality of the image reconstruction, in terms of errors and residuals, can be achieved using this approach, especially at locations with rapid changes in the underlying intensity.

Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) widely used for visualizing diseased cardiac structures, is a crucial first step for clinical diagnosis and treatment. However, direct segmentation of LGE-MRIs is challenging due to its attenuated contrast. Since most clinical studies have relied on manual and labor-intensive approaches, automatic methods are of high interest, particularly optimized machine learning approaches. To address this, we organized the "2018 Left Atrium Segmentation Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset, and associated labels of the left atrium segmented by three medical experts, ultimately attracting the participation of 27 international teams. In this paper, extensive analysis of the submitted algorithms using technical and biological metrics was performed by undergoing subgroup analysis and conducting hyper-parameter analysis, offering an overall picture of the major design choices of convolutional neural networks (CNNs) and practical considerations for achieving state-of-the-art left atrium segmentation. Results show the top method achieved a dice score of 93.2% and a mean surface to a surface distance of 0.7 mm, significantly outperforming prior state-of-the-art. Particularly, our analysis demonstrated that double, sequentially used CNNs, in which a first CNN is used for automatic region-of-interest localization and a subsequent CNN is used for refined regional segmentation, achieved far superior results than traditional methods and pipelines containing single CNNs. This large-scale benchmarking study makes a significant step towards much-improved segmentation methods for cardiac LGE-MRIs, and will serve as an important benchmark for evaluating and comparing the future works in the field.

We propose a novel estimation procedure for scale-by-scale lead-lag relationships of financial assets observed at high-frequency in a non-synchronous manner. The proposed estimation procedure does not require any interpolation processing of original datasets and is applicable to those with highest time resolution available. Consistency of the proposed estimators is shown under the continuous-time framework that has been developed in our previous work Hayashi and Koike (2018). An empirical application to a quote dataset of the NASDAQ-100 assets identifies two types of lead-lag relationships at different time scales.

Søren Wengel Mogensen, Niels Richard Hansen.

Source: The Annals of Statistics, Volume 48, Number 1, 539--559.

Abstract:
Symmetric independence relations are often studied using graphical representations. Ancestral graphs or acyclic directed mixed graphs with $m$-separation provide classes of symmetric graphical independence models that are closed under marginalization. Asymmetric independence relations appear naturally for multivariate stochastic processes, for instance, in terms of local independence. However, no class of graphs representing such asymmetric independence relations, which is also closed under marginalization, has been developed. We develop the theory of directed mixed graphs with $mu $-separation and show that this provides a graphical independence model class which is closed under marginalization and which generalizes previously considered graphical representations of local independence. Several graphs may encode the same set of independence relations and this means that in many cases only an equivalence class of graphs can be identified from observational data. For statistical applications, it is therefore pivotal to characterize graphs that induce the same independence relations. Our main result is that for directed mixed graphs with $mu $-separation each equivalence class contains a maximal element which can be constructed from the independence relations alone. Moreover, we introduce the directed mixed equivalence graph as the maximal graph with dashed and solid edges. This graph encodes all information about the edges that is identifiable from the independence relations, and furthermore it can be computed efficiently from the maximal graph.

Stephen Berg, Jun Zhu, Murray K. Clayton, Monika E. Shea, David J. Mladenoff.

Source: The Annals of Applied Statistics, Volume 13, Number 4, 2312--2340.

Abstract:
The Wisconsin Public Land Survey database describes historical forest composition at high spatial resolution and is of interest in ecological studies of forest composition in Wisconsin just prior to significant Euro-American settlement. For such studies it is useful to identify recurring subpopulations of tree species known as communities, but standard clustering approaches for subpopulation identification do not account for dependence between spatially nearby observations. Here, we develop and fit a latent discrete Markov random field model for the purpose of identifying and classifying historical forest communities based on spatially referenced multivariate tree species counts across Wisconsin. We show empirically for the actual dataset and through simulation that our latent Markov random field modeling approach improves prediction and parameter estimation performance. For model fitting we introduce a new stochastic approximation algorithm which enables computationally efficient estimation and classification of large amounts of spatial multivariate count data.

Gabriela V. Cohen Freue, David Kepplinger, Matías Salibián-Barrera, Ezequiel Smucler.

Source: The Annals of Applied Statistics, Volume 13, Number 4, 2065--2090.

Abstract:
In large-scale quantitative proteomic studies, scientists measure the abundance of thousands of proteins from the human proteome in search of novel biomarkers for a given disease. Penalized regression estimators can be used to identify potential biomarkers among a large set of molecular features measured. Yet, the performance and statistical properties of these estimators depend on the loss and penalty functions used to define them. Motivated by a real plasma proteomic biomarkers study, we propose a new class of penalized robust estimators based on the elastic net penalty, which can be tuned to keep groups of correlated variables together in the selected model and maintain robustness against possible outliers. We also propose an efficient algorithm to compute our robust penalized estimators and derive a data-driven method to select the penalty term. Our robust penalized estimators have very good robustness properties and are also consistent under certain regularity conditions. Numerical results show that our robust estimators compare favorably to other robust penalized estimators. Using our proposed methodology for the analysis of the proteomics data, we identify new potentially relevant biomarkers of cardiac allograft vasculopathy that are not found with nonrobust alternatives. The selected model is validated in a new set of 52 test samples and achieves an area under the receiver operating characteristic (AUC) of 0.85.

Qiwei Li, Xinlei Wang, Faming Liang, Guanghua Xiao.

Source: The Annals of Applied Statistics, Volume 13, Number 3, 1708--1732.

Abstract:
With the advance of imaging technology, digital pathology imaging of tumor tissue slides is becoming a routine clinical procedure for cancer diagnosis. This process produces massive imaging data that capture histological details in high resolution. Recent developments in deep-learning methods have enabled us to identify and classify individual cells from digital pathology images at large scale. Reliable statistical approaches to model the spatial pattern of cells can provide new insight into tumor progression and shed light on the biological mechanisms of cancer. We consider the problem of modeling spatial correlations among three commonly seen cells observed in tumor pathology images. A novel geostatistical marking model with interpretable underlying parameters is proposed in a Bayesian framework. We use auxiliary variable MCMC algorithms to sample from the posterior distribution with an intractable normalizing constant. We demonstrate how this model-based analysis can lead to sharper inferences than ordinary exploratory analyses, by means of application to three benchmark datasets and a case study on the pathology images of $188$ lung cancer patients. The case study shows that the spatial correlation between tumor and stromal cells predicts patient prognosis. This statistical methodology not only presents a new model for characterizing spatial correlations in a multitype spatial point pattern conditioning on the locations of the points, but also provides a new perspective for understanding the role of cell–cell interactions in cancer progression.

Lekha Patel, Nils Gustafsson, Yu Lin, Raimund Ober, Ricardo Henriques, Edward Cohen.

Source: The Annals of Applied Statistics, Volume 13, Number 3, 1397--1429.

Abstract:
Fluorescing molecules (fluorophores) that stochastically switch between photon-emitting and dark states underpin some of the most celebrated advancements in super-resolution microscopy. While this stochastic behavior has been heavily exploited, full characterization of the underlying models can potentially drive forward further imaging methodologies. Under the assumption that fluorophores move between fluorescing and dark states as continuous time Markov processes, the goal is to use a sequence of images to select a model and estimate the transition rates. We use a hidden Markov model to relate the observed discrete time signal to the hidden continuous time process. With imaging involving several repeat exposures of the fluorophore, we show the observed signal depends on both the current and past states of the hidden process, producing emission probabilities that depend on the transition rate parameters to be estimated. To tackle this unusual coupling of the transition and emission probabilities, we conceive transmission (transition-emission) matrices that capture all dependencies of the model. We provide a scheme of computing these matrices and adapt the forward-backward algorithm to compute a likelihood which is readily optimized to provide rate estimates. When confronted with several model proposals, combining this procedure with the Bayesian Information Criterion provides accurate model selection.

Pasha Tkachov.

Source: Bernoulli, Volume 26, Number 2, 1354--1380.

Abstract:
The aim of this paper is to prove stability of traveling waves for integro-differential equations connected with branching Markov processes. In other words, the limiting law of the left-most particle of a (time-continuous) branching Markov process with a Lévy non-branching part is demonstrated. The key idea is to approximate the branching Markov process by a branching random walk and apply the result of Aïdékon [ Ann. Probab. 41 (2013) 1362–1426] on the limiting law of the latter one.

Elena Issoglio, Francesco Russo.

Source: Bernoulli, Volume 26, Number 1, 728--766.

Abstract:
In this paper, we investigate BSDEs where the driver contains a distributional term (in the sense of generalised functions) and derive general Feynman–Kac formulae related to these BSDEs. We introduce an integral operator to give sense to the equation and then we show the existence of a strong solution employing results on a related PDE. Due to the irregularity of the driver, the $Y$-component of a couple $(Y,Z)$ solving the BSDE is not necessarily a semimartingale but a weak Dirichlet process.

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).

L. F. South, A. N. Pettitt, C. C. Drovandi.

Source: Bayesian Analysis, Volume 14, Number 3, 773--796.

Abstract:
Sequential Monte Carlo (SMC) methods for sampling from the posterior of static Bayesian models are flexible, parallelisable and capable of handling complex targets. However, it is common practice to adopt a Markov chain Monte Carlo (MCMC) kernel with a multivariate normal random walk (RW) proposal in the move step, which can be both inefficient and detrimental for exploring challenging posterior distributions. We develop new SMC methods with independent proposals which allow recycling of all candidates generated in the SMC process and are embarrassingly parallelisable. A novel evidence estimator that is easily computed from the output of our independent SMC is proposed. Our independent proposals are constructed via flexible copula-type models calibrated with the population of SMC particles. We demonstrate through several examples that more precise estimates of posterior expectations and the marginal likelihood can be obtained using fewer likelihood evaluations than the more standard RW approach.

To view more press releases, please visit http://www.snl.com/irweblinkx/news.aspx?iid=1024452.

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.

E-learning was quite the buzzword a couple of decades ago – then when the internet started in earnest it became even more so. Today e-learning is mainstreamed in many organization, including FAO with more than 400 000 learners taking advantage of FAO’s offerings. FAO’s e-learning center offers free interactive courses – in English, French and Spanish - on topics ranging [...]

The terraced hills of the Andes, the rice paddies of southern China, the oasis systems of the Maghreb: agriculture molds landscapes and places. Agriculture also shapes livelihoods, lifestyles, food traditions and cultures. What kind of plants grow or can’t grow, how they are harvested and what people eat define people’s lives.  Because our natural resources are under great strain, we need [...]

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 [...]

The Farmers’ Market has been postponed for Friday 6 March and until further notice.

The remote cabin could be the difference between life and death for travellers stuck on the territory’s long winter road. It was built to remember Stella Barnaby, who would have been 55 this Saturday.

Amid widespread sell-offs in risky asset classes, corporate bond exchange-traded funds (ETFs) traded at steep discounts to underlying asset values in March. Contributing factors were high market volatility, reduced risk-taking by dealers and investors' reaction to policy decisions. Policy interventions that improve market functioning in a given sector can have temporary yet important spillovers to other segments through portfolio rebalancing by investors.

BIS Bulletin No 8, April 2020. Information on local labour markets and Google searches can be used to construct a measure of the vulnerability of employment in different regions of the United States to the Covid-19 shock. Regional exposure to Covid-19 varies significantly, ranging from a low of 2% to a high of 98% of total local employment. We test for the usefulness of the Covid-19 exposure measure by showing that areas with higher exposure report more Google search queries related to the pandemic and unemployment benefits.

Markets Committee calls for wider adoption of global code of conduct for foreign exchange markets (Press release, 30 January 2020)

No global real estate market despite higher price synchronisation and growing role of international investors, central banks find (Press release, 18 February 2020)

CPMI Press release "Jurisdictions move towards full implementation of standards for financial market infrastructures", 8 April 2020.

Jean-Talon market has changed during the COVID-19 pandemic. Today, there are controlled entrances, someone making sure you douse your hands with sanitizer and another with a clicker in hand, counting the number of people who enter.

The Metuchen Farmers Market, like many others, has moved to online orders and drive-thru pickups during the coronavirus pandemic

With the business world -- and the world at large, for that matter -- changing at what feels like a moment's notice, businesses and brands have never been required to be as limber as in this current moment. Marketing leaders want hard evidence and objective facts for decision making. It wasn't long ago that multi-touch attribution was the prized child of the hype cycle among marketers.

A look at support and regenerative policy initiatives by the Federal Reserve and the Government as more than 20 million jobs were lost in April 2020.

Introductory remarks by Mr Thomas Jordan, Chairman of the Governing Board of the Swiss National Bank, at the Media News Conference of the Swiss National Bank, Berne, 25 March 2020.

The NBA gave its approval for teams to reopen their practice facilities on a limited basis on Friday, but only three teams have confirmed they will. And Dallas Mavericks owner Mark Cuban is in no hurry for his team to join the list.

Netizens celebrate as Kamal Haasan crosses 6 million mark on Twitter  DNA India

At the onset of the coronavirus crisis, the Federal Reserve relied on repurchase agreements, or repos, as a large part...

The post OpenMarkets Weekly: The Importance of Repo Markets appeared first on OpenMarkets.

The volatility experienced this spring in the 7 trillion-dollar foreign exchange market was unprecedented. The JP Morgan G7 FX Volatility...

The post OpenMarkets Weekly: FX Volatility appeared first on OpenMarkets.

Uranium futures is one of the few contracts that has experienced price gains in recent weeks, as the economic impact...

The post Why Uranium Markets Are Gaining Interest appeared first on OpenMarkets.

The U.S. Department of the Treasury will issue significantly more debt than the market had expected in the wake of...

The post What The 20-Year Bond Means for Interest Rate Markets appeared first on OpenMarkets.

The Canada-wide chain will introduce a mandatory mask policy on May 11, claiming customers and employees want a policy more in line with how Asian countries have handled the COVID-19 crisis.

About half of the confirmed or presumptive cases of COVID-19 in Waterloo region have been marked as resolved, according to numbers released by Region of Waterloo Public Health on Saturday.

Farmers markets throughout B.C.’s Interior and South Coast are ramping up for their spring seasons, but COVID-19 has forced them to make some changes to how they operate. 

We develop a parsimonious model to study the effect of regulations aimed at reducing counterparty risk on the structure of over-the-counter securities markets. We find that such regulations promote entry of dealers, thus fostering competition and lowering spreads. Greater competition, however, has an indirect negative effect on market-making profitability.