National Association of Wine Retailers Release Letter Delivered to Attorneys General and Alcohol Regulatory Chiefs Concerning Unconstitutional and Unenforceable Wine Shipping Bans

(PRWeb April 15, 2020)

Read the full story at https://www.prweb.com/releases/wine_retailers_seek_alcohol_shipping_compromise_with_18_states/prweb17050617.htm

Xinran Li, Peng Ding, Donald B. Rubin.

Source: The Annals of Statistics, Volume 48, Number 1, 43--63.

Abstract:
With many pretreatment covariates and treatment factors, the classical factorial experiment often fails to balance covariates across multiple factorial effects simultaneously. Therefore, it is intuitive to restrict the randomization of the treatment factors to satisfy certain covariate balance criteria, possibly conforming to the tiers of factorial effects and covariates based on their relative importances. This is rerandomization in factorial experiments. We study the asymptotic properties of this experimental design under the randomization inference framework without imposing any distributional or modeling assumptions of the covariates and outcomes. We derive the joint asymptotic sampling distribution of the usual estimators of the factorial effects, and show that it is symmetric, unimodal and more “concentrated” at the true factorial effects under rerandomization than under the classical factorial experiment. We quantify this advantage of rerandomization using the notions of “central convex unimodality” and “peakedness” of the joint asymptotic sampling distribution. We also construct conservative large-sample confidence sets for the factorial effects.

Xiaohui Chen, Kengo Kato.

Source: The Annals of Statistics, Volume 47, Number 6, 3127--3156.

Abstract:
This paper studies inference for the mean vector of a high-dimensional $U$-statistic. In the era of big data, the dimension $d$ of the $U$-statistic and the sample size $n$ of the observations tend to be both large, and the computation of the $U$-statistic is prohibitively demanding. Data-dependent inferential procedures such as the empirical bootstrap for $U$-statistics is even more computationally expensive. To overcome such a computational bottleneck, incomplete $U$-statistics obtained by sampling fewer terms of the $U$-statistic are attractive alternatives. In this paper, we introduce randomized incomplete $U$-statistics with sparse weights whose computational cost can be made independent of the order of the $U$-statistic. We derive nonasymptotic Gaussian approximation error bounds for the randomized incomplete $U$-statistics in high dimensions, namely in cases where the dimension $d$ is possibly much larger than the sample size $n$, for both nondegenerate and degenerate kernels. In addition, we propose generic bootstrap methods for the incomplete $U$-statistics that are computationally much less demanding than existing bootstrap methods, and establish finite sample validity of the proposed bootstrap methods. Our methods are illustrated on the application to nonparametric testing for the pairwise independence of a high-dimensional random vector under weaker assumptions than those appearing in the literature.

Bryan D. Martin, Daniela Witten, Amy D. Willis.

Source: The Annals of Applied Statistics, Volume 14, Number 1, 94--115.

Abstract:
Using a sample from a population to estimate the proportion of the population with a certain category label is a broadly important problem. In the context of microbiome studies, this problem arises when researchers wish to use a sample from a population of microbes to estimate the population proportion of a particular taxon, known as the taxon’s relative abundance . In this paper, we propose a beta-binomial model for this task. Like existing models, our model allows for a taxon’s relative abundance to be associated with covariates of interest. However, unlike existing models, our proposal also allows for the overdispersion in the taxon’s counts to be associated with covariates of interest. We exploit this model in order to propose tests not only for differential relative abundance, but also for differential variability. The latter is particularly valuable in light of speculation that dysbiosis , the perturbation from a normal microbiome that can occur in certain disease conditions, may manifest as a loss of stability, or increase in variability, of the counts associated with each taxon. We demonstrate the performance of our proposed model using a simulation study and an application to soil microbial data.

Li Zhu, Zhiguang Huo, Tianzhou Ma, Steffi Oesterreich, George C. Tseng.

Source: The Annals of Applied Statistics, Volume 13, Number 4, 2611--2636.

Abstract:
Variable selection is a pervasive problem in modern high-dimensional data analysis where the number of features often exceeds the sample size (a.k.a. small-n-large-p problem). Incorporation of group structure knowledge to improve variable selection has been widely studied. Here, we consider prior knowledge of a hierarchical overlapping group structure to improve variable selection in regression setting. In genomics applications, for instance, a biological pathway contains tens to hundreds of genes and a gene can be mapped to multiple experimentally measured features (such as its mRNA expression, copy number variation and methylation levels of possibly multiple sites). In addition to the hierarchical structure, the groups at the same level may overlap (e.g., two pathways can share common genes). Incorporating such hierarchical overlapping groups in traditional penalized regression setting remains a difficult optimization problem. Alternatively, we propose a Bayesian indicator model that can elegantly serve the purpose. We evaluate the model in simulations and two breast cancer examples, and demonstrate its superior performance over existing models. The result not only enhances prediction accuracy but also improves variable selection and model interpretation that lead to deeper biological insight of the disease.

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.

Youyi Zhang, Jeffrey S. Morris, Shivali Narang Aerry, Arvind U. K. Rao, Veerabhadran Baladandayuthapani.

Source: The Annals of Applied Statistics, Volume 13, Number 3, 1957--1988.

Abstract:
Technological innovations have produced large multi-modal datasets that include imaging and multi-platform genomics data. Integrative analyses of such data have the potential to reveal important biological and clinical insights into complex diseases like cancer. In this paper, we present Bayesian approaches for integrative analysis of radiological imaging and multi-platform genomic data, where-in our goals are to simultaneously identify genomic and radiomic, that is, radiology-based imaging markers, along with the latent associations between these two modalities, and to detect the overall prognostic relevance of the combined markers. For this task, we propose Radio-iBAG: Radiomics-based Integrative Bayesian Analysis of Multiplatform Genomic Data , a multi-scale Bayesian hierarchical model that involves several innovative strategies: it incorporates integrative analysis of multi-platform genomic data sets to capture fundamental biological relationships; explores the associations between radiomic markers accompanying genomic information with clinical outcomes; and detects genomic and radiomic markers associated with clinical prognosis. We also introduce the use of sparse Principal Component Analysis (sPCA) to extract a sparse set of approximately orthogonal meta-features each containing information from a set of related individual radiomic features, reducing dimensionality and combining like features. Our methods are motivated by and applied to The Cancer Genome Atlas glioblastoma multiforme data set, where-in we integrate magnetic resonance imaging-based biomarkers along with genomic, epigenomic and transcriptomic data. Our model identifies important magnetic resonance imaging features and the associated genomic platforms that are related with patient survival times.

Jeffrey Regier, Andrew C. Miller, David Schlegel, Ryan P. Adams, Jon D. McAuliffe, Prabhat.

Source: The Annals of Applied Statistics, Volume 13, Number 3, 1884--1926.

Abstract:
We present a new, fully generative model for constructing astronomical catalogs from optical telescope image sets. Each pixel intensity is treated as a random variable with parameters that depend on the latent properties of stars and galaxies. These latent properties are themselves modeled as random. We compare two procedures for posterior inference. One procedure is based on Markov chain Monte Carlo (MCMC) while the other is based on variational inference (VI). The MCMC procedure excels at quantifying uncertainty, while the VI procedure is 1000 times faster. On a supercomputer, the VI procedure efficiently uses 665,000 CPU cores to construct an astronomical catalog from 50 terabytes of images in 14.6 minutes, demonstrating the scaling characteristics necessary to construct catalogs for upcoming astronomical surveys.

Jesús D. Arroyo Relión, Daniel Kessler, Elizaveta Levina, Stephan F. Taylor.

Source: The Annals of Applied Statistics, Volume 13, Number 3, 1648--1677.

Abstract:
While statistical analysis of a single network has received a lot of attention in recent years, with a focus on social networks, analysis of a sample of networks presents its own challenges which require a different set of analytic tools. Here we study the problem of classification of networks with labeled nodes, motivated by applications in neuroimaging. Brain networks are constructed from imaging data to represent functional connectivity between regions of the brain, and previous work has shown the potential of such networks to distinguish between various brain disorders, giving rise to a network classification problem. Existing approaches tend to either treat all edge weights as a long vector, ignoring the network structure, or focus on graph topology as represented by summary measures while ignoring the edge weights. Our goal is to design a classification method that uses both the individual edge information and the network structure of the data in a computationally efficient way, and that can produce a parsimonious and interpretable representation of differences in brain connectivity patterns between classes. We propose a graph classification method that uses edge weights as predictors but incorporates the network nature of the data via penalties that promote sparsity in the number of nodes, in addition to the usual sparsity penalties that encourage selection of edges. We implement the method via efficient convex optimization and provide a detailed analysis of data from two fMRI studies of schizophrenia.

Members of the future American workforce could see losses of earnings that add up to trillions of dollars, depending on how long coronavirus-related school closures persist.

The post Economists Expect Huge Future Earnings Loss for Students Missing School Due to COVID-19 appeared first on Market Brief.

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.

Brian Neelon.

Source: Bayesian Analysis, Volume 14, Number 3, 849--875.

Abstract:
Motivated by a study examining spatiotemporal patterns in inpatient hospitalizations, we propose an efficient Bayesian approach for fitting zero-inflated negative binomial models. To facilitate posterior sampling, we introduce a set of latent variables that are represented as scale mixtures of normals, where the precision terms follow independent Pólya-Gamma distributions. Conditional on the latent variables, inference proceeds via straightforward Gibbs sampling. For fixed-effects models, our approach is comparable to existing methods. However, our model can accommodate more complex data structures, including multivariate and spatiotemporal data, settings in which current approaches often fail due to computational challenges. Using simulation studies, we highlight key features of the method and compare its performance to other estimation procedures. We apply the approach to a spatiotemporal analysis examining the number of annual inpatient admissions among United States veterans with type 2 diabetes.

Jennifer L. Zamanian
May 2, 2012; 32:6391-6410
Neurobiology of Disease

Vinod Venkatraman
Mar 9, 2011; 31:3712-3718
BehavioralSystemsCognitive

Erin E. Hecht
Sep 25, 2019; 39:7748-7758
BehavioralSystemsCognitive

Italian translation of Annual Economic Report 2018 of the BIS, June 2018 - Le autorità possono fare in modo che l'attuale ripresa economica si mantenga oltre il breve termine avviando riforme strutturali, ridando margine di manovra alle politiche monetarie e di bilancio per affrontare eventuali future minacce, e incoraggiando la pronta attuazione delle riforme regolamentari, scrive la Banca dei Regolamenti Internazionali (BRI) nella sua Relazione economica annuale.

Italian translation of the BIS press release on the presentation of the Annual Economic Report 2019, 30 June 2019.

French translation of the press release about the Basel Committee publishing a discussion paper on "The regulatory treatment of sovereign exposures" (7 December 2017)

French translation of the Annual Economic Report 2018 of the BIS, June 2018 - Les responsables des politiques publiques peuvent prolonger la phase de croissance actuelle en engageant des réformes structurelles, en restaurant les marges de manœuvre monétaires et budgétaires pour faire face aux menaces futures et en encourageant une mise en œuvre rapide des réformes réglementaires, indique la Banque des Règlements Internationaux (BRI) dans son Rapport économique annuel.

French version of Press release about Pablo Hernández de Cos appointed as Chairman of Basel Committee on Banking Supervision

French translation of the BIS press release on the presentation of the Annual Economic Report 2019, 30 June 2019. La politique monétaire ne peut plus être le principal moteur de la croissance économique, et d'autres leviers de politique publique doivent être actionnés pour faire en sorte que l'économie mondiale connaisse une dynamique durable ...

Spanish version of Press release about new members joining the Committee on Payments and Market Infrastructures (19 March 2018)

Spanish translation of press release - the Basel Committee on Banking Supervision is finalising stress-testing principles, reviews ways to stop regulatory arbitrage behaviour, agrees on annual G-SIB list, discusses leverage ratio, crypto-assets, market risk framework and implementation, 20 September 2018.

Spanish version of Press release about Pablo Hernández de Cos appointed as Chairman of Basel Committee on Banking Supervision, 7 March 2019. Pablo Hernández de Cos, nombrado Presidente del Comité de Supervisión Bancaria de Basilea.

Paper by Mr Luiz Awazu Pereira da Silva, Deputy General Manager of the BIS, Enisse Kharroubi, Emanuel Kohlscheen and Benoît Mojon based on remarks at the University of Basel, 5 May 2019.

Exiting low inflation traps by "consensus": nominal wages and price stability - Speech by Luiz A Pereira da Silva and Benoît Mojon, based on the keynote speech at the Eighth High-level Policy Dialogue between the Eurosystem and Latin American Central Banks, Cartagena de Indias, Colombia, 28-29 November 2019.

Hepatocyte growth factor (HGF) is a multifunctional protein that signals through the MET receptor. HGF stimulates cell proliferation, cell dispersion, neuronal survival, and wound healing. In the inner ear, levels of HGF must be fine-tuned for normal hearing. In mice, a deficiency of HGF expression limited to the auditory system, or an overexpression of HGF, causes neurosensory deafness. In humans, noncoding variants in HGF are associated with nonsyndromic deafness DFNB39. However, the mechanism by which these noncoding variants causes deafness was unknown. Here, we reveal the cause of this deafness using a mouse model engineered with a noncoding intronic 10 bp deletion (del10) in Hgf. Male and female mice homozygous for del10 exhibit moderate-to-profound hearing loss at 4 weeks of age as measured by tone burst auditory brainstem responses. The wild type (WT) 80 mV endocochlear potential was significantly reduced in homozygous del10 mice compared with WT littermates. In normal cochlea, endocochlear potentials are dependent on ion homeostasis mediated by the stria vascularis (SV). Previous studies showed that developmental incorporation of neural crest cells into the SV depends on signaling from HGF/MET. We show by immunohistochemistry that, in del10 homozygotes, neural crest cells fail to infiltrate the developing SV intermediate layer. Phenotyping and RNAseq analyses reveal no other significant abnormalities in other tissues. We conclude that, in the inner ear, the noncoding del10 mutation in Hgf leads to developmental defects of the SV and consequently dysfunctional ion homeostasis and a reduction in the EP, recapitulating human DFNB39 nonsyndromic deafness.

SIGNIFICANCE STATEMENT Hereditary deafness is a common, clinically and genetically heterogeneous neurosensory disorder. Previously, we reported that human deafness DFNB39 is associated with noncoding variants in the 3'UTR of a short isoform of HGF encoding hepatocyte growth factor. For normal hearing, HGF levels must be fine-tuned as an excess or deficiency of HGF cause deafness in mouse. Using a Hgf mutant mouse with a small 10 bp deletion recapitulating a human DFNB39 noncoding variant, we demonstrate that neural crest cells fail to migrate into the stria vascularis intermediate layer, resulting in a significantly reduced endocochlear potential, the driving force for sound transduction by inner ear hair cells. HGF-associated deafness is a neurocristopathy but, unlike many other neurocristopathies, it is not syndromic.

Despite their opposing actions on food intake, POMC and NPY/AgRP neurons in the arcuate nucleus of the hypothalamus (ARH) are derived from the same progenitors that give rise to ARH neurons. However, the mechanism whereby common neuronal precursors subsequently adopt either the anorexigenic (POMC) or the orexigenic (NPY/AgRP) identity remains elusive. We hypothesize that POMC and NPY/AgRP cell fates are specified and maintained by distinct intrinsic factors. In search of them, we profiled the transcriptomes of developing POMC and NPY/AgRP neurons in mice. Moreover, cell-type-specific transcriptomic analyses revealed transcription regulators that are selectively enriched in either population, but whose developmental functions are unknown in these neurons. Among them, we found the expression of the PR domain-containing factor 12 (Prdm12) was enriched in POMC neurons but absent in NPY/AgRP neurons. To study the role of Prdm12 in vivo, we developed and characterized a floxed Prdm12 allele. Selective ablation of Prdm12 in embryonic POMC neurons led to significantly reduced Pomc expression as well as early-onset obesity in mice of either sex that recapitulates symptoms of human POMC deficiency. Interestingly, however, specific deletion of Prdm12 in adult POMC neurons showed that it is no longer required for Pomc expression or energy balance. Collectively, these findings establish a critical role for Prdm12 in the anorexigenic neuron identity and suggest that it acts developmentally to program body weight homeostasis. Finally, the combination of cell-type-specific genomic and genetic analyses provides a means to dissect cellular and functional diversity in the hypothalamus whose neurodevelopment remains poorly studied.

SIGNIFICANCE STATEMENT POMC and NPY/AgRP neurons are derived from the same hypothalamic progenitors but have opposing effects on food intake. We profiled the transcriptomes of genetically labeled POMC and NPY/AgRP neurons in the developing mouse hypothalamus to decipher the transcriptional codes behind the versus orexigenic neuron identity. Our analyses revealed 29 transcription regulators that are selectively enriched in one of the two populations. We generated new mouse genetic models to selective ablate one of POMC-neuron enriched transcription factors Prdm12 in developing and adult POMC neurons. Our studies establish a previously unrecognized role for Prdm12 in the anorexigenic neuron identity and suggest that it acts developmentally to program body weight homeostasis.

Food waste has become a dangerous habit: buying more than we need at supermarkets, letting fruits and vegetables spoil at home or ordering more than we can eat at restaurants.  Each year, about 1/3 of the food we produce globally is lost or wasted. In developing countries, a large part of this food (40%) is lost at the harvest or processing [...]

The pint-sized, mustachioed Gaul immortalized in the French cartoon has spawned films, a theme park and many other spin-offs

Over his five-decade-plus career, the "Strega Nona" author contributed to more than 270 books

The opossum-sized mammal lived in Madagascar at the end of the age of the dinosaurs

The dominant bear in the swamp is fending off another challenge to his authority by an intruder. It's a clash of titans with mating rights on the line.

If New Brunswickers were planning on doing any yard work this weekend they will likely be using their snow shovels instead of their lawn rakes. 

Business owners throughout northern Ontario will have the chance Friday morning to speak directly with federal Economic Development Minister Mélanie Joly.

BIS Bulletin No 7, April 2020. Past epidemics had long-lasting effects on economies through illness and the loss of lives, while Covid-19 is marked by widespread containment measures and relatively lower fatalities among young people. The short-term costs of Covid-19 will probably dwarf those of past epidemics, due to the unprecedented and synchronised global sudden stop in economic activity induced by containment measures. The current estimated impact on global GDP growth for 2020 is around -4%, with substantial downside risks if containment policies are prolonged. Output losses are larger for major economies.

Will a nationalist president be undone by his underreaction to a foreign threat?

Global real residential property prices rose 1.4% year/year in aggregate in Q3 2019, reflecting subdued developments both in advanced (+ 1.5%) and emerging market economies (+ 1.3%).

Long-term interest rates have remained low despite a surge in the issuance of sovereign debt to mitigate the economic impact of the pandemic.

Gold and silver prices often move in tandem, but the gap between them widened by 31% January through April in line with a trend that began nine years ago.

Address by Mr Philip Lowe, Governor of the Reserve Bank of Australia, at the Reserve Bank of Australia, Sydney, 21 April 2020.

Japan’s Yuzuru Hanyu believes he was born to figure skate, but that doesn’t mean he’s immune to pressure and expectations.

What do we make of a difficult economic situation new to everyone? CME Group's Chief Economist addressed these and other questions around the economics of the pandemic.

The post An Economist Answers Students’ Questions About the Economics of COVID-19 appeared first on OpenMarkets.