Numerical convergence of the block-maxima approach to the Generalized Extreme Value distribution

In this paper we perform an analytical and numerical study of Extreme Value distributions in discrete dynamical systems. In this setting, recent works have shown how to get a statistics of extremes in agreement with the classical Extreme Value Theory. We pursue these investigations by giving analytical expressions of Extreme Value distribution parameters for maps that have an absolutely continuous invariant measure. We compare these analytical results with numerical experiments in which we study the convergence to limiting distributions using the so called block-maxima approach, pointing out in which cases we obtain robust estimation of parameters. In regular maps for which mixing properties do not hold, we show that the fitting procedure to the classical Extreme Value Distribution fails, as expected. However, we obtain an empirical distribution that can be explained starting from a different observable function for which Nicolis et al. [2006] have found analytical results.Comment: 34 pages, 7 figures; Journal of Statistical Physics 201

Whiteness and diasporic Irishness: nation, gender and class

Whiteness is often detached from the notion of diaspora in the recent flurry of interest in the phenomenon, yet it is a key feature of some of the largest and oldest displacements. This paper explores the specific contexts of white racial belonging and status over two centuries in two main destinations of the Irish diaspora, the USA and Britain. Its major contribution is a tracing of the untold story of ‘How the Irish became white in Britain’ to parallel and contrast with the much more fully developed narrative in the USA. It argues that, contrary to popular belief, the racialisation of the Irish in England did not fade away at the end of the nineteenth century but became transmuted in new forms which have continued to place the ‘white’ Irish outside the boundaries of the English nation. These have been strangely ignored by social scientists, who conflate Irishness and working-class identities in England without acknowledging the distinctive contribution of Irish backgrounds to constructions of class difference. Gender locates Irish women and men differently in relation to these class positions, for example allowing mothers to be blamed for the perpetuation of the underclass. Class and gender are also largely unrecognised dimensions of Irish ethnicity in the USA, where the presence of ‘poor white’ neighbourhoods continues to challenge the iconic story of Irish upward mobility. Irishness thus remains central to the construction of mainstream ‘white’ identities in both the USA and Britain into the twenty-first century

Comparison of summer and winter objectively measured physical activity and sedentary behavior in older adults: Age, gene/environment susceptibility Reykjavik study

In Iceland, there is a large variation in daylight between summer and winter. The aim of the study was to identify how this large variation influences physical activity (PA) and sedentary behavior (SB). Free living PA was measured by a waist-worn accelerometer for one week during waking hours in 138 community-dwelling older adults (61.1% women, 80.3 ± 4.9 years) during summer and winter months. In general, SB occupied about 75% of the registered wear-time and was highly correlated with age (β = 0.36). Although the differences were small, more time was spent during the summer in all PA categories, except for the moderate-to-vigorous PA (MVPA), and SB was reduced. More lifestyle PA (LSPA) was accumulated in ≥5-min bouts during summer than winter, especially among highly active participants. This information could be important for policy makers and health professionals working with older adults. Accounting for seasonal difference is necessary in analyzing SB and PA data. View Full-TextThis study has been funded by NIA contract N01-AG-1-2100, the NIA Intramural Research Program, Hjartavernd (the Icelandic Heart Association), and the Althingi (the Icelandic Parliament). This work was also supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE-0940903 and by the National Institutes of Health Intramural Research Program, grant number: Z01 DK071013 and Z01 DK071014 to Robert J. Brychta and Kong Y. Chen. The researchers are indebted to the participants for their willingness to participate in the study.Peer Reviewe

QuantCrit: education, policy, ‘Big Data’ and principles for a critical race theory of statistics

Quantitative research enjoys heightened esteem among policy-makers, media and the general public. Whereas qualitative research is frequently dismissed as subjective and impressionistic, statistics are often assumed to be objective and factual. We argue that these distinctions are wholly false; quantitative data is no less socially constructed than any other form of research material. The first part of the paper presents a conceptual critique of the field with empirical examples that expose and challenge hidden assumptions that frequently encode racist perspectives beneath the façade of supposed quantitative objectivity. The second part of the paper draws on the tenets of Critical Race Theory (CRT) to set out some principles to guide the future use and analysis of quantitative data. These ‘QuantCrit’ ideas concern (1) the centrality of racism as a complex and deeply-rooted aspect of society that is not readily amenable to quantification; (2) numbers are not neutral and should be interrogated for their role in promoting deficit analyses that serve White racial interests; (3) categories are neither ‘natural’ nor given and so the units and forms of analysis must be critically evaluated; (4) voice and insight are vital: data cannot ‘speak for itself’ and critical analyses should be informed by the experiential knowledge of marginalized groups; (5) statistical analyses have no inherent value but can play a role in struggles for social justice

Cluster Headache Genomewide Association Study and Meta-Analysis Identifies Eight Loci and Implicates Smoking as Causal Risk Factor

Objective: The objective of this study was to aggregate data for the first genomewide association study meta-analysis of cluster headache, to identify genetic risk variants, and gain biological insights. Methods: A total of 4,777 cases (3,348 men and 1,429 women) with clinically diagnosed cluster headache were recruited from 10 European and 1 East Asian cohorts. We first performed an inverse-variance genomewide association meta-analysis of 4,043 cases and 21,729 controls of European ancestry. In a secondary trans-ancestry meta-analysis, we included 734 cases and 9,846 controls of East Asian ancestry. Candidate causal genes were prioritized by 5 complementary methods: expression quantitative trait loci, transcriptome-wide association, fine-mapping of causal gene sets, genetically driven DNA methylation, and effects on protein structure. Gene set and tissue enrichment analyses, genetic correlation, genetic risk score analysis, and Mendelian randomization were part of the downstream analyses. Results: The estimated single nucleotide polymorphism (SNP)-based heritability of cluster headache was 14.5%. We identified 9 independent signals in 7 genomewide significant loci in the primary meta-analysis, and one additional locus in the trans-ethnic meta-analysis. Five of the loci were previously known. The 20 genes prioritized as potentially causal for cluster headache showed enrichment to artery and brain tissue. Cluster headache was genetically correlated with cigarette smoking, risk-taking behavior, attention deficit hyperactivity disorder (ADHD), depression, and musculoskeletal pain. Mendelian randomization analysis indicated a causal effect of cigarette smoking intensity on cluster headache. Three of the identified loci were shared with migraine. Interpretation: This first genomewide association study meta-analysis gives clues to the biological basis of cluster headache and indicates that smoking is a causal risk factor. ANN NEUROL 2023

Rare variants with large effects provide functional insights into the pathology of migraine subtypes, with and without aura

Migraine is a complex neurovascular disease with a range of severity and symptoms, yet mostly studied as one phenotype in genome-wide association studies (GWAS). Here we combine large GWAS datasets from six European populations to study the main migraine subtypes, migraine with aura (MA) and migraine without aura (MO). We identified four new MA-associated variants (in PRRT2, PALMD, ABO and LRRK2) and classified 13 MO-associated variants. Rare variants with large effects highlight three genes. A rare frameshift variant in brain-expressed PRRT2 confers large risk of MA and epilepsy, but not MO. A burden test of rare loss-of-function variants in SCN11A, encoding a neuron-expressed sodium channel with a key role in pain sensation, shows strong protection against migraine. Finally, a rare variant with cis-regulatory effects on KCNK5 confers large protection against migraine and brain aneurysms. Our findings offer new insights with therapeutic potential into the complex biology of migraine and its subtypes.</p