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The Global Financial Crisis of 2007–2008 has elicited various debates, ranging from ethics over the stability of the banking system to subtle technical issues regarding the Gaussian and other copulas. We want to look at the crisis from a particular perspective. Credit derivatives have much in common with treaty reinsurance, including risk transfer via pooling and layering, scarce data, skewed distributions, and a limited number of specialised players in the market. This leads to a special mixture of mathematical/statistical and behavioural challenges. Reinsurers have been struggling to cope with these, not always successfully, but they have learned some lessons over the course of more than one century in business. This has led to certain rules being adopted by the reinsurance market and to a certain mindset being adopted by the individuals working in the industry. Some cultures established in the reinsurance world could possibly inspire markets like the credit derivatives market, but the subtle differences between the two worlds matter. We will see that traditional reinsurance has built-in incentives for (some) fairness, while securitisation can foster opportunism.
Self-sovereign identity (SSI) is an emerging and promising concept that enables users to control their identity while enhancing security and privacy compared to other identity management (IDM) approaches. Despite the recent advancements in SSI technologies, federated identity management (FIDM) systems continue to dominate the IDM market. Selecting an IDM to implement for a specific application is a complex task that requires a thorough understanding of the potential external cyber risks. However, existing research scarcely compares SSI and FIDM from the perspective of these external threats. In response to this gap, our article provides an attack surface analysis focused solely on external threats for both systems. This analysis can serve as a reference to compare the relevant security and privacy risks associated with these external threats. The threat landscapes of external attackers were systematically synthesized from the main components and functionalities of the common standards and designs. We further present a use case analysis that applies this attack surface analysis to compare the external cyber risks of the two systems in detail when managing cross-border identity between European countries. This work can be particularly useful for considering a more secure design for future IDM applications, taking into account the landscape of external threats.
We carried out a retrospective study of acute gastroenteritis (AGE) outbreaks reported between 1 January 2015 and 31 December 2021 in Catalonia (Spain) to compare the incidence from 2015 to 2019 with that observed from 2020 to 2021. We observed a higher incidence rate of outbreaks during the prepandemic period (16.89 outbreaks/1,000,000 person-years) than during the pandemic period (6.96 outbreaks/1,000,000 person-years) (rate ratio (RR) 0.41; 95% confidence interval (CI) 0.34 to 0.51). According to the aetiology of the outbreak, those of viral aetiology decreased from 7.82 to 3.38 outbreaks/1,000,000 person-years (RR 2.31; 95% CI 1.72 to 3.12), and those of bacterial aetiology decreased from 5.01 to 2.78 outbreaks/1,000,000 person-years (RR 1.80; 95% CI 1.29 to 2.52). There was a great reduction in AGE outbreaks in Catalonia. This reduction may have been due to the effect of the nonpharmaceutical measures applied to reduce the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), but the collapse of the healthcare system and epidemiological surveillance services may also have had a strong influence.
Since 1998, Bogotá has consistently made substantial efforts to foster the bicycle’s role as a primary mode of transportation. Recent years have witnessed a compelling aspiration for the city to ascend as the “bicycle capital of the world,” evident in its accomplishment of 6.6% of daily trips completed by bicycle in 2019. This achievement translates to 880.367 daily cycling journeys (District Secretariat of Mobility of Bogotá, 2019). These statistics surpass regional benchmarks; for instance, other capital cities such as Santiago de Chile account for 510.569 bicycle trips, Mexico City for 433.981, and Rio de Janeiro for 217.000 (Ríos et al., 2015). Despite this progress, Bogotá lacks a comprehensive evaluation of both infrastructure quality and the user experience while cycling.
This translational research article aimed to explore this gap by delving into the integration of user perceptions and experiences within the policy formulation process. This strategic approach is poised to enhance cycling’s allure as a mode of transportation for prospective cyclists while simultaneously maximizing the efficiency of investments in cycling infrastructure.
Statistics Using R introduces the most up-to-date approaches to R programming alongside an introduction to applied statistics using real data in the behavioral, social, and health sciences. It is uniquely focused on the importance of data management as an underlying and key principle of data analysis. It includes an online R tutorial for learning the basics of R, as well as two R files for each chapter, one in Base R code and the other in tidyverse R code, that were used to generate all figures, tables, and analyses for that chapter. These files are intended as models to be adapted and used by readers in conducting their own research. Additional teaching and learning aids include solutions to all end-of-chapter exercises and PowerPoint slides to highlight the important take-aways of each chapter. This textbook is appropriate for both undergraduate and graduate students in social sciences, applied statistics, and research methods.
Chapter 10 covers INFERENCES INVOLVING THE MEAN OF A SINGLE POPULATION WHEN σ IS KNOWN and includes the following specific topics, among others: Estimating the Population Mean, μ, Interval Estimation, Confidence Intervals, Hypothesis Testing and Interval Estimation, Effect Size,Type II Error, and Power.
Chapter 6 covers SIMPLE LINEAR REGRESSION and includes the following specific topics, among others: the “best-fitting” line, accuracy of prediction, standardized regressin, R as a measure of overal fit and the importance of the scatterplot..
Chapter 4 covers the re-expression/trannsformatin of variables and includes the following specific topics, among others: Linear and Nonlinear Transformations, Standard Scores, z-Scores,Recoding Variables, Combining Variables, Data Management Fundamentals, and the importance of the .do-File.
Chapter 12 covers AN INTRODUCTION TO RESEARCH DESIGN and includes the following specific topics, among others: Descriptive, Relational, and Causal Research Studies , Blocking, Quasi-Experimental Designs, Threats to Internal Validity, and Threats to External Validity.
Chapter 2 covers univariate distributions and includes the following specific topics, among others: Frequency and Percent Distribution Tables, Bar Charts, Pie Charts, Stem-and-Leaf Displays, Histograms, Line Graphs, Shape of a Distribution, Cumulative Percent Distributions, Percentiles, Percentile Ranks, and Boxplots.
Chapter 7 covers PROBABILITY FUNDAMENTALS and includes the following specific topics, among others: the discrete case, additive rules of probability, complement rule of probability, multiplicative rule of probability,conditional probability, Bayes’ theorem, the Law of Large Numbers.
Chapter 17 covers TWO-WAY INTERACTIONS IN MULTIPLE REGRESSION and includes the following specific topics, among others: Two-Way Interaction, First-Order Effects, Main Effects, Interaction Effects, Model Selection, AIC, BIC, and Probing Interactions.
Chapter 15 covers CORRELATION AND SIMPLE REGRESSION AS INFERENTIAL TECHNIQUES and includes the following specific topics, among others:Bivariate Normal Distribution, Statistical Significance Test of Correlation, Confidence Intervals, Statistical Significance of b-Weight, Fit of the Overall Regression Equation, R and R-squared, Adjusted R-squared, Regression Diagnostics, Residual Plots, Influential Observations, Discrepancy, Leverage, Influence, and Power Analyses.
Chapter 1 provides an introdution to the study of statistics and covers the following specific topics among others: Statistical Software in Data Analysis, Descriptive and Inferential Statistics, Measurement of Variables, and an introduction to the Stata Software Package
Chapter 11 covers INFERENCES INVOLVING THE MEAN WHEN σ IS NOT KNOWN: ONE- AND TWO-SAMPLE DESIGNS and includes the following specific topics, among others: t-Distribution, Degrees of Freedom, t-test assumptions, one-sample t-test, two-sample test for independent groups, two-sample test for related groups, paired-sample t-tests, effect size, the bootstrap, and power analysis.