Population-based structural health monitoring (PBSHM) provides a means of accounting for inter-turbine correlations when solving the problem of wind farm anomaly detection. Across a wind farm, where a group of structures (turbines) is placed in close vicinity to each other, the environmental conditions and, thus, structural behavior vary in a spatiotemporal manner. Spatiotemporal trends are often overlooked in the existing data-based wind farm anomaly detection methods, because most current methods are designed for individual structures, that is, detecting anomalous behavior of a turbine based on the past behavior of the same turbine. In contrast, the idea of PBSHM involves sharing data across a population of structures and capturing the interactions between structures. This paper proposes a population-based anomaly detection method, specifically for a localized population of structures, which accounts for the spatiotemporal correlations in structural behavior. A case study from an offshore wind farm is given to demonstrate the potential of the proposed method as a wind farm performance indicator. It is concluded that the method has the potential to indicate operational anomalies caused by a range of factors across a wind farm. The method may also be useful for other tasks such as wind power and turbine load modeling.

It is well known that each statistic in the family of power divergence statistics, across n trials and r classifications with index parameter $\lambda\in\mathbb{R}$ (the Pearson, likelihood ratio, and Freeman–Tukey statistics correspond to $\lambda=1,0,-1/2$, respectively), is asymptotically chi-square distributed as the sample size tends to infinity. We obtain explicit bounds on this distributional approximation, measured using smooth test functions, that hold for a given finite sample n, and all index parameters ($\lambda>-1$) for which such finite-sample bounds are meaningful. We obtain bounds that are of the optimal order $n^{-1}$. The dependence of our bounds on the index parameter $\lambda$ and the cell classification probabilities is also optimal, and the dependence on the number of cells is also respectable. Our bounds generalise, complement, and improve on recent results from the literature.

We analyse features of the patterns formed from a simple model for a martensitic phase transition that fragments the unit square into rectangles. This is a fragmentation model that can be encoded by a general branching random walk. An important quantity is the distribution of the lengths of the interfaces in the pattern, and we establish limit theorems for some of the asymptotics of the interface profile. In particular, we are able to use a general branching process to show almost sure power law decay of the number of interfaces of at least a certain size and a general branching random walk to examine the numbers of rectangles of a certain aspect ratio. In doing so we extend a theorem on the growth of the general branching random walk as well as developing results on the tail behaviour of the limiting random variable in our general branching process.

We study convergence to non-minimal quasi-stationary distributions for one-dimensional diffusions. We give a method for reducing the convergence to the tail behavior of the lifetime via a property we call the first hitting uniqueness. We apply the results to Kummer diffusions with negative drift and give a class of initial distributions converging to each non-minimal quasi-stationary distribution.

In this article, we explore the challenges of global governance and the particular challenge presented by global data governance. We discuss a range of challenges to developing meaningful global governance institutions for regulating how companies and governments around the world manage and utilize consumer data. These challenges are compounded by their global nature and the complexities of Internet-based technologies. We argue that the following gaps exist for effective global data governance: (a) there is no overarching global framework for protecting consumer data, and it is partial and incomplete; (b) there is a lack of data protection for international data transfers, as much of the regulation that is being developed is not global in scale; and (c) new areas of data collection and use compound concerns to effective data governance in a globalized digital world. Moreover, we highlight important needs in terms of both global governance and impending challenges related to current and new uses of data. Any global governance framework should recognize the need for an iterative process where communication is ongoing between the necessary stakeholders. Agreements should incorporate common goals to maximize the potential development of global data governance norms. However, goals must remain flexible to the different data environments across nation-states while maintaining a global scope to ensure data protection. In addition, any agreement should consider the emerging challenges in this area. These challenges include new methods of data collection and use, as well as protecting individuals from manipulation and undue influence based on how their data are being used, processed, and collected.

We show that load-sharing models (a very special class of multivariate probability models for nonnegative random variables) can be used to obtain basic results about a multivariate extension of stochastic precedence and related paradoxes. Such results can be applied in several different fields. In particular, applications of them can be developed in the context of paradoxes which arise in voting theory. Also, an application to the notion of probability signature may be of interest, in the field of systems reliability.

In March 2018, the US Food and Drug Administration (FDA), US Centers for Disease Control and Prevention, California Department of Public Health, Los Angeles County Department of Public Health and Pennsylvania Department of Health initiated an investigation of an outbreak of Burkholderia cepacia complex (Bcc) infections. Sixty infections were identified in California, New Jersey, Pennsylvania, Maine, Nevada and Ohio. The infections were linked to a no-rinse cleansing foam product (NRCFP), produced by Manufacturer A, used for skin care of patients in healthcare settings. FDA inspected Manufacturer A's production facility (manufacturing site of over-the-counter drugs and cosmetics), reviewed production records and collected product and environmental samples for analysis. FDA's inspection found poor manufacturing practices. Analysis by pulsed-field gel electrophoresis confirmed a match between NRCFP samples and clinical isolates. Manufacturer A conducted extensive recalls, FDA issued a warning letter citing the manufacturer's inadequate manufacturing practices, and federal, state and local partners issued public communications to advise patients, pharmacies, other healthcare providers and healthcare facilities to stop using the recalled NRCFP. This investigation highlighted the importance of following appropriate manufacturing practices to minimize microbial contamination of cosmetic products, especially if intended for use in healthcare settings.

Branching-stable processes have recently appeared as counterparts of stable subordinators, when addition of real variables is replaced by branching mechanisms for point processes. Here we are interested in their domains of attraction and describe explicit conditions for a branching random walk to converge after a proper magnification to a branching-stable process. This contrasts with deep results obtained during the past decade on the asymptotic behavior of branching random walks and which involve either shifting without rescaling, or demagnification.

This paper studies the properties of predictive regressions for asset returns in economic systems governed by persistent vector autoregressive dynamics. In particular, we allow for the state variables to be fractionally integrated, potentially of different orders, and for the returns to have a latent persistent conditional mean, whose memory is difficult to estimate consistently by standard techniques in finite samples. Moreover, the predictors may be endogenous and “imperfect.” In this setting, we develop a consistent local spectrum (LCM) estimation procedure, that delivers asymptotic Gaussian inference. Furthermore, we provide a new LCM-based estimator of the conditional mean persistence, that leverages biased regression slopes as well as new LCM-based tests for significance of (a subset of) the predictors, which are valid even without estimating the return persistence. Simulations illustrate the theoretical arguments. Finally, an empirical application to monthly S&P 500 return predictions provides evidence for a fractionally integrated conditional mean component. Our new LCM procedure and tools indicate significant predictive power for future returns stemming from key state variables such as the default spread and treasury interest rates.

Conditional value-at-risk (CVaR) and conditional expected shortfall (CES) are widely adopted risk measures which help monitor potential tail risk while adapting to evolving market information. In this paper, we propose an approach to constructing simultaneous confidence bands (SCBs) for tail risk as measured by CVaR and CES, with the confidence bands uniformly valid for a set of tail levels. We consider one-sided tail risk (downside or upside tail risk) as well as relative tail risk (the ratio of upside to downside tail risk). A general class of location-scale models with heavy-tailed innovations is employed to filter out the return dynamics. Then, CVaR and CES are estimated with the aid of extreme value theory. In the asymptotic theory, we consider two scenarios: (i) the extreme scenario that allows for extrapolation beyond the range of the available data and (ii) the intermediate scenario that works exclusively in the case where the available data are adequate relative to the tail level. For finite-sample implementation, we propose a novel bootstrap procedure to circumvent the slow convergence rates of the SCBs as well as infeasibility of approximating the limiting distributions. A series of Monte Carlo simulations confirm that our approach works well in finite samples.

The welfare state is currently undergoing a transition toward data-driven policies, management, and execution. This has important repercussions for frontline bureaucrats in such a “digital welfare state.” So far, impact of data-driven tools on frontline bureaucrats is primarily described in terms of curtailing or enlarging their discretionary space to make decisions. It is unclear, however, how daily work practices and role identities of frontline bureaucrats change in situ and which norms they develop to work with new data tools. In this article, we present an empirical study about the impact of a data dashboard in the Work and Income department of the municipality of Rotterdam. We answer the following research question: Which role identities, work practices, and norms of appropriate behavior of frontline bureaucrats in the social domain are reshaped by the introduction of a data dashboard? We use a multiple methods design consisting of semi-structured interviews, ethnographic observations, and document analysis. Our results reveal two role identities among frontline bureaucrats: (a) the client coach, and (b) the caseload manager. We show that the implementation of the dashboard stimulates a shift from a client coach role identity toward a caseload manager role identity. This shift is contested as it leads to role identity conflicts among frontline bureaucrats with a client coach role. Furthermore, we establish that the accommodation of the institutional void in which the introduction of the dashboard takes place, is centered around three themes of contestation: (a) data quality, (b) quality of service provision, and (c) data representations.

We propose a new estimator for the ultimate prediction uncertainty within the famous Mack’s distribution-free chain-ladder model, which can be proved to be unbiased (conditionally given the first triangle column) under some additional technical assumptions. A peculiar behaviour of the unbiased estimator is given by its possible negativity. This is a drawback which might be worth trading off for the unbiasedness property, since there is empirical evidence that the likelihood of a negative realisation is extremely low. This offers an alternative to the well-known Mack and BBMW formulas since the latters can be proved to be biased. However, we also show that this novel estimator, as well as the Mack and BBMW formulas, can (with non-negligible probability) materially fail to estimate the true uncertainty.

In this paper we introduce two new classes of stationary random simplicial tessellations, the so-called $\beta$- and $\beta^{\prime}$-Delaunay tessellations. Their construction is based on a space–time paraboloid hull process and generalizes that of the classical Poisson–Delaunay tessellation. We explicitly identify the distribution of volume-power-weighted typical cells, establishing thereby a remarkable connection to the classes of $\beta$- and $\beta^{\prime}$-polytopes. These representations are used to determine the principal characteristics of such cells, including volume moments, expected angle sums, and cell intensities.

The rich-get-richer rule reinforces actions that have been frequently chosen in the past. What happens to the evolution of individuals’ inclinations to choose an action when agents interact? Interaction tends to homogenize, while each individual dynamics tends to reinforce its own position. Interacting stochastic systems of reinforced processes have recently been considered in many papers, in which the asymptotic behavior is proven to exhibit almost sure synchronization. In this paper we consider models where, even if interaction among agents is present, absence of synchronization may happen because of the choice of an individual nonlinear reinforcement. We show how these systems can naturally be considered as models for coordination games or technological or opinion dynamics.

In recent papers, Bonus-Malus Scales (BMS) estimated using data have been considered as an alternative to longitudinal data and hierarchical data approaches to model the dependence between different contracts for the same insured. Those papers, however, did not discuss in detail how to construct and understand BMS models, and many of the BMS’s basic properties were not discussed. The first objective of this paper is to correct this situation by explaining the logic behind BMS models and by describing those properties. More particularly, we will explain how BMS models are linked with simple count regression models that have covariates associated with the past claims experience. This study could help actuaries to understand how and why they should use BMS models for experience rating. The second objective of this paper is to create artificial past claims history for each insured. This is done by combining recent panel data theory with BMS models. We show that this addition significantly improves the prediction capacity of the BMS and provides a temporary solution for insurers who do not have enough historical data. We apply the BMS model to real data from a major Canadian insurance company. Results are analysed deeply to identify specific aspects of the BMS model.

Soil-transmitted helminths, such as Ascaris lumbricoides, are the most prevalent parasites globally. Optimal anthelmintic treatment for A. lumbricoides in endemically infected communities is challenged by several host-related and environmental factors influencing infection acquisition. We assessed the risk of A. lumbricoides (re)infection after treatment in a Venezuelan rural community. Individual merthiolate-iodine-formaldehyde-fixed faecal samples were collected from 224 persons before a single-dose pyrantel treatment and at 1, 3, 6, 9 and 15 months after treatment. Effects of age, sex and socioeconomic status (SES) on A. lumbricoides prevalence, eggs/gram faeces (EPG) and infection (re)acquisition were assessed using both generalised linear mixed-effects models and survival analysis. Pre-treatment A. lumbricoides prevalence was 39.7%. Higher prevalence was associated with younger age and lower SES. Higher EPG values were observed among young children. Median time to A. lumbricoides infection was six months after treatment: at 1, 3, 6, 9 and 15 months post-treatment, cumulative incidence was 6.7%, 18.9%, 34.6%, 42.2%, and 52.6%, respectively. Younger age, lower SES, and pre-treatment A. lumbricoides infection status showed significantly elevated hazard ratios. Mass drug administration protocols would benefit from considering these factors in selective treatment strategies and possibly more than just annual or biannual treatments in the target population.