Detection and correction of artificial shifts in climate series

Summary.  Many long instrumental climate records are available and might provide useful information in climate research. These series are usually affected by artificial shifts, due to changes in the conditions of measurement and various kinds of spurious data. A comparison with surrounding weather-stations by means of a suitable two-factor model allows us to check the reliability of the series. An adapted penalized log-likelihood procedure is used to detect an unknown number of breaks and outliers. An example concerning temperature series from France confirms that a systematic comparison of the series together is valuable and allows us to correct the data even when no reliable series can be taken as a reference.     

Data-driven Kriging models based on FANOVA-decomposition

Kriging models have been widely used in computer experiments for the analysis of time-consuming computer codes. Based on kernels, they are flexible and can be tuned to many situations. In this paper, we construct kernels that reproduce the computer code complexity by mimicking its interaction structure. While the standard tensor-product kernel implicitly assumes that all interactions are active, the new kernels are suited for a general interaction structure, and will take advantage of the absence of interaction between some inputs. The methodology is twofold. First, the interaction structure is estimated from the data, using a first initial standard Kriging model, and represented by a so-called FANOVA graph. New FANOVA-based sensitivity indices are introduced to detect active interactions. Then this graph is used to derive the form of the kernel, and the corresponding Kriging model is estimated by maximum likelihood. The performance of the overall procedure is illustrated by several 3-dimensional and 6-dimensional simulated and real examples. A substantial improvement is observed when the computer code has a relatively high level of complexity.     

Risk Assessment for Hospital-Acquired Diseases: A Risk-Theory Approach

We introduce a new approach to hospital-acquired disease risk assessment from public health databases. In a spirit similar to actuarial risk theory, we define an adjustment coefficient that can quantify the risk associated with a hospital department, allowing comparisons of similar departments. The adjustment coefficient characterizes the tail of the distribution of the total patient length of stay in a department before the first disease event occurs. We show that this coefficient is the solution of a Lundberg-like equation, and we provide a nonparametric estimation procedure for this measure, based on a Cramér-Lundberg approximation for the tail of the distribution. Using simulations, we provide evidence of the robustness of the approximation to various individual risk models. In addition, we illustrate the relevance of this approach by evaluating the risk associated with a standard patient safety indicator in 20 hospitals of southeastern France.     

Time‐Varying Risk Premium in Large Cross‐Sectional Equity Data Sets

We develop an econometric methodology to infer the path of risk premia from a large unbalanced panel of individual stock returns. We estimate the time‐varying risk premia implied by conditional linear asset pricing models where the conditioning includes both instruments common to all assets and asset‐specific instruments. The estimator uses simple weighted two‐pass cross‐sectional regressions, and we show its consistency and asymptotic normality under increasing cross‐sectional and time series dimensions. We address consistent estimation of the asymptotic variance by hard thresholding, and testing for asset pricing restrictions induced by the no‐arbitrage assumption. We derive the restrictions given by a continuum of assets in a multi‐period economy under an approximate factor structure robust to asset repackaging. The empirical analysis on returns for about ten thousand U.S. stocks from July 1964 to December 2009 shows that risk premia are large and volatile in crisis periods. They exhibit large positive and negative strays from time‐invariant estimates, follow the macroeconomic cycles, and do not match risk premia estimates on standard sets of portfolios. The asset pricing restrictions are rejected for a conditional four‐factor model capturing market, size, value, and momentum effects.     

Entry and Exit in OTC Derivatives Markets

We develop a parsimonious model to study the equilibrium and socially optimal decisions of banks to enter, trade in, and possibly exit, an OTC market. Although we endow all banks with the same trading technology, banks' optimal entry and trading decisions endogenously lead to a realistic market structure composed of dealers and customers with distinct trading patterns. We decompose banks' entry incentives into incentives to hedge risk and incentives to make intermediation profits. We show that dealer banks enter more than is socially optimal. In the face of large negative shocks, they may also exit more than is socially optimal when markets are not perfectly resilient.     

Data-driven stochastic inversion via functional quantization

In this paper, we propose a new methodology for solving stochastic inversion problems through computer experiments, the stochasticity being driven by a functional random variables. This study is motivated by an automotive application. In this context, the simulator code takes a double set of simulation inputs: deterministic control variables and functional uncertain variables. This framework is characterized by two features. The first one is the high computational cost of simulations. The second is that the probability distribution of the functional input is only known through a finite set of realizations. In our context, the inversion problem is formulated by considering the expectation over the functional random variable. We aim at solving this problem by evaluating the model on a design, whose adaptive construction combines the so-called stepwise uncertainty reduction methodology with a strategy for an efficient expectation estimation. Two greedy strategies are introduced to sequentially estimate the expectation over the functional uncertain variable by adaptively selecting curves from the initial set of realizations. Both of these strategies consider functional principal component analysis as a dimensionality reduction technique assuming that the realizations of the functional input are independent realizations of the same continuous stochastic process. The first strategy is based on a greedy approach for functional data-driven quantization, while the second one is linked to the notion of space-filling design. Functional PCA is used as an intermediate step. For each point of the design built in the reduced space, we select the corresponding curve from the sample of available curves, thus guaranteeing the robustness of the procedure to dimension reduction. The whole methodology is illustrated and calibrated on an analytical example. It is then applied on the automotive industrial test case where we aim at identifying the set of control parameters leading to meet the pollutant emission standards of a vehicle.     

Early Segmentation Abilities in Preterm Infants

Infants born preterm have higher risks of developing linguistic deficits. Considering that the ability to segment words from fluent speech is crucial for lexical acquisition, Experiment 1 tested the ability of healthy extremely‐to‐late preterm infants to segment monosyllabic words at 6 months of postnatal age. Results establish basic segmentation skills in these infants. While we failed to find an effect of the degree of prematurity, this issue will need further exploration. Future studies will also have to specify the scope of these early segmentation skills, both in terms of the types of words segmented, the cues used to do so, and in terms of possible differences in performance between subgroups of preterm infants (e.g., based on gestational age or medical risks). Lastly, given that the preterm infants tested had a mean maturational age of 4 months, Experiment 2 explored monosyllabic segmentation in full‐term 4‐month olds. Infants succeeded at the task, providing the earliest developmental evidence of word segmentation in full‐term infants. These findings better specify the early trajectory of segmentation abilities in both full‐term and healthy, low‐risk preterm infants and support the proposal that prematurity might have a differential effect on the early acquisition of various linguistic levels.     

Latent Class Analysis of Gambling Activities in a Sample of Young Swiss Men: Association with Gambling Problems,Substance Use Outcomes,Personality Traits and Coping Strategies

The study aimed to identify different patterns of gambling activities (PGAs) and to investigate how PGAs differed in gambling problems, substance use outcomes, personality traits and coping strategies. A representative sample of 4989 young Swiss males completed a questionnaire assessing seven distinct gambling activities, gambling problems, substance use outcomes, personality traits and coping strategies. PGAs were identified using latent class analysis (LCA). Differences between PGAs in gambling and substance use outcomes, personality traits and coping strategies were tested. LCA identified six different PGAs. With regard to gambling and substance use outcomes, the three most problematic PGAs were extensive gamblers, followed by private gamblers, and electronic lottery and casino gamblers, respectively. By contrast, the three least detrimental PGAs were rare or non-gamblers, lottery only gamblers and casino gamblers. With regard to personality traits, compared with rare or non-gamblers, private and casino gamblers reported higher levels of sensation seeking. Electronic lottery and casino gamblers, private gamblers and extensive gamblers had higher levels of aggression-hostility. Extensive and casino gamblers reported higher levels of sociability, whereas casino gamblers reported lower levels of anxiety–neuroticism. Extensive gamblers used more maladaptive and less adaptive coping strategies than other groups. Results suggest that gambling is not a homogeneous activity since different types of gamblers exist according to the PGA they are engaged in. Extensive gamblers, electronic and casino gamblers and private gamblers may have the most problematic PGAs. Personality traits and coping skills may predispose individuals to PGAs associated with more or less negative outcomes.     

Influence of competition level on referees’ decision-making in handball

The influence of competition level on referees’ decision-making was investigated. Referees’ decisions in 90 handball games (30 games X 3 competition levels) were observed in different situations related to the advantage rule, and 100 referees from two different levels of expertise were subsequently asked to offer explanations for the competition-level effects from the first part of the study. Results revealed that at the highest level of competition referees intervened less frequently with sporting sanctions, but more frequently with disciplinary sanctions. These effects were apparent mainly in immediate intervention situations and unsuccessful advantage situations, but not in successful situations. Referees explained these effects of competition level in terms of a player competence stereotype, in addition to referees’ different expertise across competition level. The implications of the findings for understanding how status-related stereotypes impact on intervention behavior are discussed.     

No claim, no pain. Measuring the non-take-up of social assistance using register data

The main objectives of social assistance benefits, including poverty alleviation and labor-market or social reintegration, can be seriously compromised if support is difficult to access. While recent studies point to high non-take-up rates, existing evidence does not make full use of the information recorded by benefit agencies. Most studies have to rely on interview-based data, with misreporting and measurement errors affecting the variables needed to establish both benefit receipt and benefit entitlement. In this paper, we exploit a unique combination of Finnish administrative data and eligibility simulations based on the tax-benefit calculator of the Finnish authorities, carefully investigating the measurement issues that remain. We find rates of non-take-up that are both substantial and robust: 40 to 50% of those eligible do not claim. Using repeated cross-section estimations for years 1996?C2003, we identify a set of stable determinants of claiming behavior and suggest that changes in behavior could drive the observed downward trend in take-up rates during the post-recession period. We discuss the poverty implications of our results.