Sex-specific associations between sleep and mental health in university students: a large cross-sectional study

Abstract

Objective: To examine the associations between sleep problems and mental health dimensions in university students, and the effect of sex on these associations. Participants: Self-reported survey data from 3,483 students aged 18–30?years was drawn from a larger web-based study (i-Share) conducted in France in the years 2013–2017. Methods: We performed logistic regression analyses stratified by sex using insufficient sleep duration, poor sleep quality, difficulty initiating sleep and excessive daytime sleepiness, in relation with stress, self-esteem, depression and anxiety. Results: All sleep problems were strongly associated with all mental health dimensions, particularly anxiety, in female students. Sleep and mental health problems were also associated in male students, with the exception of low self-esteem, but odds ratios were lower than for female students. Conclusions: Present findings warrant attention to propose early interventions targeting sleep and mental health in the university setting taking sex into account.     

The Gaussian rank correlation estimator: robustness properties

The Gaussian rank correlation equals the usual correlation coefficient computed from the normal scores of the data. Although its influence function is unbounded, it still has attractive robustness properties. In particular, its breakdown point is above 12%. Moreover, the estimator is consistent and asymptotically efficient at the normal distribution. The correlation matrix obtained from pairwise Gaussian rank correlations is always positive semidefinite, and very easy to compute, also in high dimensions. We compare the properties of the Gaussian rank correlation with the popular Kendall and Spearman correlation measures. A simulation study confirms the good efficiency and robustness properties of the Gaussian rank correlation. In the empirical application, we show how it can be used for multivariate outlier detection based on robust principal component analysis.     

What do board members in art organizations do? A grounded theory approach

This paper explores the characteristics and activities of board members in art organizations. It describes two case studies within international artists’ residencies in France and Germany. Adopting a grounded theory approach, our study identifies the different characteristics of board members such as friendship, competence and diversity, together with board activities such as controlling, advising, legitimating, helping, exchanging and deciding. The contributions of this research are twofold. First, contrary to most governance literature, the independence of board members and board monitoring roles do not seem to be important issues for the two art organizations involved. Instead, friendship and networking appear to be key factors for the board, helping the organizations to be successful by giving them more chance to survive and grow. Second, as specific board members in the two cases studied, executive directors play a continual role of “governance entrepreneurs,” building and managing various board-related organs to counterbalance the influence of fund providers. In this way, executive directors attempt to satisfy their predominant public fund providers and to help the artists’ residencies survive.     

Top Management Team Members’ Decision Influence and Cooperative Behaviour: An Empirical Study in the Information Technology Industry

Organizational leadership is generally distributed between the chief executive officer (CEO) and the top management team (TMT) members. Building on this observation, we present an empirical investigation of the cues for CEOs to delegate decision‐making influence to particular TMT members. In the literature, explanations both based on expertise and driven by similarity are described. In this study, we reconcile both explanations by examining the moderating role of the TMT's level of ‘cooperative behaviour’ (collaboration and information exchange). We analyse when and in what circumstances TMT members’ expertise and similarity to the CEO regarding his/her functional background and/or locus‐of‐control predict their decision‐making influence. We postulate that TMT cooperative behaviour will advance the effect of expertise on TMT members’ decision influence but impede the effect of similarity to the CEO. Our hypotheses are tested on a data set of 135 TMT members from 32 Dutch and Belgian information technology firms. Overall, we find that our proposed research model is confirmed for technology‐oriented decisions. Furthermore, we draw exploratory conclusions about the effect of TMT cooperative behaviour on the systematic distribution of decision influence in TMTs.     

Alignments with non-overlapping moves,inversions and tandem duplications in <Emphasis Type="Italic">O</Emphasis>(<Emphasis Type="Italic">n</Emphasis><Superscript>4</Superscript>) time

Sequence alignment is a central problem in bioinformatics. The classical dynamic programming algorithm aligns two sequences by optimizing over possible insertions, deletions and substitutions. However, other evolutionary events can be observed, such as inversions, tandem duplications or moves (transpositions). It has been established that the extension of the problem to move operations is NP-complete. Previous work has shown that an extension restricted to non-overlapping inversions can be solved in O(n ³) with a restricted scoring scheme. In this paper, we show that the alignment problem extended to non-overlapping moves can be solved in O(n ⁵) for general scoring schemes, O(n ⁴log n) for concave scoring schemes and O(n ⁴) for restricted scoring schemes. Furthermore, we show that the alignment problem extended to non-overlapping moves, inversions and tandem duplications can be solved with the same time complexities. Finally, an example of an alignment with non-overlapping moves is provided. A preliminary version of this paper appeared in the Proceedings of COCOON 2007, LNCS, vol. 4598, pp. 151–164.     

Location adjustment for the minimum volume ellipsoid estimator

Estimating multivariate location and scatter with both affine equivariance and positive breakdown has always been difficult. A well-known estimator which satisfies both properties is the Minimum Volume Ellipsoid Estimator (MVE). Computing the exact MVE is often not feasible, so one usually resorts to an approximate algorithm. In the regression setup, algorithms for positive-breakdown estimators like Least Median of Squares typically recompute the intercept at each step, to improve the result. This approach is called intercept adjustment. In this paper we show that a similar technique, called location adjustment, can be applied to the MVE. For this purpose we use the Minimum Volume Ball (MVB), in order to lower the MVE objective function. An exact algorithm for calculating the MVB is presented. As an alternative to MVB location adjustment we propose L ₁ location adjustment, which does not necessarily lower the MVE objective function but yields more efficient estimates for the location part. Simulations compare the two types of location adjustment. We also obtain the maxbias curves of L ₁ and the MVB in the multivariate setting, revealing the superiority of L ₁.     

How Risk Disciplines Pre-Commitment

This paper studies the entry strategies of firms on risky markets. We focus on markets where demand is affine and cost is linear; moreover, the demand includes a normally distributed random variable. In such a model, we show that the leader’s strategy changes with the level of market risk even when firms are risk neutral. Therefore, the availability of future information for a Stackelberg follower has a feedback effect on the leader’s strategy. We also show that compared with traditional markets with no risk, the basic trade-off between flexibility and pre-commitment is only slightly changed in the qualitative game where firms are free to choose when to enter the market.

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On sequential Monte Carlo sampling methods for Bayesian filtering

In this article, we present an overview of methods for sequential simulation from posterior distributions. These methods are of particular interest in Bayesian filtering for discrete time dynamic models that are typically nonlinear and non-Gaussian. A general importance sampling framework is developed that unifies many of the methods which have been proposed over the last few decades in several different scientific disciplines. Novel extensions to the existing methods are also proposed. We show in particular how to incorporate local linearisation methods similar to those which have previously been employed in the deterministic filtering literature; these lead to very effective importance distributions. Furthermore we describe a method which uses Rao-Blackwellisation in order to take advantage of the analytic structure present in some important classes of state-space models. In a final section we develop algorithms for prediction, smoothing and evaluation of the likelihood in dynamic models.     

Robust online scale estimation in time series: A model-free approach

This paper presents variance extraction procedures for univariate time series. The volatility of a times series is monitored allowing for non-linearities, jumps and outliers in the level. The volatility is measured using the height of triangles formed by consecutive observations of the time series. This idea was proposed by Rousseeuw and Hubert [1996. Regression-free and robust estimation of scale for bivariate data. Comput. Statist. Data Anal. 21, 67–85] in the bivariate setting. This paper extends their procedure to apply for online scale estimation in time series analysis. The statistical properties of the new methods are derived and finite sample properties are given. A financial and a medical application illustrate the use of the procedures.     

Risk Measure Inference

We propose a bootstrap-based test of the null hypothesis of equality of two firms’ conditional risk measures (RMs) at a single point in time. The test can be applied to a wide class of conditional risk measures issued from parametric or semiparametric models. Our iterative testing procedure produces a grouped ranking of the RMs, which has direct application for systemic risk analysis. Firms within a group are statistically indistinguishable from each other, but significantly more risky than the firms belonging to lower ranked groups. A Monte Carlo simulation demonstrates that our test has good size and power properties. We apply the procedure to a sample of 94 U.S. financial institutions using ΔCoVaR, MES, and %SRISK. We find that for some periods and RMs, we cannot statistically distinguish the 40 most risky firms due to estimation uncertainty.