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.     

Sampling of Bayesian posteriors with a non-Gaussian probabilistic learning on manifolds from a small dataset

Statistics and Computing - This paper tackles the challenge presented by small-data to the task of Bayesian inference. A novel methodology, based on manifold learning and manifold sampling, is...     

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.     

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.     

Contrast Estimation for Parametric Stationary Determinantal Point Processes

We study minimum contrast estimation for parametric stationary determinantal point processes. These processes form a useful class of models for repulsive (or regular, or inhibitive) point patterns and are already applied in numerous statistical applications. Our main focus is on minimum contrast methods based on the Ripley's K‐function or on the pair correlation function. Strong consistency and asymptotic normality of theses procedures are proved under general conditions that only concern the existence of the process and its regularity with respect to the parameters. A key ingredient of the proofs is the recently established Brillinger mixing property of stationary determinantal point processes. This work may be viewed as a complement to the study of Y. Guan and M. Sherman who establish the same kind of asymptotic properties for a large class of Cox processes, which in turn are models for clustering (or aggregation).     

The cosine geometric distribution with count data modeling

In this paper, a new two-parameter discrete distribution is introduced. It belongs to the family of the weighted geometric distribution (GD), with the feature of using a particular trigonometric weight. This configuration adds an oscillating property to the former GD which can be helpful in analyzing the data with over-dispersion, as developed in this study. First, we present the basic statistical properties of the new distribution, including the cumulative distribution function, hazard rate function and moment generating function. Estimation of the related model parameters is investigated using the maximum likelihood method. A simulation study is performed to illustrate the convergence of the estimators. Applications to two practical datasets are given to show that the new model performs at least as well as some competitors.     

Marginal distribution of Markov-switching VAR processes

We make available simple and accurate closed-form approximations to the marginal distribution of Markov-switching vector auto-regressive (MS VAR) processes. The approximation is built upon the property of MS VAR processes of being Gaussian conditionally on any semi-infinite sequence of the latent state. Truncating the semi-infinite sequence and averaging over all possible sequences of that finite length yields a mixture of normals that converges to the unknown marginal distribution as the sequence length increases. Numerical experiments confirm the viability of the approach which extends to the closely related class of MS state space models. Several applications are discussed.     

A bivariate first-order signed integer-valued autoregressive process

Bivariate integer-valued time series occur in many areas, such as finance, epidemiology, business etc. In this article, we present bivariate autoregressive integer-valued time-series models, based on the signed thinning operator. Compared to classical bivariate INAR models, the new processes have the advantage to allow for negative values for both the time series and the autocorrelation functions. Strict stationarity and ergodicity of the processes are established. The moments and the autocovariance functions are determined. The conditional least squares estimator of the model parameters is considered and the asymptotic properties of the obtained estimators are derived. An analysis of a real dataset from finance and a simulation study are carried out to assess the performance of the model.     

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.