Regression in random design and Bayesian warped wavelets estimators

In this paper we deal with the regression problem in a random design setting. We investigate asymptotic optimality under minimax point of view of various Bayesian rules based on warped wavelets and show that they nearly attain optimal minimax rates of convergence over the Besov smoothness class considered. Warped wavelets have been introduced recently, they offer very good computable and easy-to-implement properties while being well adapted to the statistical problem at hand. We particularly put emphasis on Bayesian rules leaning on small and large variance Gaussian priors and discuss their simulation performances comparing them with a hard thresholding procedure

Localized spherical deconvolution

We provide a new algorithm for the treatment of the deconvolution problem on the sphere which combines the traditional SVD inversion with an appropriate thresholding technique in a well chosen new basis. We establish upper bounds for the behavior of our procedure for any $\mathbb {L}_p$ loss. It is important to emphasize the adaptation properties of our procedures with respect to the regularity (sparsity) of the object to recover as well as to inhomogeneous smoothness. We also perform a numerical study which proves that the procedure shows very promising properties in practice as well.Comment: Published in at http://dx.doi.org/10.1214/10-AOS858 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Zooplankton from Can Giuoc River in Southern Vietnam

In this study, the variables of zooplankton and water quality were investigated in the Can Giuoc River, Southern Vietnam. Zooplankton was monitored in April and September 2015 at 5 sampling sites in the river. Some basic water quality parameters were also tested, including pH, total suspended solid (TSS), dissolved oxygen (DO), biological oxygen demand (BOD5), inorganic nitrogen (NH4+), dissolved phosphorus (PO43-), and coliform. The zooplankton biodiversity indices were applied for the water quality assessment. The results showed that pH ranged from 6.7 to 7.6 during the monitoring. The TSSs were between 34–117 mg/L. The DO and BOD5 were from 0.6 to 3.8 mg/L and from 6.3 to 13.2 mg/L, respectively. The NH4+ and PO43- concentrations ranged from 0.44 to 3.23 and from 0.08 to 1.85 mg/L, respectively. The coliform number was between 9.3x103–9.3x104 MPN/100 mL. The zooplankton analyses showed that there were 31 species of coelenterates, rotatoria, oligochaetes, cladocerans, copepods, ostracods, mysidacea, and 8 larval types. Thereof, the species of copepods were dominant in the species number. The zooplankton density ranged from 9 500 to 23 600 individuals/m3 with the main dominant species of Moina dubia (Cladocera), Thermocyclops hyalinus, Acartia clausi, Oithona similis (Copepoda), and nauplius copepods. The biodiversity index values during the monitoring were from 1.47 to 1.79 characteristic of mesotrophic conditions of the aquatic environment. Besides, the species richness positively correlated with pH, TSS, DO, BOD5, NH4+, PO43-, and coliform, while the zooplankton densities got a positive correlation with DO, BOD5, NH4+, PO43-, and coliform. The results confirmed the advantage of using zooplankton and its indices for water quality assessment

Covariates of turnover intentions of teleworking call center agents in Québec during the COVID-19 pandemic

Les télétravailleurs de plusieurs centres d'appels au Québec ont fourni des données des questionnaires sur leurs diverses demandes au travail (mesurées par les facteurs de stress organisationnels, la charge mentale et la charge émotionnelle), les ressources au travail (mesurées par l'indépendance au travail, la participation et les relations avec les superviseurs) ainsi que pour les mesures des résultats de la satisfaction au travail, de l'engagement organisationnel et de l’intention de quitter. Les hypothèses structurées par le modèle Job Demands-Resources ont été testées à l'aide de méthodes corrélationnelles. Comme prévu, les ressources au travail étaient liées de façon significative à la fois à la satisfaction au travail et à l'engagement organisationnel perçu par l'échantillon. Les demandes au travail prédisaient la satisfaction au travail, mais elles n'étaient pas liées à l'engagement organisationnel. Les implications théoriques et pratiques de ces résultats ont été discutées.Teleworkers from multiple call centers in Québec provided questionnaire data about their various job demands (measured by organizational stressors, mental load, and emotional load), job resources (measured by independence in the work, participation, and relationship with supervisors) as well as for outcome measures of job satisfaction, organizational commitment, and turnover intentions. Hypotheses structured by the JD-R model were tested using correlational methods. As predicted, job resources were significantly related to both job satisfaction and organizational commitment perceived by the sample. Job demands predicted job satisfaction, but they did not relate to organizational commitment. The theoretical and practical implications of these results were discussed

A Primal-Dual Proximal Algorithm for Sparse Template-Based Adaptive Filtering: Application to Seismic Multiple Removal

Unveiling meaningful geophysical information from seismic data requires to deal with both random and structured "noises". As their amplitude may be greater than signals of interest (primaries), additional prior information is especially important in performing efficient signal separation. We address here the problem of multiple reflections, caused by wave-field bouncing between layers. Since only approximate models of these phenomena are available, we propose a flexible framework for time-varying adaptive filtering of seismic signals, using sparse representations, based on inaccurate templates. We recast the joint estimation of adaptive filters and primaries in a new convex variational formulation. This approach allows us to incorporate plausible knowledge about noise statistics, data sparsity and slow filter variation in parsimony-promoting wavelet frames. The designed primal-dual algorithm solves a constrained minimization problem that alleviates standard regularization issues in finding hyperparameters. The approach demonstrates significantly good performance in low signal-to-noise ratio conditions, both for simulated and real field seismic data