Prélèvement à la source de l’impôt sur le revenu : comment faire ?

Le gouvernement a décidé de reporter le prélèvement à la source de l’impôt sur le revenu (IR) à janvier 2019. A partir de cette date, les employeurs prélèveront directement l’impôt sur la fiche de paie à un taux transmis par l’administration fiscale. Ce taux sera calculé sur la base de la déclaration fiscale effectuée au printemps 2018 (sur les revenus 2017). En 2019, l’impôt sera ainsi payé sur les revenus 2019. L’avantage principal de la réforme réside dans cette contemporanéité : si les revenus d’un ménage baissent (chômage, départ à la retraite, …), l’impôt baissera proportionnellement[1]. En cas de changement de situation conduisant à une baisse prévisible significative de l’impôt dû, les ménages pourront demander en cours d’année sur le site impots.gouv.fr une mise à jour de leur taux de prélèvement à la source, de sorte que la baisse de l’impôt payé sera plus que proportionnelle. Le prélèvement à la source évite ainsi les difficultés de trésorerie pour les personnes dont la situation change en cours d’année. Du point de vue de l’Etat, le prélèvement à la source permettrait également une plus grande efficacité des stabilisateurs automatiques (l’IR variera en temps réel avec les revenus). [Premier paragraphe

CUSUM Statistical Monitoring of M/M/1 Queues and Extensions

<div><p>Many production and service systems can be modeled as queueing systems. Their operational efficiency and performance are often measured using queueing performance metrics (QPMs), such as average cycle time, average waiting length, and throughput rate. These metrics need to be quantitatively evaluated and monitored in real time to continuously improve the system performance. However, QPMs are often highly stochastic, and hence are difficult to monitor using existing methods. In this article, we propose the cumulative sum (CUSUM) schemes to efficiently monitor the performance of typical queueing systems based on different sampling schemes. We use M/M/1 queues to illustrate how to design the CUSUM chart and compare their performance with several alternative methods. We demonstrate that the performance of CUSUM is superior, responding faster to many shift patterns through extensive numerical studies. We also briefly discuss the extensions of CUSUM charts to more general queues, such as M/G/1, G/G/1, or M/M/c queues. We use case studies to demonstrate the applications of our approach. Supplementary materials for this article are available online.</p></div

Vector status of <i>Aedes</i> species determines geographical risk of autochthonous Zika virus establishment

<div><p>Background</p><p>The 2015-16 Zika virus pandemic originating in Latin America led to predictions of a catastrophic global spread of the disease. Since the current outbreak began in Brazil in May 2015 local transmission of Zika has been reported in over 60 countries and territories, with over 750 thousand confirmed and suspected cases. As a result of its range expansion attention has focused on possible modes of transmission, of which the arthropod vector-based disease spread cycle involving <i>Aedes</i> species is believed to be the most important. Additional causes of concern are the emerging new links between Zika disease and Guillain-Barre Syndrome (GBS), and a once rare congenital disease, microcephaly.</p><p>Methodology/principal findings</p><p>Like dengue and chikungunya, the geographic establishment of Zika is thought to be limited by the occurrence of its principal vector mosquito species, <i>Ae. aegypti</i> and, possibly, <i>Ae. albopictus</i>. While <i>Ae. albopictus</i> populations are more widely established than those of <i>Ae. aegypti</i>, the relative competence of these species as a Zika vector is unknown. The analysis reported here presents a global risk model that considers the role of each vector species independently, and quantifies the potential spreading risk of Zika into new regions. Six scenarios are evaluated which vary in the weight assigned to <i>Ae. albopictus</i> as a possible spreading vector. The scenarios are bounded by the extreme assumptions that spread is driven by air travel and <i>Ae. aegypti</i> presence alone and spread driven equally by both species. For each scenario destination cities at highest risk of Zika outbreaks are prioritized, as are source cities in affected regions. Finally, intercontinental air travel routes that pose the highest risk for Zika spread are also ranked. The results are compared between scenarios.</p><p>Conclusions/significance</p><p>Results from the analysis reveal that if <i>Ae. aegypti</i> is the only competent Zika vector, then risk is geographically limited; in North America mainly to Florida and Texas. However, if <i>Ae. albopictus</i> proves to be a competent vector of Zika, which does not yet appear to be the case, then there is risk of local establishment in all American regions including Canada and Chile, much of Western Europe, Australia, New Zealand, as well as South and East Asia, with a substantial increase in risk to Asia due to the more recent local establishment of Zika in Singapore.</p></div

State space modeling of autocorrelated multivariate Poisson counts

<p>Although many applications involve autocorrelated multivariate counts, there is a scarcity of research on their statistical modeling. To fill this research gap, this article proposes a state space model to describe autocorrelated multivariate counts. The model builds upon the multivariate log-normal mixture Poisson distribution and allows for serial correlations by considering the Poisson mean vector as a latent process driven by a nonlinear autoregressive model. In this way, the model allows for flexible cross-correlation and autocorrelation structures of count data and can also capture overdispersion. The Monte Carlo Expectation Maximization algorithm, together with particle filtering and smoothing methods, provides satisfactory estimators for the model parameters and the latent process variables. Numerical studies show that, compared with other state-of-the-art models, the proposed model has superiority and more generality with respect to describing count data generated from different mechanisms of the process of counts. Finally, we use this model to analyze counts of different types of damage collected from a power utility system as a case study. Supplementary materials are available for this article. Go to the publisher’s online edition of <i>IISE Transactions</i> for additional tables and figures.</p

Monitoring wafers’ geometric quality using an additive Gaussian process model

<div><p>ABSTRACT</p><p>The geometric quality of a wafer is an important quality characteristic in the semiconductor industry. However, it is difficult to monitor this characteristic during the manufacturing process due to the challenges created by the complexity of the data structure. In this article, we propose an Additive Gaussian Process (AGP) model to approximate a standard geometric profile of a wafer while quantifying the deviations from the standard when a manufacturing process is in an in-control state. Based on the AGP model, two statistical tests are developed to determine whether or not a newly produced wafer is conforming. We have conducted extensive numerical simulations and real case studies, the results of which indicate that our proposed method is effective and has potentially wide application.</p></div

Modeling Regression Quantile Process Using Monotone B-Splines

<p>Quantile regression as an alternative to conditional mean regression (i.e., least-square regression) is widely used in many areas. It can be used to study the covariate effects on the entire response distribution by fitting quantile regression models at multiple different quantiles or even fitting the entire regression quantile process. However, estimating the regression quantile process is inherently difficult because the induced conditional quantile function needs to be monotone at all covariate values. In this article, we proposed a regression quantile process estimation method based on monotone B-splines. The proposed method can easily ensure the validity of the regression quantile process and offers a concise framework for variable selection and adaptive complexity control. We thoroughly investigated the properties of the proposed procedure, both theoretically and numerically. We also used a case study on wind power generation to demonstrate its use and effectiveness in real problems. Supplementary materials for this article are available online.</p

A Distribution-Free Multivariate Control Chart

<div><p>Monitoring multivariate quality variables or data streams remains an important and challenging problem in statistical process control (SPC). Although the multivariate SPC has been extensively studied in the literature, designing distribution-free control schemes are still challenging and yet to be addressed well. This paper develops a new nonparametric methodology for monitoring location parameters when only a small reference dataset is available. The key idea is to construct a series of conditionally distribution-free test statistics in the sense that their distributions are free of the underlying distribution given the empirical distribution functions. The conditional probability that the charting statistic exceeds the control limit at present given that there is no alarm before the current time point can be guaranteed to attain a specified false alarm rate. The success of the proposed method lies in the use of data-dependent control limits, which are determined based on the observations on-line rather than decided before monitoring. Our theoretical and numerical studies show that the proposed control chart is able to deliver satisfactory in-control run-length performance for any distributions with any dimension. It is also very efficient in detecting multivariate process shifts when the process distribution is heavy-tailed or skewed. Supplementary materials for this article are available online.</p></div

Interplay between Crystallization and Phase Separation in PS‑<i>b</i>‑PMMA/PEO Blends: The Effect of Confinement

Interplay between phase separation and crystallization under confinement for the blends of PEO homopolymers with different molecular weight and PS-<i>b</i>-PMMA block copolymer is studied. Phase structures of the blends are investigated by atomic force microscope (AFM) and theoretically simulated by the dissipative particle dynamics (DPD) method, and a phase diagram describing the phase structure is established. Low molecular weight PEO (PEO2) disperses uniformly in the PMMA block domain and causes a transition from cylinder phase to perforated lamellar phase, while high molecular weight PEO (PEO20) causes expansion of the cylinder domains and formation of disordered domains. Crystallization and melting behavior of the blends are detected by differential scanning calorimetry (DSC). The results show the liquid–liquid phase separation between PEO homopolymer and PMMA block under PS-<i>b</i>-PMMA microphase-separated structure is suppressed due to the hard confinement caused by glassy PS block. As a result, in the blends of PS-<i>b</i>-PMMA/PEO2, PEO2 is unable to crystallize, and in the blends of PS-<i>b</i>-PMMA/PEO20, PEO20 shows a more obvious melting point depression compared with the homopolymer blends of PMMA/PEO20

Kinetic studies for nitrate adsorption on granular chitosan–Fe(III) complex

<p>In this study, a generalized kinetic equation was proposed to simulate adsorption behaviors in batch systems and several useful kinetic equations were deduced. The results indicated that the amount of nitrate uptake increased rapidly in the initial stage, followed by a slower process until adsorption equilibrium was reached after approximately 1.5 h. The rate constant was a function of the initial nitrate concentration. The adsorption and desorption rate constants quantitatively reflected the adsorption and desorption reactions at the solid/solution interface. The adsorption and desorption processes for nitrate adsorption followed identical reaction order. The kinetic parameters (adsorption and desorption rate constants, half-time and instantaneous rate) provided by these kinetic equations are of significant importance for the understanding of adsorption mechanisms.</p