Joseph Ngatchou-Wandji

Work-related asthma in France: recent trends for the period 2001–2009

Objective Knowledge on the time-course (trends) of work-related asthma (WRA) remains sparse. The aim of this study was to describe WRA trends in terms of industrial activities and the main causal agents in France over the period 2001–2009. Method Data were collected from the French national network of occupational health surveillance and prevention (Réseau National de Vigilance et de Prévention des Pathologies Professionnelles (RNV3P)). Several statistical models (non-parametric test, zero-inflated negative binomial, logistic regression and time-series models) were used and compared with assess trends. Results Over the study period, 2914 WRA cases were included in the network. A significant decrease was observed overall and for some agents such as isocyanates (p=0.007), aldehydes (p=0.01) and latex (p=0.01). Conversely, a significant increase was observed for cases related to exposure to quaternary ammonium compounds (p=0.003). The health and social sector demonstrated both a growing number of cases related to the use of quaternary ammonium compounds and a decrease of cases related to aldehyde and latex exposure. Conclusions WRA declined in France over the study period. The only significant increase concerned WRA related to exposure to quaternary ammonium compounds. Zero-inflated negative binomial and logistic regression models appear to describe adequately these data.

Testing Symmetry of the Error Distribution in Nonlinear Heteroscedastic Models

Using the empirical characteristic function, we derive a Cramér-von Mises test for symmetry of the error distribution in a class of nonlinear parametric heteroscedastic models. We study the convergence of the residual-based empirical distribution function. We establish a functional limit theorem for an empirical process of residuals, and investigate the asymptotic null distribution function of our test statistic. A simulation experiment is conducted to evaluate small-sample performances of our test.

Estimation in a class of nonlinear heteroscedastic time series models

Parameter estimation in a class of heteroscedastic time series models is investigated. The existence of conditional least-squares and conditional likelihood estimators is proved. Their consistency and their asymptotic normality are established. Kernel estimators of the noises density and its derivatives are defined and shown to be uniformly consistent. A simulation experiment conducted shows that the estimators perform well for large sample size.

Weak convergence of some marked empirical processes: Application to testing heteroscedasticity

We present a procedure for testing the goodness-of-fit of the conditional variance function of a Markov model of order 1, under stationarity and ergodicity. The autoregressive parameter, the distribution of the noise and the stationary distribution of the observations are assumed to be unknown. Under the null hypothesis H 0 that the conditional variance function belongs to a class of parametric functions, we define an estimator {\tilde{\theta}}_{n} of 0 , the assumed true parameter, and we establish its consistency and asymptotic normality. We define a marked empirical process A n (), for which we state and prove a functional limit theorem under H 0 . The asymptotic behavior of this process is studied under fixed alternatives H 1 . Based on the process A n (), a chi-squared test is derived. Simulation experiments show that the test is powerful against some heteroscedastic time series models.