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 noise's 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.Comment: Published in at http://dx.doi.org/10.1214/07-EJS157 the Electronic Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of Mathematical Statistics (http://www.imstat.org

A Nonparametric Goodness-of-fit Test for a Class of Parametric Autoregressive Models

We derive a nonparametric test for discriminating between generalized autoregressive models. This test is based on a suitably normalized sum of residuals. The null distribution and the power of the test under both fixed and a sequence of local alternatives are studied under mild stationarity and $\alpha$--mixing conditions. This procedure can be applied to testing linear models against nonlinear models or certain nonlinear models against others. Numerical simulations show that the proposed test is powerful against most of the alternatives considered

Bayesian Modelling of PWR Vessels Flaw Distributions

We present a full Bayesian method for estimating the density and size distribution of subclad-flaws in French Pressurized Water Reactor (PWR) vessels. This model takes into account in service inspection (ISI) data, a flaw size-dependent probability of detection function (different function types are possible) with a threshold of detection, and a flaw sizing error distribution (different distribution types are possible). It is identified through a Markov Chain Monte Carlo (MCMC) algorithm. The article includes discussion for choosing the prior distribution parameters and an illustrative application is presented highlighting its ability to provide good parameter estimates even when a small number of flaws is observed

A Nonparametric Goodness-of-fit Test for a Class of Parametric Autoregressive Models

: We derive a nonparametric test for discriminating between generalized autoregressive models. This test is based on a suitably normalized sum of residuals. The null distribution and the power of the test under both fixed and a sequence of local alternatives are studied under mild stationarity and ff--mixing conditions. This procedure can be applied to testing linear models against nonlinear models or certain nonlinear models against others. Numerical simulations show that the proposed test is powerful against most of the alternatives considered. Key-words: Autoregressive models, contiguity, goodness--of--fit tests, mixing, nonlinear models, nonparametric methods. (R'esum'e : tsvp) Unite de recherche INRIA Rhone-Alpes 655, avenue de l'Europe, 38330 MONTBONNOT ST MARTIN (France) Telephone : (33) 04 76 61 52 00 -- Telecopie : (33) 04 76 61 52 52 Un test d'ad'equation pour une classe param'etrique de processus autor'egressifs R'esum'e : Nous proposons un test non param'etrique pour di..

Nonparametric Estimation of the Density Function of the Distribution of the Noise in CHARN Models

This work is concerned with multivariate conditional heteroscedastic autoregressive nonlinear (CHARN) models with an unknown conditional mean function, conditional variance matrix function and density function of the distribution of noise. We study the kernel estimator of the latter function when the former are either parametric or nonparametric. The consistency, bias and asymptotic normality of the estimator are investigated. Confidence bound curves are given. A simulation experiment is performed to evaluate the performance of the results

Testing for conditional symmetry in absolutely regular and possibly nonstationary dynamical models

International audienc

Power of the Lagrange multiplier test for certain subdiagonal bilinear models

International audienceWe investigate the local power of the Lagrange multiplier test against a sequence of bilinear alternatives contiguous to the null hypothesis. Simulation experiments show that the experimental power agrees with the theoretical power, and that these powers are good

On the zero-inflated count models with application to modelling annual trends in incidences of some occupational allergic diseases in France

International audienc

Recent Tests for Symmetry with Multivariate and Structured Data: A Review

International audienc