Modèles de séries chronologiques avec seuils
Modèles statistiques et systèmes standards de processus de vraisemblance
Modeling seasonal time series.
Modélisation statistique de la température de surface de la mer
Modelización de datos longitudinales con estructuras de covarianza no estacionarias: modelos de coeficientes aleatorios frente a modelos alternativos.
Un tema que ha suscitado el interés de los investigadores en datos longitudinales durante las dos últimas décadas, ha sido el desarrollo y uso de modelos paramétricos explícitos para la estructura de covarianza de los datos. Sin embargo, el análisis de estructuras de covarianza no estacionarias en el contexto de datos longitudinales no se ha realizado de forma detallada principalmente debido a que las distintas aplicaciones no hacían necesario su uso. Muchos son los modelos propuestos recientemente,...
Modelling financial time series using reflections of copulas
We have intensified studies of reflections of copulas (that we introduced recently in [6]) and found that their convex combinations exhibit potentially useful fitting properties for original copulas of the Normal, Frank, Clayton and Gumbel types. We show that these properties enable us to construct interesting models for the relations between investment in stocks and gold.
Modelling stock returns with AR-GARCH processes.
Financial returns are often modelled as autoregressive time series with random disturbances having conditional heteroscedastic variances, especially with GARCH type processes. GARCH processes have been intensely studied in financial and econometric literature as risk models of many financial time series. Analyzing two data sets of stock prices we try to fit AR(1) processes with GARCH or EGARCH errors to the log returns. Moreover, hyperbolic or generalized error distributions occur to be good models...
Modelos GARCH e TARCH - estacionaridade forte, estacionaridade fraca, ergodicidade e comportamento limite do agregado temporal
Moderate deviations for stable Markov chains and regression models.
Moderate deviations for the Durbin–Watson statistic related to the first-order autoregressive process
The purpose of this paper is to investigate moderate deviations for the Durbin–Watson statistic associated with the stable first-order autoregressive process where the driven noise is also given by a first-order autoregressive process. We first establish a moderate deviation principle for both the least squares estimator of the unknown parameter of the autoregressive process as well as for the serial correlation estimator associated with the driven noise. It enables us to provide a moderate deviation...
Moindres carrés pondérés et poursuite
Moment estimation methods for stationary spatial Cox processes - A comparison
In the present paper we consider the problem of fitting parametric spatial Cox point process models. We concentrate on the moment estimation methods based on the second order characteristics of the point process in question. These methods represent a simulation-free faster-to-compute alternative to the computationally intense maximum likelihood estimation. We give an overview of the available methods, discuss their properties and applicability. Further we present results of a simulation study in...
Moment estimation of customer loss rates from transactional data.
Monte Carlo simulation of Markov chain steady-state distribution.
Moyennes mobiles centrées et non-centrées. Construction et comparaison
MSE of the best linear predictor in nonorthogonal models
Multidimensional Hermite Polynomials of Complex Gaussian Variables
Multidimensional Hermite polynomials of complex Gaussian variables.
Multidimensional limit theorems for smoothed extreme value estimates of point processes boundaries
In this paper, we give sufficient conditions to establish central limit theorems and moderate deviation principle for a class of support estimates of empirical and Poisson point processes. The considered estimates are obtained by smoothing some bias corrected extreme values of the point process. We show how the smoothing permits to obtain Gaussian asymptotic limits and therefore pointwise confidence intervals. Some unidimensional and multidimensional examples are provided.
Multidimensional random processes with normal covariances