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The longitudinal regression model where is the th measurement of the th subject at random time , is the regression function, is a predictable covariate process observed at time and is a noise, is studied in marked point process framework. In this paper we introduce the assumptions which guarantee the consistency and asymptotic normality of smooth -estimator of unknown parameter .
The paper investigates the relation between maximum likelihood and minimum -divergence estimates of unknown parameters and studies the asymptotic behaviour of the likelihood ratio maximum. Observations are assumed to be done in the continuous time.
The paper investigates the relation between maximum likelihood and minimum -divergence estimates of unknown parameters and studies the asymptotic behaviour of the likelihood ratio maximum. Observations are assumed to be done in the discrete time.
In this paper, we study the problem of non parametric estimation of an unknown regression function from dependent data with sub-gaussian errors. As a particular case, we handle the autoregressive framework. For this purpose, we consider a collection of finite dimensional linear spaces (e.g. linear spaces spanned by wavelets or piecewise polynomials on a possibly irregular grid) and we estimate the regression function by a least-squares estimator built on a data driven selected linear space among...
In this paper, we study the problem of non parametric estimation
of an unknown regression function from dependent data with
sub-Gaussian errors. As a particular case, we handle the
autoregressive framework. For this purpose, we consider a
collection of finite dimensional linear spaces (e.g. linear spaces
spanned by wavelets or piecewise polynomials on a possibly
irregular grid) and we estimate the regression function by a
least-squares estimator built on a data driven selected linear
space among...
En este artículo se presenta un análisis comparativo entre los algoritmos más interesantes para la estimación de parámetros de series temporales, tanto en bloque como recursivos. Se propone que los modelos autorregresivos largos constituyen una parametrización general para modelizar series inestables, cuyos parámetros pueden estimarse adecuadamente con algoritmos recursivos, tales como los filtros celosía.
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,...
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.
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...
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