AbstractIn this paper a general method of constructing robust quasi-likelihood estimating functions for discrete time stochastic processes is given. Examples of a regression model with autoregressive errors and a general contamination model are presented to illustrate the methodology. The loss of efficiency involved in robustification is also discussed
Abstract. In this paper we consider some aspects of quasi-likelihood methods used in estimation of p...
It is shown that, for discrete-time processes, both the causal minimum variance estimate of an arbit...
By starting from a natural class of robust estimators for generalized linear models based on the not...
AbstractThe comparison of competing estimating functions for a vector parameter of a stochastic proc...
The comparison of competing estimating functions for a vector parameter of a stochastic process is d...
In this article we consider the nonparametric robust estimation problem for regression models in con...
International audienceThe robust filtering problem for uncertain discrete-time systems is treated in...
Abstract—Estimation in conventional signal processing is often based on strong assumptions on the pr...
Consider an ergodic Markov chain on the real line, with parametric models for the conditional mean a...
We address the problem of finding robust sampling designs for the estimation of a discrete time seco...
International audienceThe paper considers the problem of estimating a periodic function in a continu...
This paper is concerned with a polynomial approach to robust deconvolution filtering of linear discr...
AbstractA result of Godambe [1] on optimal combination of estimating functions for discrete time sto...
AbstractWe consider robust operations in time series. We present a definition and subsequent qualita...
The theory of linear filtering of stochastic processes provides continuous time analogues of finite-...
Abstract. In this paper we consider some aspects of quasi-likelihood methods used in estimation of p...
It is shown that, for discrete-time processes, both the causal minimum variance estimate of an arbit...
By starting from a natural class of robust estimators for generalized linear models based on the not...
AbstractThe comparison of competing estimating functions for a vector parameter of a stochastic proc...
The comparison of competing estimating functions for a vector parameter of a stochastic process is d...
In this article we consider the nonparametric robust estimation problem for regression models in con...
International audienceThe robust filtering problem for uncertain discrete-time systems is treated in...
Abstract—Estimation in conventional signal processing is often based on strong assumptions on the pr...
Consider an ergodic Markov chain on the real line, with parametric models for the conditional mean a...
We address the problem of finding robust sampling designs for the estimation of a discrete time seco...
International audienceThe paper considers the problem of estimating a periodic function in a continu...
This paper is concerned with a polynomial approach to robust deconvolution filtering of linear discr...
AbstractA result of Godambe [1] on optimal combination of estimating functions for discrete time sto...
AbstractWe consider robust operations in time series. We present a definition and subsequent qualita...
The theory of linear filtering of stochastic processes provides continuous time analogues of finite-...
Abstract. In this paper we consider some aspects of quasi-likelihood methods used in estimation of p...
It is shown that, for discrete-time processes, both the causal minimum variance estimate of an arbit...
By starting from a natural class of robust estimators for generalized linear models based on the not...
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