Thèse de doctorat : Università della Svizzera italiana, 2011 ; 2011ECO004.
The goal of this PhD Thesis is the definition of new robust estimators, thereby extending the available theory and exploring new directions for applications in finance. The Thesis contains three papers, which analyze three different types of estimators: M-, Minimum Distance- and R- estimators. The focus is manly of their infinitesimal robustness, but global robustness properties are also...
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In: Journal of multivariate analysis, 2009, vol. 100, no. 9, p. 2126-2136
In the framework of generalized linear models, the nonrobustness of classical estimators and tests for the parameters is a well known problem and alternative methods have been proposed in the literature. These methods are robust and can cope with deviations from the assumed distribution. However, they are based on ¯rst order asymptotic theory and their accuracy in moderate to small samples...
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In: Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2004, vol. 66, no. 4, p. 893–908
Generalized Linear Latent Variable Models (GLLVM), as defined in Bartholomew and Knott (1999) enable modelling of relationships between manifest and latent variables. They extend structural equation modelling techniques, which are powerful tools in the social sciences. However, because of the complexity of the log-likelihood function of a GLLVM, an approximation such as numerical integration must...
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In: Journal of the American Statistical Association, 2005, vol. 100, no. 470, p. 628-641
This paper studies the local robustness of estimators and tests for the conditional location and scale parameters in a strictly stationary time series model. We first derive optimal bounded-influence estimators for such settings under a conditionally Gaussian reference model. Based on these results, optimal bounded-influence versions of the classical likelihood-based tests for parametric...
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Thèse de doctorat : Università della Svizzera italiana, 2004 ; 2004ECO001.
In the first part of the thesis, we study the local robustness of inference procedures of the conditional location and scale parameters in a stationary time series model. We first derive optimal bounded-influence estimators for the parameters of conditional location and scale models under a conditionally Gaussian reference model. Based on these results, optimal bounded-influence versions of the...
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