1,721,338 research outputs found
Robust estimators of the generalized loggamma distribution
Full text linkWe propose robust estimators of the generalized log-gamma distribution and, more generally, of location-shape-scale families of distributions. A (weighted) Qτ estimator minimizes a τ scale of the differences between empirical and theoretical quantiles. It is n1/2 consistent; unfortunately, it is not asymptotically normal and, therefore, inconvenient for inference. However, it is a convenient starting point for a one-step weighted likelihood estimator, where the weights are based on a disparity measure between the model density and a kernel density estimate. The one-step weighted likelihood estimator is asymptotically normal and fully efficient under the model. It is also highly robust under outlier contamination. Supplementary materials are available online.Fil: Agostinelli, Claudio. Universita' Ca' Foscari Di Venezia; ItaliaFil: Marazzi, Alfio Natale. Universite de Lausanne; SuizaFil: Yohai, Victor Jaime. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Matemática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin
Costruzione e valutazione statistica di indici ambientali sintetici
No full textTesi di Laurea in Scienze Statistiche ed Economiche, Facolta' di Scienze Statistiche, PADOV
wle: A Package for Robust Statistics using Weighted Likelihood
No full texthttp://cran.r-project.org/doc/Rnew
Stato di Salute della Popolazione Veneta. Indagine 1986--87 dell'ISTAT
No full textTesi di Diploma in Statistica, Facolta' di Scienze Statistiche, Demografiche ed Attuariali, PADOV
Notes on Pearson residuals and weighted likelihood estimating equations
No full textIn these notes we show that the Pearson residuals (PR) [Lindsay, B.G., 1994. Efficiency versus robustness: the case for minimum Hellinger distance and related methods. Ann. Statist. 22, 1018-1114.] have a natural asymptotic lower bound under the gross error model which can be used in the problem of choosing a root when multiple roots are present in the weighted likelihood estimating equations. Further, we show through an example how the minimum of the PR plays an important role in the robust estimation approach based on weighted likelihood
Estimating the model of the majority of the data
No full textIn the last years attention has been devoted to the construction of estimators that (optimally) bound the bias (and/or the variance) under the assumption that the data are in a neighborhood of the assumed model. These estimators are often referred to as able to recognize the model of the majority of the data under contamination. We formalize the concept of the “model of the majority of the data” and we study the conditions under which this model is unique.
We provide simple examples where minimum max bias estimators fail to recognize the model of the majority of the data. A quantitative measure of robustness is introduced with the aim of evaluating the estimators regarding this aspect. We define estimators which are asymptotically optimal with respect to this criterion. For completeness of the presentation, we study their maxbias functions, breakdowns and asymptotic distributions. Simulations and examples are provided throughout to illustrate the theoretical results and the performance of the estimators
Estimating the model of the majority of the data
No full textSerie Redazioni Provvisorie del Dipartimento di Statistica, Universita' Ca' Foscar
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