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Fast and Robust Bootstrap
 STATISTICAL METHODS AND APPLICATIONS
"... In this paper we review recent developments on a bootstrap method for robust estimators which is computationally faster and more resistant to outliers than the classical bootstrap. This fast and robust bootstrap method is, under reasonable regularity conditions, asymptotically consistent. We describ ..."
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In this paper we review recent developments on a bootstrap method for robust estimators which is computationally faster and more resistant to outliers than the classical bootstrap. This fast and robust bootstrap method is, under reasonable regularity conditions, asymptotically consistent. We describe the method in general and then consider its application to perform inference based on robust estimators for the linear regression and multivariate locationscatter models. In particular, we study confidence and prediction intervals and tests of hypotheses for linear regression models, inference for locationscatter parameters and principal components, and classification error estimation for discriminant analysis.
Robust Regression in Stata
 The Stata Journal
, 2009
"... In regression analysis, the presence of outliers in the data set can strongly distort the classical least squares estimator and lead to unreliable results. To deal with this, several robusttooutliers methods have been proposed in the statistical literature. In Stata, some of these methods are avai ..."
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Cited by 8 (1 self)
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In regression analysis, the presence of outliers in the data set can strongly distort the classical least squares estimator and lead to unreliable results. To deal with this, several robusttooutliers methods have been proposed in the statistical literature. In Stata, some of these methods are available through the commands rreg and qreg. Unfortunately, these methods only resist to some specific types of outliers and turn out to be ineffective under alternative scenarios. In this paper we present more effective robust estimators that we implemented in Stata. We also present a graphical tool that allows recognizing the type of existing outliers.
Do risk preferences change? Evidence from panel data before and after the great east Japan earthquake
, 2014
"... Behavior and Socioeconomic Dynamics ” (investigators: Yoshiro Tsutsui, Fumio Ohtake, and Shinsuke Ikeda). The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and co ..."
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Cited by 3 (0 self)
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Behavior and Socioeconomic Dynamics ” (investigators: Yoshiro Tsutsui, Fumio Ohtake, and Shinsuke Ikeda). The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peerreviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.
On the Use of Robust Regression in Econometrics
, 2009
"... Note: The Discussion Papers in this series are prepared by members of the Department of Economics, University of Essex, for private circulation to interested readers. They often represent preliminary reports on work in progress and should therefore be neither quoted nor referred to in published work ..."
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Note: The Discussion Papers in this series are prepared by members of the Department of Economics, University of Essex, for private circulation to interested readers. They often represent preliminary reports on work in progress and should therefore be neither quoted nor referred to in published work without the written consent of the author. On the use of robust regression in econometrics ∗
SaP2A1.8 TopographyTimeFrequency Atomic Decomposition for EventRelated M/EEG Signals
"... Abstract — We present a method for decomposing MEG or EEG data (channel × time × trials) into a set of atoms with fixed spatial and timefrequency signatures. The spatial part (i.e., topography) is obtained by independent component analysis (ICA). We propose a frequency prewhitening procedure as a p ..."
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Abstract — We present a method for decomposing MEG or EEG data (channel × time × trials) into a set of atoms with fixed spatial and timefrequency signatures. The spatial part (i.e., topography) is obtained by independent component analysis (ICA). We propose a frequency prewhitening procedure as a preprocessing step before ICA, which gives access to high frequency activity. The timefrequency part is obtained with a novel iterative procedure, which is an extension of the matching pursuit procedure. The method is evaluated on a simulated dataset presenting both lowfrequency evoked potentials and highfrequency oscillatory activity. We show that the method is able to recover well both lowfrequency and highfrequency simulated activities. There was however crosstalk across some recovered components due to the correlation introduced in the simulation. I.
unknown title
"... 2First I would like to thank my thesis adviser Prof. Werner Stahel for his constant help, advice, support, and mentorship during my work. Many thanks to the whole Seminar für Statistik’s staff for their kindness and for their assistance in every moment. I also would like to thank Thomas Rast for th ..."
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2First I would like to thank my thesis adviser Prof. Werner Stahel for his constant help, advice, support, and mentorship during my work. Many thanks to the whole Seminar für Statistik’s staff for their kindness and for their assistance in every moment. I also would like to thank Thomas Rast for the many corrections and suggestions. Finally I express my gratitude to my parents, my siblings, my friends and all the people that have supported me anyhow throughout my studies at ETH. 3Abstract Analyzing data using statistical methods means to break reality down to a mathematical framework, a model. Often this model is based on strong assumptions, for example normally distributed data. Classical statistics provides methods that fit the chosen model perfectly. But in reality the model assumptions usually hold only approximately. Anomalies and untrue assumptions might render the statistical analysis useless. Robust statistics aims for methods that are based on weaker assumptions and
ETH Zurich
, 2012
"... An outlierrobust extreme bounds analysis of the determinants of healthcare expenditure growth ..."
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An outlierrobust extreme bounds analysis of the determinants of healthcare expenditure growth
International Workshop on Robust Statistics and R Claudio Agostinelli (Universita ̀ Ca’Foscari di Venezia),
, 2007
"... Robust Statistics deals with a pressing problem in statistical applications: many classical statistical methods work well only for highquality data that can be modelled adequately. In many practical applications, however, this is the exception than the rule. When the sample size is moderately smal ..."
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Robust Statistics deals with a pressing problem in statistical applications: many classical statistical methods work well only for highquality data that can be modelled adequately. In many practical applications, however, this is the exception than the rule. When the sample size is moderately small, the sampling variability (i.e. the variation induced by the random nature of the sample obtained from the underlying population) may dominate the error produced from possible model misspecifications. However, the sampling variance decreases when the sample size increases, and for large data sets this variance can be very small, and the overall error may be mainly due to the systematic bias of model misspecification, which is not reduced by larger sample sizes. In recent years, we have seen an enormous increase in the amount of data being modelled and analyzed. For example automated electronic data collection, and complex data sets, produce data sets for which both the number of cases and variables much exceed the orders of magnitude that were routine only a decade ago. Two problems may arise when analysing these large and complex data sets with classical statistical methodologies: (a) it may not be easy to fit simple and parsimonious models that reflect equally well all the data; and (b) the sampling variability for such large datasets can be very small, to the extent that, as mentioned above, the possible model misspecification bias (which, unlike the variance of most estimators,
Uniform asymptotics for S and MMregression estimators Running title: “Uniform asymptotics for robust regression”
, 2008
"... In this paper we find verifiable regularity conditions to ensure that Sestimators of scale and regression and MMestimators of regression are uniformly consistent and uniformly asymptotically normally distributed over contamination neighbourhoods. Moreover, we show how to calculate the size of the ..."
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In this paper we find verifiable regularity conditions to ensure that Sestimators of scale and regression and MMestimators of regression are uniformly consistent and uniformly asymptotically normally distributed over contamination neighbourhoods. Moreover, we show how to calculate the size of these neighbourhoods. In particular, we find that, for MMestimators computed with Tukey’s family of bisquare score functions, there is a tradeoff between the size of these neighbourhoods and both the breakdown point of the Sestimators and the leverage of the contamination that is allowed in the neighbourhood. These results extend previous work of SalibianBarrera and Zamar for locationscale to the linear regression model. Key words: Robustness, robust inference, uniform asymptotics, robust regression. 1
Vincenzo Verardi is Associated Researcher of the FNRS and gratefully acknowledges their finacial support
"... Abstract. In regression analysis, the presence of outliers in the data set can strongly distort the classical least squares estimator and lead to unreliable results. To deal with this, several robusttooutliers methods have been proposed in the statistical literature. In Stata, some of these metho ..."
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Abstract. In regression analysis, the presence of outliers in the data set can strongly distort the classical least squares estimator and lead to unreliable results. To deal with this, several robusttooutliers methods have been proposed in the statistical literature. In Stata, some of these methods are available through the commands rreg and qreg. Unfortunately, these methods only resist to some specific types of outliers and turn out to be ineffective under alternative scenarios. In this paper we present more effective robust estimators that we implemented in Stata. We also present a graphical tool that allows recognizing the type of detected outliers.