We herein propose a new robust estimation method based on random projections that is adaptive and automatically produces a robust estimate, while enabling easy computations for high or infinite dimensional data. Under some restricted contamination models, the procedure is robust and attains full efficiency. We tested the method using both simulated and real data.Fil: Fraiman, Jacob Ricardo. Universidad de San Andrés; Argentina. Universidad de la República; Uruguay. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Svarc, Marcela. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés; Argentin
We study the problem of performing statistical inference based on robust estimates when the distrib...
In many situations, data are recorded over a period of time and may be regarded as realizations of...
Outliers in the data are a common problem in applied statistics. Estimators that give reliable resul...
We herein propose a new robust estimation method based on random pro-jections that is adaptive and, ...
A preeminent expert in the field explores new and exciting methodologies in the ever-growing field o...
The research reported in this thesis describes a new algorithm which can be used to robustify statis...
Presented on October 31, 2016 at 11:00 a.m. in the Klaus Advanced Computing Building, Room 1116EAnku...
A powerful procedure for outlier detection and robust estimation of shape and location with multivar...
Beran (2003) defined statistics as the study of algorithms for data analysis. In many situations se...
Random projections is a technique used primarily in dimension reduction, in order to estimate distan...
Recent work on robust estimation has led to many procedures, which are easy to formulate and straigh...
In modern statistics, the robust estimation of parameters of a regression hyperplane is a central pr...
International audienceSome recent contributions to robust inference are presented. Firstly, the clas...
Includes bibliography.This study initially set out to consider the possibility of constructing an ad...
We propose a new procedure for computing an approximation to regression estimates based on the minim...
We study the problem of performing statistical inference based on robust estimates when the distrib...
In many situations, data are recorded over a period of time and may be regarded as realizations of...
Outliers in the data are a common problem in applied statistics. Estimators that give reliable resul...
We herein propose a new robust estimation method based on random pro-jections that is adaptive and, ...
A preeminent expert in the field explores new and exciting methodologies in the ever-growing field o...
The research reported in this thesis describes a new algorithm which can be used to robustify statis...
Presented on October 31, 2016 at 11:00 a.m. in the Klaus Advanced Computing Building, Room 1116EAnku...
A powerful procedure for outlier detection and robust estimation of shape and location with multivar...
Beran (2003) defined statistics as the study of algorithms for data analysis. In many situations se...
Random projections is a technique used primarily in dimension reduction, in order to estimate distan...
Recent work on robust estimation has led to many procedures, which are easy to formulate and straigh...
In modern statistics, the robust estimation of parameters of a regression hyperplane is a central pr...
International audienceSome recent contributions to robust inference are presented. Firstly, the clas...
Includes bibliography.This study initially set out to consider the possibility of constructing an ad...
We propose a new procedure for computing an approximation to regression estimates based on the minim...
We study the problem of performing statistical inference based on robust estimates when the distrib...
In many situations, data are recorded over a period of time and may be regarded as realizations of...
Outliers in the data are a common problem in applied statistics. Estimators that give reliable resul...