Add to ORKGIn modern statistics, the robust estimation of parameters of a regression hyperplane is a central problem. Robustness means that the estimation is not or only slightly affected by outliers in the data. In this paper, it is shown that the following robust estimators are hard to compute: LMS, LQS, LTS, LTA, MCD, MVE, Constrained M estimator, Projection Depth (PD) and Stahel-Donoho. In addition, a data set is presented such that the ltsReg-procedure of R has probability less than 0.0001 of finding a correct answer. Furthermore, it is described, how to design new robust estimators. --Computational statistics,complexity theory,robust statistics,algorithms,search heuristics
We propose a new procedure for computing an approximation to regression estimates based on the minim...
Many datasets are collected automatically, and are thus easily contaminated by outliers. In order to...
One of the most commonly encountered tasks in computer vision is the estimation of model parameters ...
In modern statistics, the robust estimation of parameters of a re- gression hyperplane is a central...
A drawback of robust statistical techniques is the increased computational effort often needed comp...
Presented on October 31, 2016 at 11:00 a.m. in the Klaus Advanced Computing Building, Room 1116EAnku...
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer...
International audienceThis paper deals with robust regression and subspace estimation and more preci...
In this article, we consider a large class of computational problems in robust statistics that can b...
Data sets with millions of observations occur nowadays in different areas. An insurance company or a...
Recent work on robust estimation has led to many procedures, which are easy to formulate and straigh...
Real-world datasets are often characterised by outliers; data items that do not follow the same stru...
We herein propose a new robust estimation method based on random projections that is adaptive and au...
A preeminent expert in the field explores new and exciting methodologies in the ever-growing field o...
Statistical learning theory aims at providing a better understanding of the statistical properties ...
We propose a new procedure for computing an approximation to regression estimates based on the minim...
Many datasets are collected automatically, and are thus easily contaminated by outliers. In order to...
One of the most commonly encountered tasks in computer vision is the estimation of model parameters ...
In modern statistics, the robust estimation of parameters of a re- gression hyperplane is a central...
A drawback of robust statistical techniques is the increased computational effort often needed comp...
Presented on October 31, 2016 at 11:00 a.m. in the Klaus Advanced Computing Building, Room 1116EAnku...
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer...
International audienceThis paper deals with robust regression and subspace estimation and more preci...
In this article, we consider a large class of computational problems in robust statistics that can b...
Data sets with millions of observations occur nowadays in different areas. An insurance company or a...
Recent work on robust estimation has led to many procedures, which are easy to formulate and straigh...
Real-world datasets are often characterised by outliers; data items that do not follow the same stru...
We herein propose a new robust estimation method based on random projections that is adaptive and au...
A preeminent expert in the field explores new and exciting methodologies in the ever-growing field o...
Statistical learning theory aims at providing a better understanding of the statistical properties ...
We propose a new procedure for computing an approximation to regression estimates based on the minim...
Many datasets are collected automatically, and are thus easily contaminated by outliers. In order to...
One of the most commonly encountered tasks in computer vision is the estimation of model parameters ...