Add to ORKGWe herein propose a new robust estimation method based on random pro-jections that is adaptive and, automatically produces a robust estimate, while enabling easy computations for high or infinite dimensional data. Under some re-stricted contamination models, the procedure is robust and attains full efficiency. We tested the method using both simulated and real data
Statistical inference using machine learning techniques may be difficult with small datasets because...
Outliers in the data are a common problem in applied statistics. Estimators that give reliable resul...
Recent advances in technologies for cheaper and faster data acquisition and storage have led to an e...
We herein propose a new robust estimation method based on random projections that is adaptive and au...
Presented on October 31, 2016 at 11:00 a.m. in the Klaus Advanced Computing Building, Room 1116EAnku...
In every corner of machine learning and statistics, there is a need for estimators that work not jus...
Data sets with millions of observations occur nowadays in different areas. An insurance company or a...
This paper presents a novel robust adaptive filtering scheme based on the interactive use of statist...
Randomly censored survival data appear in a wide variety of applications in which the time until the...
Data sets where the number of variables p is comparable to or larger than the number of observations...
A preeminent expert in the field explores new and exciting methodologies in the ever-growing field o...
We consider the problem of constrained M-estimation when both explanatory and response variables hav...
We propose straightforward nonparametric estimators for the mean and the covariance functions of fun...
International audienceUsing Renyi pseudodistances, new robustness and efficiency measures are define...
We study the problem of performing statistical inference based on robust estimates when the distrib...
Statistical inference using machine learning techniques may be difficult with small datasets because...
Outliers in the data are a common problem in applied statistics. Estimators that give reliable resul...
Recent advances in technologies for cheaper and faster data acquisition and storage have led to an e...
We herein propose a new robust estimation method based on random projections that is adaptive and au...
Presented on October 31, 2016 at 11:00 a.m. in the Klaus Advanced Computing Building, Room 1116EAnku...
In every corner of machine learning and statistics, there is a need for estimators that work not jus...
Data sets with millions of observations occur nowadays in different areas. An insurance company or a...
This paper presents a novel robust adaptive filtering scheme based on the interactive use of statist...
Randomly censored survival data appear in a wide variety of applications in which the time until the...
Data sets where the number of variables p is comparable to or larger than the number of observations...
A preeminent expert in the field explores new and exciting methodologies in the ever-growing field o...
We consider the problem of constrained M-estimation when both explanatory and response variables hav...
We propose straightforward nonparametric estimators for the mean and the covariance functions of fun...
International audienceUsing Renyi pseudodistances, new robustness and efficiency measures are define...
We study the problem of performing statistical inference based on robust estimates when the distrib...
Statistical inference using machine learning techniques may be difficult with small datasets because...
Outliers in the data are a common problem in applied statistics. Estimators that give reliable resul...
Recent advances in technologies for cheaper and faster data acquisition and storage have led to an e...