robust statistics

David Olive's site contains course notes on robust statistics and some data sets.

Indeed, it is one of the least efficient and least robust statistics.

It was used by Peter Huber and others working on robust statistics.

Other loss functions are used in statistical theory, particularly in robust statistics.

The survey response rate has consistently been 100%, which implies robust statistics are being produced as no imputation is required.

Improving isochron calculations with robust statistics and the bootstrap.

M-estimator, an approach used in robust statistics.

As such, confidence intervals as discussed below are not robust statistics, though changes can be made to add robustness.

Brian Ripley's robust statistics course notes.

Because communities naturally vary as do samples collected from a larger population, identifying robust statistics with acceptable variance is an area of active research.

The Huber loss function is used in robust statistics, M-estimation and additive modelling.

The sample maximum and minimum are the least robust statistics: they are maximally sensitive to outliers.

Thus, in the context of robust statistics, distributionally robust and outlier-resistant are effectively synonymous.

Robust Statistics, Peter.

However, they are inefficient, and in modern robust statistics M-estimators are preferred, though these are much more difficult computationally.

Therefore, as a welcome by-product, the theory also provides a formal framework for models used in robust statistics and non-parametric statistics.

They thus are useful in robust statistics, as descriptive statistics, in statistics education, and when computation is difficult.

Robust Statistics: the Approach Based on Influence Functions.

In robust statistics, more importance is placed on robustness and applicability to a wide variety of distributions, rather than efficiency on a single distribution.

The definition of M-estimators was motivated by robust statistics, which contributed new types of M-estimators.

However, they suffer from certain drawbacks; notably, they are not robust statistics, meaning that they are sensitive to outliers.

Exploratory data analysis, robust statistics, nonparametric statistics, and the development of statistical programming languages facilitated statisticians' work on scientific and engineering problems.

In robust statistics, Peirce's criterion is a rule for eliminating outliers from data sets, which was devised by Benjamin Peirce.

In 1973, Huber introduced M-estimation for regression (see robust statistics for additional details of M-estimation).

Robust Bayes methods are related to important and seminal ideas in other areas of statistics such as robust statistics and resistance estimators.