A method for calculating optic flow, using robust statistics, is developed. The method generally out-performs all competing methods in terms of accuracy. One of the key features in the success of this method, is that we use Least Median of Squares, which is known to be robust to outliers. The computational cost is kept very low by using an approximate solution to the Least Median of Squares onl...

Not all experiments publish their results with a description of the correlations between data points. This makes it difficult to do hypothesis tests or model fits that data, since just assuming no correlation can lead an overestimation underestimation resulting uncertainties. work presents robust test statistics be used datasets missing information. They are exact in case and either guaranteed ...

We propose robust tests as alternatives to the classical Wilks’ Lambda test in one-way MANOVA. The robust tests use highly robust and efficient multi-sample multivariate Sor MM-estimators instead of the empirical covariances. The properties of several robust test statistics are compared. Under the null hypothesis, the distribution of the test statistics is proportional to a chi-square distribut...

This chapter presents an introduction to robust statistics with applications of a chemometric nature. Following a description of the basic ideas and concepts behind robust statistics, including how robust estimators can be conceived, the chapter builds up to the construction (and use) of robust alternatives for some methods for multivariate analysis frequently used in chemometrics, such as prin...

Robust statistics is an extension of classical parametric statistics that specifically takes into account the fact that the assumed parametric models used by the researchers are only approximate. In this article, we review and outline how robust inferential procedures may routinely be applied in practice in the biomedical research. Numerical illustrations are given for the t-test, regression mo...