Hypothesize: Robust Statistics for Python

Robust statistics

Robust stat ist ics seeks to provide methods that emulate popular stat ist ical methods, but which are not unduly af fected by out liers or other small departures f rom model assumptions. In stat ist ics, classical est imat ion methods rely heavily on assumptions which are of ten not met in pract ice. In part icular, it is of ten assumed that the data errors are normally distributed, at least a...

Robust Statistics for Image Deconvolution

We present a blind multiframe image-deconvolution method based on robust statistics. The usual shortcomings of iterative optimization of the likelihood function are alleviated by minimizing theM -scale of the residuals, which achieves more uniform convergence across the image. We focus on the deconvolution of astronomical images, which are among the most challenging due to their huge dynamic ra...

Rough Histograms for Robust Statistics

Applied statistics are widely used in pattern recognition and other computing applications to find the most likely value of a parameter. The use of classical empirical statistics is based upon assumption about normality of underhing density distribution of data. When the data is corrupted by contaminated noise, then classical tools are usually not robust enough and the estimation of the mode is...

Robust statistics for outlier detection

When analyzing data, outlying observations cause problems because they may strongly influence the result. Robust statistics aims at detecting the outliers by searching for the model fitted by the majority of the data. We present an overview of several robust methods and outlier detection tools. We discuss robust procedures for univariate, low-dimensional, and high-dimensional data such as estim...

Robust Statistics for Multivariate Methods