Special Issue on Robust Statistics

Robust Statistics is a branch of mathematical statistics. It studies adaptability of statistical methods when the general assumption has slightly changes and there are some mistakes of record data. There should be two conditions if a statistical method has good performance in practice: one is the conditions that the method based on is consistent with the conditions in practical problems; the other is that the sample should be random and does not contain gross errors (e.g. recording errors). However, these conditions are difficult to meet. If in practical cases the performance of statistical methods could be affected little, it is said to have robustness. This special issue will be focusing on studying the knowledge of robust statistics and discussing its practical applications.

In this special issue, we intend to invite front-line researchers and authors to submit original researches and review articles on exploring robust statistics. Potential topics include, but are not limited to:

• Estimation of location parameters
• Robust measures of scale
• Estimation of regression coefficients
• Manual screening for outliers
• Influence function and sensitivity curve
• Robust parametric approaches

Authors should read over the journal’s Authors’ Guidelines carefully before submission. Prospective authors should submit an electronic copy of their complete manuscript through the journal’s Paper Submission System.

Please kindly notice that the “Special Issue” under your manuscript title is supposed to be specified and the research field “Special Issue – Robust Statistics” should be chosen during your submission.

According to the following timetable:

For further questions or inquiries

Please contact Editorial Assistant at

ojs@scirp.org