ICORS 2015 - International Conference on Robust Statistics (ICORS 2015)

The International Conference on Robust Statistics (ICORS) has been an annual international conference since 2001. The aim of the conferences is to bring together researchers interested in robust statistics, data analysis and related areas. This includes theoretical and applied statisticians as well as data analysts from other fields, and leading experts as well as junior researchers and graduate students.
The ICORS meetings create a forum to discuss recent progress and emerging ideas in statistics and encourage informal contacts and discussions among all the participants. They also play an important role in maintaining a cohesive group of international researchers interested in robust statistics and related topics, whose interactions transcend the meetings and endure year round.
The previous ICORS meetings were held in Vorau, Austria (2001), Vancouver, Canada (2002), Antwerp, Belgium (2003), Beijing, China (2004), Jyväskylä, Finland (2005), Lisbon, Portugal (2006), Buenos Aires, Argentina (2007), Antalya, Turkey (2008), Parma, Italy (2009), Prague, The Czech Republic (2010), Valladolid, Spain (2011), Burlington, USA (2012) and St. Petersburg, Russia (2013). ICORS 2014 in Germany.
The conference welcomes contributions to applied statistics as well as theoretical statistics, and in particular new problems related to robust statistics and data analysis. The conference will include, but will not necessarily be limited to, presentations on the following topics; however contributions from all areas of robust statistics are welcome.
Concepts and theory of robust statistics
Asymptotic theory and efficiency
Robust and nonparametric multivariate statistics
Robust time series analysis
Linear and generalized linear models
Robust statistics in business and finance
Robust estimation of income and allied size distributions; ethical indices
Robust statistics in bioinformatics/genetics
Statistical computing, graphics and data mining
Statistical inference based on divergence measures