AStA Advances in Statistical Analysis - Call for Papers: Special Issue on Dimension Reduction

Guest Editors: Michael Massmann, WHU Otto Beisheim School of Management and Adalbert F. X. Wilhelm, Jacobs University Bremen

Deadline for submission: 31 July, 2021

With ever more data being collected in all realms of science and society there is a persistent need for the continual development of statistical dimension reduction techniques. This special issue invites original research papers that aim at contributing to this literature. In line with the aims and scopes of AStA Advances in Statistical Analysis, submissions are possible in the section `Statistical applications' on the innovative use of statistical methods applied to timely data problems, or in the section `Statistical methodology' on statistical theory and methodological developments.

The special issue aims at providing an overview on the field of dimensionality reduction by highlighting state-of-the-art research in a variety of areas. The objective is to cover techniques for use with either cross-sectional or times series data. Submissions need not be restricted to any field, yet particular emphasis is placed on dimension reduction techniques

- enabling low-dimensional visualisation and interpretation;
- based on projection on interesting directions or manifolds;
- for forecasting multiple time series with many predictors;
- for high-dimensional problems with more parameters than observations.

Empirical applications may look at, for instance,

- economic or financial data;
- survey or social science data;
- business or industrial production data.

The reproducibility of empirical results as well as simulations is imperative, and authors are asked to make data, software and code available for inclusion in an electronic supplement.

Submissions follow the editorial standard of AStA Advances in Statistical Analysis.