Abstract
One challenge of large-scale data analysis is that the assumption of an identical distribution for all samples is often not realistic. An optimal linear regression might, for example, be markedly different for distinct groups of the data. Maximin effects have been proposed as a computationally attractive way to estimate effects that are common across all data without fitting a mixture distribution explicitly. So far just point estimators of the common maximin effects have been proposed in Meinshausen and Bühlmann (Ann Stat 43(4):1801–1830, 2015). Here we propose asymptotically valid confidence regions for these effects.
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Rothenhäusler, D., Meinshausen, N., Bühlmann, P. (2016). Confidence Intervals for Maximin Effects in Inhomogeneous Large-Scale Data. In: Frigessi, A., Bühlmann, P., Glad, I., Langaas, M., Richardson, S., Vannucci, M. (eds) Statistical Analysis for High-Dimensional Data. Abel Symposia, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-319-27099-9_12
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DOI: https://doi.org/10.1007/978-3-319-27099-9_12
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