Janssen, Arnold (ed.); van der Vaart, Aad (ed.); Wellner, Jon A. (ed.) Very high dimensional semiparametric models. Abstracts from the workshop held October 2–8, 2011. (English) Zbl 1349.00200 Oberwolfach Rep. 8, No. 4, 2745-2779 (2011). Summary: Very high dimensional semiparametric models play a major role in many areas, in particular in signal detection problems when sparse signals or sparse events are hidden among high dimensional noise. Concrete examples are genomic studies in biostatistics or imaging problems. In a broad context all kind of statistical inference and model selection problems were discussed for high dimensional data. MSC: 00B05 Collections of abstracts of lectures 00B25 Proceedings of conferences of miscellaneous specific interest 62-06 Proceedings, conferences, collections, etc. pertaining to statistics 62G10 Nonparametric hypothesis testing 62G05 Nonparametric estimation 62J15 Paired and multiple comparisons; multiple testing 62F15 Bayesian inference 62G20 Asymptotic properties of nonparametric inference PDFBibTeX XMLCite \textit{A. Janssen} (ed.) et al., Oberwolfach Rep. 8, No. 4, 2745--2779 (2011; Zbl 1349.00200) Full Text: DOI References: [1] J. Horowitz; E. Mammen, Rate-optimal estimation for a general class of nonparametric regression models with unknown link functions., Ann. Statist. 35 (2007). · Zbl 1129.62034 [2] A. Juditsky; O. Lepski; A. Tsybakov Nonparametric estimation of composite functions, Ann. Statist. 37 (2009). High-dimensional causal inference Peter B”uhlmann This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.