High-Dimensional Statistics with a View Toward Applications in Biology

By Peter Buhlmann, Markus Kalisch, Lukas Meier. 2014

323 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/48d36579b83470650967b0c903e6af005ec10685

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In Molecular Biology

Citations in the corpus

Sparse and Compositionally Robust Inference of Microbial Ecological Networks (2014, background): “One assumption is that the true underlying graph is reasonably sparse, e.g., that the number of taxon-taxon associations scales linearly with the number of measured taxa.”
Foundations of Linear and Generalized Linear Models (2015, methodology): “A fundamental assumption needed for methods to perform well with large p is sparse structure, with relatively few elements in β being nonzero (Bühlmann et al. 2014).”
High-dimensional simultaneous inference with the bootstrap (2016, methodology): “We revisit a dataset about riboflavin production by bacillus subtilis (Bühlmann et al. 2014), already studied inBühlmann (2013), van deGeer et al. (2014) andDezeure et al. (2015).”
Handbook of Big Data (2016, unknown): “Tutorial for Causal Inference 363”
Introduction to the LASSO (2018, background): “We refer to [19] for details on riboflavin dataset.”
On asymptotically optimal confidence regions and tests for high-dimensional models (2013, methodology): “The data has been kindly provided by DSM (Switzerland) and is publicly available [9].”
mgm: Estimating Time-Varying Mixed Graphical Models in High-Dimensional Data (2015, background): “The reason is that the sampling distributions of parameters obtained with (cid:96) 1 -regularized regression have a zero mass around zero (B¨uhlmann, Kalisch, and Meier 2014).”
Identifying a Minimal Class of Models for High-dimensional Data (2015, methodology): “We now move to comparison of our results with models obtained using other methods, as reported in B¨uhlmann et al. (2014).”