mathematics

Peter Bühlmann

Peter Bühlmann is a mathematics topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Peter Bühlmann rather than just read about it. In short: Peter Lukas Bühlmann (born 12 April 1965 in Zürich) is a Swiss mathematician and statistician. Biography Bühlmann studied mathematics from 1985 at the ETH Zurich with Diplom in 1990 and doctorate in 1993.

Key takeaways

Peter Bühlmann belongs to mathematics; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Peter Bühlmann to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Peter Bühlmann from memory before moving on to harder problems.

Reference excerpt

Peter Lukas Bühlmann (born 12 April 1965 in Zürich) is a Swiss mathematician and statistician.

Biography Bühlmann studied mathematics from 1985 at the ETH Zurich with Diplom in 1990 and doctorate in 1993. His thesis The Blockwise Bootstrap in Time Series and Empirical Processes was written under the supervision of Hans-Rudolf Künsch and Erwin Bolthausen. At the University of California, Berkeley, Bühlmann was from 1994 to 1995 a postdoctoral research fellow and from 1995 to 1997 Neyman Assistant Professor. At ETH Zurich he became assistant professor in 1997 and is a full professor from 2004 to the present. From 2013 to 2017 he chaired the Department of Mathematics. His research deals with statistics, machine learning, and computational biology. He is married and has four children. Bühlmann is a frequent mountaineer in the Alps.

Honors and awards Bühlmann is a Fellow of the Institute of Mathematical Statistics, of the American Statistical Association, and Elected Member of the International Statistical Institute. He received the Wald Memorial Award and Lecture from the Institute of Mathematical Statistics (2024), From 2022 to 2023, he was President of the Institute of Mathematical Statistics. Since 2022, he is a member of the German National Academy of Sciences Leopoldina. He is an honorary doctor of the Catholic University of Louvain and a recipient of Guy Medal in Silver from the Royal Statistical Society (2018). He presented the Neyman Lecture from the Institute of Mathematical Statistics (2018), was Rothschild Fellow and Lecturer at the Isaac Newton Institute (2018), invited speaker at the International Congress of Mathematicians in Rio de Janeiro (2018) and a Plenary Speaker at the 8th European Congress of Mathematics in Portoroz (2021). He was recognized as a Highly Cited Researcher by Thomson Reuters/Clarivate Analytics every year from 2014 to 2020 and again in 2025. From 2010 to 2012 he was a co-editor of the Annals of Statistics.

Selected publications

Books with Sara van de Geer: Statistics for high-dimensional data. Methods, Theory and Applications, Springer 2011 as editor with P. Drineas, M. Kane, M. van der Laan: Handbook of Big Data, Chapman and Hall 2016 as editor with others: Statistical Analysis for High-Dimensional Data. The Abel Symposium 2014, Springer 2016

Articles with N. Meinshausen: High-dimensional graphs and variable selection with the lasso, Annals of Statistics, vol. 34, 2006, pp. 1436–1462, Arxiv with N. Meinshausen: Stability selection, Journal of the Royal Statistical Society, Series B, vol. 72, 2010, pp. 417–473 doi:10.1111/j.1467-9868.2010.00740.x with L. Meier, S. Van de Geer: The group lasso for logistic regression, Journal of the Royal Statistical Society, Series B, vol. 70, 2008, pp. 53–71 doi:10.1111/j.1467-9868.2007.00627.x with A. Prelić et al.: A systematic comparison and evaluation of biclustering methods for gene expression data, Bioinformatics, vol. 22, 2006, pp. 1122–1129 doi:10.1093/bioinformatics/btl060 with B. Yu: Boosting with the L2 loss: regression and classification, Journal of the American Statistical Association, vol. 98, 2003, pp. 324–339 doi:10.1198/016214503000125 with J. J. Goeman: Analyzing gene expression data in terms of gene sets: methodological issues, Bioinformatics, vol. 23, 2007, pp. 980–987 doi:10.1093/bioinformatics/btm051 with B. Yu: Analyzing bagging, Annals of Statistics, vol. 30, 2002, pp. 927–961 doi:10.1214/aos/1031689014 with T. Hothorn: Boosting algorithms: Regularization, prediction and model fitting, Statistical Science, vol. 22, 2007, pp. 477–505 doi:10.1214/07-STS242 with S. van de Geer: On the conditions used to prove oracle results for the Lasso, Electronic Journal of Statistics, vol. 3, 2009, pp. 1360–1392 doi:10.1214/09-EJS506

References

Illustrations

Worked examples

Example 1 — a first encounter with Peter Bühlmann

Start with the simplest possible case. Write down what Peter Bühlmann claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In mathematics, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Peter Bühlmann before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Peter Bühlmann ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Peter Bühlmann

In research: Peter Bühlmann appears in mathematics research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Peter Bühlmann in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Peter Bühlmann is common in secondary-school and first-year university syllabi. It links to neighbouring topics 1965 births, Academic staff of ETH Zurich, Annals of Statistics editors, so understanding it makes those chapters shorter.
In everyday life: Look for Peter Bühlmann outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Peter Bühlmann in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Peter Bühlmann means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Peter Bühlmann out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Peter Bühlmann in simple terms?

Peter Lukas Bühlmann (born 12 April 1965 in Zürich) is a Swiss mathematician and statistician. Biography Bühlmann studied mathematics from 1985 at the ETH Zurich with Diplom in 1990 and doctorate in 1993.

Why does Peter Bühlmann matter?

Because it connects several mathematics ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Peter Bühlmann?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Peter Bühlmann.