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Robert Tibshirani

Robert Tibshirani 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 Robert Tibshirani rather than just read about it. In short: Robert Tibshirani (born July 10, 1956) is a professor in the Departments of Statistics and Biomedical Data Science at Stanford University. He was a professor at the University of Toronto from 1985 to 1998.

Robert Tibshirani — main illustration
Robert Tibshirani — illustration

Key takeaways

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

Reference excerpt

Robert Tibshirani (born July 10, 1956) is a professor in the Departments of Statistics and Biomedical Data Science at Stanford University. He was a professor at the University of Toronto from 1985 to 1998. In his work, he develops statistical tools for the analysis of complex datasets, most recently in genomics and proteomics. His most well-known contributions are the Lasso method, which proposed the use of L1 penalization in regression and related problems, and Significance Analysis of Microarrays.

Education and early life Tibshirani was born on 10 July 1956 in Niagara Falls, Ontario, Canada. He received his B. Math. in statistics and computer science from the University of Waterloo in 1979 and a Master's degree in Statistics from the University of Toronto in 1980. Tibshirani joined the doctoral program at Stanford University in 1981 and received his Ph.D. in 1984 under the supervision of Bradley Efron. His dissertation was entitled "Local likelihood estimation".

Honors and awards Tibshirani received the COPSS Presidents' Award in 1996. Given jointly by the world's leading statistical societies, the award recognizes outstanding contributions to statistics by a statistician under the age of 40. He is a fellow of the Institute of Mathematical Statistics and the American Statistical Association. He won an E.W.R. Steacie Memorial Fellowship from the Natural Sciences and Engineering Research Council of Canada in 1997. He was elected a Fellow of the Royal Society of Canada in 2001 and a member of the National Academy of Sciences in 2012. Tibshirani was made the 2012 Statistical Society of Canada's Gold Medalist at their yearly meeting in Guelph, Ontario for "exceptional contributions to methodology and theory for the analysis of complex data sets, smoothing and regression methodology, statistical learning, and classification, and application areas that include public health, genomics, and proteomics". He gave his Gold Medal Address at the 2013 meeting in Edmonton. He was elected to the Royal Society in 2019. Tibshirani was named as the 2021 recipient of the ISI Founders of Statistics Prize for his 1996 paper Regression Shrinkage and Selection via the Lasso.

Personal life His son, Ryan Tibshirani, with whom he occasionally publishes scientific papers, is a professor at UC Berkeley in the Department of Statistics.

Publications Tibshirani is a prolific author of scientific works on various topics in applied statistics, including statistical learning, data mining, statistical computing, and bioinformatics. He along with his collaborators has authored about 250 scientific articles. Many of Tibshirani's scientific articles were coauthored by his longtime collaborator, Trevor Hastie. Tibshirani is one of the most ISI Highly Cited Authors in Mathematics by the ISI Web of Knowledge. He has coauthored the following books:

T. Hastie and R. Tibshirani, Generalized Additive Models, Chapman and Hall, 1990. B. Efron and R. Tibshirani, An Introduction to the Bootstrap, Chapman and Hall, 1993 T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Prediction, Inference and Data Mining, Second Edition, Springer Verlag, 2009 (available for free from the co-author's website). G. James, D. Witten, T. Hastie, R. Tibshirani, An Introduction to Statistical Learning with Applications in R, Springer Verlag, 2013 (available for free from the co-author's website). T. Hastie, R. Tibshirani, M. Wainwright, Statistical Learning with Sparsity: the Lasso and Generalizations, CRC Press, 2015 (available for free from the co-author's website).

See also List of University of Waterloo people

References

External links Robert Tibshirani publications indexed by Google Scholar

Illustrations

Robert Tibshirani illustration

Worked examples

Example 1 — a first encounter with Robert Tibshirani

Start with the simplest possible case. Write down what Robert Tibshirani 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 Robert Tibshirani 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 Robert Tibshirani 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 Robert Tibshirani

In research
Robert Tibshirani 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 Robert Tibshirani 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
Robert Tibshirani is common in secondary-school and first-year university syllabi. It links to neighbouring topics 1956 births, 20th-century American statisticians, 20th-century Canadian statisticians, so understanding it makes those chapters shorter.
In everyday life
Look for Robert Tibshirani 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 Robert Tibshirani in 20 minutes

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

Frequently asked questions

What is Robert Tibshirani in simple terms?

Robert Tibshirani (born July 10, 1956) is a professor in the Departments of Statistics and Biomedical Data Science at Stanford University. He was a professor at the University of Toronto from 1985 to 1998.

Why does Robert Tibshirani 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 Robert Tibshirani?

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 Robert Tibshirani.

Tags

  • 1956 births
  • 20th-century American statisticians
  • 20th-century Canadian statisticians
  • 21st-century American statisticians
  • 21st-century Canadian statisticians
  • Academic staff of the University of Toronto
  • Canadian fellows of the Royal Society
  • Fellows of the American Statistical Association
  • Fellows of the Institute of Mathematical Statistics
  • Fellows of the Royal Society of Canada
  • Living people
  • Mathematical statisticians

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