mathematics

Larry A. Wasserman

Larry A. Wasserman 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 Larry A. Wasserman rather than just read about it. In short: Larry Alan Wasserman (born 1959) is a Canadian-American statistician and a professor in the Department of Statistics & Data Science and the Machine Learning Department at Carnegie Mellon University. Biography Wasserman received his Ph.D. from the University of Toronto in 1988 under the supervision of Robert Tibshirani.

Key takeaways

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

Reference excerpt

Larry Alan Wasserman (born 1959) is a Canadian-American statistician and a professor in the Department of Statistics & Data Science and the Machine Learning Department at Carnegie Mellon University.

Biography Wasserman received his Ph.D. from the University of Toronto in 1988 under the supervision of Robert Tibshirani. He received the COPSS Presidents' Award in 1999 and the CRM-SSC Prize in 2002. He was elected a fellow of the American Statistical Association in 1996, of the Institute of Mathematical Statistics in 2004, and of the American Association for the Advancement of Science in 2011. He was elected to National Academy of Sciences in May, 2016.

Selected works Wasserman has written many research papers about nonparametric inference, asymptotic theory, causality, and applications of statistics to astrophysics, bioinformatics, and genetics. He has also written two advanced statistics textbooks, All of Statistics and All of Nonparametric Statistics.

2004. All of Statistics: A Concise Course in Statistical Inference. Springer-Verlag, New York. ISBN 978-0387402727 won DeGroot Prize 2005. 2006. All of Nonparametric Statistics. Springer. ISBN 978-0-387-25145-5 2013. Topological Inference. Rietz Lecture 2013.

Honors and awards 2016, Member of National Academy of Sciences Wasserman was elected to National Academy of Sciences in recognition of his distinguished and continuing achievement in original research.

See also Peer review Computational thinking

References

External links Wasserman's home page Wasserman's Blog on Statistics and Machine Learning

Illustrations

Worked examples

Example 1 — a first encounter with Larry A. Wasserman

Start with the simplest possible case. Write down what Larry A. Wasserman 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 Larry A. Wasserman 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 Larry A. Wasserman 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 Larry A. Wasserman

In research: Larry A. Wasserman 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 Larry A. Wasserman 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: Larry A. Wasserman is common in secondary-school and first-year university syllabi. It links to neighbouring topics 1959 births, 21st-century Canadian statisticians, Carnegie Mellon University faculty, so understanding it makes those chapters shorter.
In everyday life: Look for Larry A. Wasserman 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 Larry A. Wasserman in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Larry A. Wasserman 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 Larry A. Wasserman out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Larry A. Wasserman in simple terms?

Larry Alan Wasserman (born 1959) is a Canadian-American statistician and a professor in the Department of Statistics & Data Science and the Machine Learning Department at Carnegie Mellon University. Biography Wasserman received his Ph.D. from the University of Toronto in 1988 under the supervision…

Why does Larry A. Wasserman 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 Larry A. Wasserman?

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 Larry A. Wasserman.