mathematics

Shayle R. Searle

Shayle R. Searle 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 Shayle R. Searle rather than just read about it. In short: Shayle Robert Searle PhD (26 April 1928 – 18 February 2013) was a New Zealand mathematician who was professor emeritus of biological statistics at Cornell University. He was a prominent figure in the fields of linear and mixed models in statistics.

Key takeaways

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

Reference excerpt

Shayle Robert Searle PhD (26 April 1928 – 18 February 2013) was a New Zealand mathematician who was professor emeritus of biological statistics at Cornell University. He was a prominent figure in the fields of linear and mixed models in statistics. He also published widely on the topics of linear models, mixed models, and variance component estimation. Searle was one of the first statisticians to use matrix algebra in statistical methodology, and was an early proponent of the use of applied statistical techniques in animal breeding. He died at his home in Ithaca, New York.

Education BA – Victoria University of Wellington – 1949 MSc – Victoria University of Wellington – 1950 PhD – Cornell University – 1958 DSc (h.c.) – Victoria University of Wellington – 2005

Employment Research statistician – New Zealand Dairy Board – 1953 to 1955, 1959 to 1962 Statistician – University Computing Center, Cornell University – 1962 to 1965 Professor of biological statistics – Cornell University – 1965 to 1996

Honours Winner, Humboldt Research Award of the Alexander von Humboldt Foundation Fellow, American Statistical Association Fellow, Royal Statistical Society Honorary Fellow, Royal Society of New Zealand

Bibliography

Books Shayle R. Searle (2009). The Collected Works of Shayle R. Searle. New York: Wiley. ISBN 978-0-470-55606-1. Neuhaus, John William; McCulloch, Charles E.; Shayle R. Searle (2008). Generalized, Linear, and Mixed Models (Wiley Series in Probability and Statistics) (2nd ed.). New York: Wiley-Interscience. ISBN 978-0-470-07371-1. Shayle R. Searle (2006). Linear Models for Unbalanced Data (Wiley Series in Probability and Statistics). New York: Wiley-Interscience. ISBN 0-470-04004-1. McCulloch, Charles E.; Shayle R. Searle; Casella, George (2006). Variance Components (Wiley Series in Probability and Statistics). New York: Wiley-Interscience. ISBN 0-470-00959-4. Shayle R. Searle (2006). Matrix Algebra Useful for Statistics (Wiley Series in Probability and Statistics). New York: Wiley-Interscience. ISBN 0-470-00961-6. McCulloch, Charles E.; Searle, Shayle R. (2001). Generalized, Linear, and Mixed Models (1st ed.). Chichester: John Wiley & Sons. ISBN 0-471-19364-X. Willett, Lois Schertz; Searle, S. R. (2001). Matrix algebra for applied economics. Chichester: John Wiley & Sons. ISBN 0-471-32207-5. Searle, S. R. (1971). Linear models. New York: Wiley. ISBN 0-471-18499-3. Hausman, Warren H.; Searle, S. R. (1970). Matrix algebra for business and economics. New York: Wiley-Interscience. ISBN 0-471-76941-X. Shayle R. Searle (1966). Matrix Algebra for the Biological Sciences (Series on Quantitative Methods for Biologists & Medical Scientists). John Wiley & Sons Inc. ISBN 0-471-76930-4.

Selected journal articles

References

Further reading Shayle Searle (1968). "Oral and Personal Histories of Computing at Cornell". Office of Information Technologies, Cornell University. Searle, Shayle R. (2005). "Recollections from a 50-year random walk midst matrices, statistics and computing". Research Letters in the Information and Mathematical Sciences. 8: 45–52. ISSN 1175-2777. Archived from the original on 14 October 2008. Martin T. Wells (2009). "A Conversation with Shayle R. Searle". Statistical Science. 24 (2): 244–254. arXiv:1001.3272. doi:10.1214/08-STS259. S2CID 62162161. Hunter, Jeffrey (2015). "Shayle R. Searle: Pioneer in Linear Modelling". Australian & New Zealand Journal of Statistics. 57: 1–14. doi:10.1111/anzs.12107.

External links Shayle R. Searle at the Mathematics Genealogy Project

Worked examples

Example 1 — a first encounter with Shayle R. Searle

Start with the simplest possible case. Write down what Shayle R. Searle 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 Shayle R. Searle 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 Shayle R. Searle 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 Shayle R. Searle

In research: Shayle R. Searle 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 Shayle R. Searle 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: Shayle R. Searle is common in secondary-school and first-year university syllabi. It links to neighbouring topics 1928 births, 2013 deaths, Biostatisticians, so understanding it makes those chapters shorter.
In everyday life: Look for Shayle R. Searle 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 Shayle R. Searle in 20 minutes

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

Frequently asked questions

What is Shayle R. Searle in simple terms?

Why does Shayle R. Searle 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 Shayle R. Searle?

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 Shayle R. Searle.