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Kathryn Roeder

Kathryn Roeder 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 Kathryn Roeder rather than just read about it. In short: Kathryn M. Roeder is an American statistician known for her development of statistical methods to uncover the genetic basis of complex disease and her contributions to mixture models, semiparametric inference, and multiple testing.

Key takeaways

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

Reference excerpt

Kathryn M. Roeder is an American statistician known for her development of statistical methods to uncover the genetic basis of complex disease and her contributions to mixture models, semiparametric inference, and multiple testing. Roeder holds positions as professor of statistics and professor of computational biology at Carnegie Mellon University, where she leads a project focused on discovering genes associated with autism.

Education and career Roeder did her undergraduate studies at the University of Idaho, where she graduated in 1982 with a bachelor's degree in wildlife resources. Roeder worked as a biologist for a year in the Pacific Northwest before returning to academia for graduate studies in statistics. She completed her Ph.D. in 1988 at Pennsylvania State University; her dissertation, supervised by Bruce G. Lindsay, was Method of Spacings for Semiparametric Inference. Roeder joined the faculty of Yale University in 1988 and earned tenure there. She remained at Yale until 1994, when she moved to the statistics department at Carnegie Mellon. She added a second appointment in computational biology in 1998, and served a term as Vice Provost for Faculty from 2015 to 2019.

Recognition In 1995 Roeder became an elected member of the International Statistical Institute. She was elected a Fellow of the American Statistical Association in 1996. In 1997 she received two major awards from the Committee of Presidents of Statistical Societies: the Presidents' Award "in recognition of outstanding contributions to the profession of statistics", and the George W. Snedecor Award, for her work in biometry with Bruce Lindsay and Raymond J. Carroll. In the same year she was elected as a fellow of the Institute of Mathematical Statistics, and in 1999 gave the Medallion Lecture of the Institute of Mathematical Statistics. She won the Janet L Norwood Award for outstanding achievement by a woman in the statistical sciences in 2013. Roeder was elected to the National Academy of Sciences and as a fellow of the American Association for the Advancement of Science (AAAS) in 2019. She was awarded the 2020 R. A. Fisher Lectureship.

Personal Roeder is married to Bernard J. Devlin, a psychiatrist at the University of Pittsburgh, and has worked with him on research involving genetics and autism.

Selected publications Roeder, Kathryn; Carroll, Raymond J.; Lindsay, Bruce G. (1996), "A semiparametric mixture approach to case-control studies with errors in covariables", Journal of the American Statistical Association, 91 (434): 722–732, doi:10.2307/2291667, JSTOR 2291667, MR 1395739 Devlin, B.; Daniels, Michael; Roeder, Kathryn (July 1997), "The heritability of IQ", Nature, 388 (6641): 468–471, Bibcode:1997Natur.388..468D, doi:10.1038/41319, PMID 9242404, S2CID 4313884 Roeder, Kathryn; Wasserman, Larry (September 1997), "Practical Bayesian density estimation using mixtures of normals", Journal of the American Statistical Association, 92 (439): 894–902, Bibcode:1997JASA...92..894R, doi:10.1080/01621459.1997.10474044. Devlin, B.; Roeder, Kathryn (December 1999), "Genomic control for association studies", Biometrics, 55 (4): 997–1004, doi:10.1111/j.0006-341x.1999.00997.x, JSTOR 2533712, PMID 11315092, S2CID 6297807 Jones, Bobby L.; Nagin, Daniel S.; Roeder, Kathryn (February 2001), "A SAS procedure based on mixture models for estimating developmental trajectories", Sociological Methods & Research, 29 (3): 374–393, doi:10.1177/0049124101029003005, S2CID 15594963 Wasserman, Larry; Roeder, Kathryn (2009), "High-dimensional variable selection", The Annals of Statistics, 37 (5A): 2178–2201, arXiv:0704.1139, doi:10.1214/08-AOS646, MR 2543689, PMC 2752029, PMID 19784398

References

External links Kathryn Roeder publications indexed by Google Scholar

Worked examples

Example 1 — a first encounter with Kathryn Roeder

Start with the simplest possible case. Write down what Kathryn Roeder 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 Kathryn Roeder 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 Kathryn Roeder 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 Kathryn Roeder

In research
Kathryn Roeder 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 Kathryn Roeder 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
Kathryn Roeder is common in secondary-school and first-year university syllabi. It links to neighbouring topics American statisticians, American women statisticians, Carnegie Mellon University faculty, so understanding it makes those chapters shorter.
In everyday life
Look for Kathryn Roeder 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 Kathryn Roeder in 20 minutes

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

Frequently asked questions

What is Kathryn Roeder in simple terms?

Kathryn M. Roeder is an American statistician known for her development of statistical methods to uncover the genetic basis of complex disease and her contributions to mixture models, semiparametric inference, and multiple testing.

Why does Kathryn Roeder 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 Kathryn Roeder?

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 Kathryn Roeder.

Tags

  • American statisticians
  • American women statisticians
  • Carnegie Mellon University faculty
  • Elected Members of the International Statistical Institute
  • Fellows of the American Statistical Association
  • Fellows of the Institute of Mathematical Statistics
  • Living people
  • Members of the United States National Academy of Sciences
  • Pennsylvania State University alumni
  • University of Idaho alumni
  • Yale University faculty

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