M
Michael D. Lee
Researcher at University of California, Irvine
Publications - 295
Citations - 17888
Michael D. Lee is an academic researcher from University of California, Irvine. The author has contributed to research in topics: Population & Bayesian probability. The author has an hindex of 65, co-authored 288 publications receiving 16437 citations. Previous affiliations of Michael D. Lee include Iowa State University & Cooperative Research Centre.
Papers
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Journal ArticleDOI
Quantitative trait loci for cell-wall components in recombinant inbred lines of maize (Zea mays L.) I: stalk tissue.
TL;DR: The objectives of this study were to map quantitative trait loci (QTLs) for fiber and lignin in the maize stalk and compare them with QTLs from other populations, and to useful to breeding efforts aimed at improving the quality of maize silage.
Book
Bayesian Cognitive Modeling: A Practical Course
TL;DR: In this article, the basics of Bayesian analysis are discussed, and a WinBUGS-based approach is presented to get started with WinBUGs, which is based on the SIMPLE model of memory.
Journal ArticleDOI
Statistical Evidence in Experimental Psychology An Empirical Comparison Using 855 t Tests
Ruud Wetzels,Dora Matzke,Michael D. Lee,Jeffrey N. Rouder,Geoffrey J. Iverson,Eric-Jan Wagenmakers +5 more
TL;DR: The authors provide a practical comparison of p values, effect sizes, and default Bayes factors as measures of statistical evidence, using 855 recently published t tests in psychology and conclude that the Bayesian approach is comparatively prudent, preventing researchers from overestimating the evidence in favor of an effect.
Journal ArticleDOI
Expanding the genetic map of maize with the intermated B73 x Mo17 (IBM) population.
Michael D. Lee,Natalya Sharopova,Natalya Sharopova,William D. Beavis,William D. Beavis,David Grant,David Grant,Maria Katt,Deborah Blair,Arnel R. Hallauer +9 more
TL;DR: This mapping population and related information should connect research involving dense genetic maps, physical mapping, gene isolation, comparative genomics, analysis of quantitative trait loci and investigations of heterosis.
Journal ArticleDOI
The fallacy of placing confidence in confidence intervals
TL;DR: It is shown in a number of examples that CIs do not necessarily have any of the properties of confidence intervals, and can lead to unjustified or arbitrary inferences, and is suggested that other theories of interval estimation should be used instead.