By William Briggs
Ants, motorcycles, and Clocks is a wonderful textual content for an undergraduate problem-solving direction or as a source for arithmetic educators, delivering hundreds of thousands of mathematical difficulties that may be utilized in any path. Mathematically the booklet is dependent upon semesters of calculus, even if a lot of the booklet calls for in simple terms precalculus talents.
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Extra info for Ants, bikes, & clocks: problem solving for undergraduates
Math. Meth. Statist. 11, 37-68.  Jin J. (2006). Higher criticism statistic: theory and applications in nonGaussian detection. In Proc. PHYSTAT 2005: Statistical Problems in Particle Physics, Astrophysics and Cosmology ( L. Lyons and M. K linel, eds). World Scientific Publishing, Singapore.  Kimeldorf G. and Wahba G. (1971). Some results on Tchebycheffian spline functions, J. Math. Anal. Applic. 33, 82-95.  Krefsel U. (1998). Pairwise classification and support vector machines. In Advances in Kernel Methods - Support Vector Learning (B.
In a fairly general asymptotic framework, this simple but effective correlation learning is shown to have the sure screening property even for the case of exponentially growing dimensionality, that is, the screening retains the true important predictor variables with probability tending to one exponentially fast. The SIS methodology may break down if a predictor variable is marginally unrelated, but jointly related with the response, or if a predictor variable is jointly uncorrelated with the response but has higher marginal correlation with the response than some important predictors.
Ann. Statist. 5, 2135-2152. , Pittelkow Y. and Ghosh M. (2008). Theoretical measures of relative performance of classifiers for high dimensional data with small sample sizes. J. R. Statist. Soc. B 70, 159-173. , Tibshirani R. and Friedman J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd edition). SpringerVerlag, New York.  Hsu C. and Lin C. (2002). A comparison of methods for multi-class support vector machines. IEEE Trans. Neural Netw. 13, 415-425.