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Introduction to robust estimation and hypothesis testing Rand R. Wilcox
Publisher: Academic Press

San Diego, CA: Academic Press; 2005. I was reminded of this idea when reading Christian Robert and George Casella's fun new book, Introducing Monte Carlo Methods with R. I do most of my work in statistical methodology and applied statistics, but sometimes I back up my methodology . Introduction to robust estimation and hypothesis testing (2nd ed.). Above all, the hypothesis tests give means for measuring the whole pattern recognition process effciency. It moves on to chapters on point estimation (7), hypothesis testing (8), interval estimation (9), asymptotics (10), analysis of variance and simple linear regression (11), and finally, regression (12) (errors in variables, logistic and robust). Introduction to robust estimation and hypothesis testing. "Robust Estimation and Outlier Detection with Correlation Coefficients". Modern robust data analysis methods: Measures of central tendency. In particular, the gain offered by the proposed feature extraction step can be evaluated. The abscissa unit is the mean absolute deviation (MAD) scale estimate (a robust estimate of the dispersion in the data) of all RefSeq NM transcript lengths, while the origin is set to the M-estimator of location (MEL; robust estimate of the mean) of the NM transcript lengths. If the inferred transcript lengths were unbiased, the histogram would be .. Tags:Introduction to robust estimation and hypothesis testing, tutorials, pdf, djvu, chm, epub, ebook, book, torrent, downloads, rapidshare, filesonic, hotfile, fileserve. ^ Devlin, Susan J; Gnanadesikan, R; Kettenring J.R. Wilcox RR: Introduction to Robust Estimation and Hypothesis Testing.