Christopher Z. Mooney
Bootstrapping: A Nonparametric Approach to Statistical Inference - Quantitative Applications in the Social Sciences Christopher Z. Mooney

Name: Bootstrapping: A Nonparametric Approach to Statistical Inference - Quantitative Applications in the Social Sciences
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Author: Christopher Z. Mooney

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Bootstrapping: A Nonparametric Approach to Statistical Inference - Quantitative Applications in the Social Sciences

Christopher Z. Mooney

Bootstrapping, a computational nonparametric technique for "re-sampling," enables researchers to draw a conclusion about the characteristics of a population strictly from the existing sample rather than by making parametric assumptions about the estimator. Using real data examples from per capita personal income to median preference differences between legislative committee members and the entire legislature, Mooney and Duval discuss how to apply bootstrapping when the underlying sampling distribution of the statistics cannot be assumed normal, as well as when the sampling distribution has no analytic solution. In addition, they show the advantages and limitations of four bootstrap confidence interval methods: normal approximation, percenti

80 pages, illustrations

Mediji	Grāmatas Paperback Book (Grāmata ar mīksto vāku un līmēto muguru)
Izlaists	1993. gada 29. septembris
ISBN13	9780803953819
Izdevēji	SAGE Publications Inc
Lapas	80
Izmēri	137 × 216 × 4 mm · 100 g
Valoda	Angļu