Abstract
In the preceding chapters, we emphasized the visual viewpoint of representing data and fitting parametric distributions to the data. This is the exploratory approach to analyzing data. In the present chapter, we add some parametric estimation and test procedures which have been partially deduced from the visual ones.
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References
See also Andrews, D.F. and Herzberg, A.H. (1985). Data. Springer, New York.
The L 2 distance between normal densities and histograms was used in Brown, L.D. and Gene Hwang, J.T. (1993). How to approximate a histogram by a normal density. The American Statistician 47, 251–255.
Beran, R.J. (1977). Minimum Hellinger distance estimates for parametric models. Ann. Statist. 5, 445–463.
A term coined in Efron, B. (1979). Bootstrap methods: another look at the jackknife. Ann. Statist. 7, 1–26.
Manteiga, W.G. and Sánchez, J.M.P. (1994). The bootstrap—a review. Comp. Statist. 9, 165–205
See, e.g., Rice, J.A. (1988). Mathematical Statistics and Data Analysis. Wadsworth & Brooks, Pacific Grove.
Matsunawa, T. (1982). Some strong ∈-equivalence of random variables. Ann. Inst. Math. Statist. 34, 209–224.
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© 2007 Birkhäuser Verlag AG
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(2007). An Introduction to Parametric Inference. In: Statistical Analysis of Extreme Values. Birkhäuser Basel. https://doi.org/10.1007/978-3-7643-7399-3_3
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DOI: https://doi.org/10.1007/978-3-7643-7399-3_3
Publisher Name: Birkhäuser Basel
Print ISBN: 978-3-7643-7230-9
Online ISBN: 978-3-7643-7399-3
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