Anderson–Darling Tests of Goodness-of-Fit
Reference work entry
First Online:
DOI: https://doi.org/10.1007/978-3-642-04898-2_118
Introduction
A “goodness-of-fit” test is a procedure for determining whether a sample of n observations, x1, …, xn, can be considered as a sample from a given specified distribution. For example, the distribution might be a normal distribution with mean 0 and variance 1. More generally, the specified distribution is defined as
$$F(x) = \int \limits_{-\infty }^{x}f(y)dy,\quad -\infty <x <\infty \,,$$
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References and Further Reading
- Anderson TW, Darling DA (1952) Asymptotic theory of certain ‘goodness-of-fit’ criteria based on stochastic processes. Ann Math Stat 23:193–212MATHMathSciNetGoogle Scholar
- Anderson TW, Darling DA (1954) A test of goodness-of-fit. J Am Stat Assoc 49:765–769MATHMathSciNetGoogle Scholar
- Lockhart RA, Stephens MA (1985) Tests of fit for the von-Mises distribution. Biometrika 72:647–652MATHMathSciNetGoogle Scholar
- Lockhart RA, Stephens MA (1994) Estimation and tests of fit for the three-parameter Weibull distribution. J R Stat Soc B 56:491–500MATHMathSciNetGoogle Scholar
- Stephens MA (1976) Asymptotic results for goodness-of-fit statistics with unknown parameters. Ann Stat 4:357–369MATHMathSciNetGoogle Scholar
- Stephens MA (1986) In: D’Agostino R, Stephens MA (eds) Goodness-of-fit techniques, chap. 4. Marcel Dekker, New York
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