Central Limit Theorem

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Central Limit Theorem

The Law of Large Numbers states that as a sample of independent, identically distributed random numbers approaches infinity, its probability density function approaches the normal distribution. See: Normal Distribution.

Central Limit Theorem

In statistics, a theory stating that as the sample size of identically distributed, random numbers approaches infinity, it is more likely that the distribution of the numbers will approximate normal distribution. That is, the mean of all samples within that universe of numbers will be roughly the mean of the whole sample.
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Due to the asymptotic normality, the method can also provide an approximate confidence interval for the intensity, once computed the variance of the estimator.
Consistency and uniformly asymptotic normality of wavelet estimator in regression model with assoeiated samples, Statist.
This is the urn used by (12) to show the asymptotic normality (1.
1996) Consistency and Asymptotic Normality of the Quasi-maximum Likelihood Estimator in IGARCH (1,1).
The consistency and asymptotic normality of the QMLE has been established only for specific special cases of the ARFIMA and/or FIGARCH model.
On the Asymptotic Normality for [phi]-mixing Dependent Errors of Wavelet Regression Function Estimator, Acta Mathematicae Applicatae Sinica, 31(2008), No.
Keywords: asymptotic normality, Brillinger-mixing point processes, shot-noise processes, a-stable distribution functions.
Robinson (1995a) provides formal proofs of consistency and asymptotic normality for the Gauss case with -0.

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