central limit theorem

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cen·tral lim·it the·o·rem

the sum (or average) of n realizations of the same process, provided only that it has a finite variance, will approach the gaussian distribution as n becomes indefinitely large. This theory provides a broad warrant for the use of normal theory even for nongaussian data. In the form stated here, it constitutes the classical version; more general versions allow serious relaxation of the usual assumptions.
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Under the assumptions needed for the asymptotic normality of [[?
Heinrich and Prokesova (2010) proved that if P is a stationary point process, with milder mixing conditions than the ones required for the asymptotic normality of the corresponding counting measure, and if [{[W.
The proof by Bona (4) of the asymptotic normality (1.
Consistency and asymptotic normality for the quasi maximum likelihood estimator in IGARCH(1,1) and covariance stationary GARCH(1,1) models.
In particular, we state asymptotic normality (Theorem 1) and weak consistency (Theorem 4) in the cases s = d - 1 and s = d - 2, respectively.
This last estimate confirms the asymptotic normality of the truncated shot-noise process [[?
The estimator is asymptotically more efficient that the GPH estimator and consistency and asymptotic normality of [?
Hence the statistical significances with which RAND drew causal conclusions need a grain of salt (for with asymptotic normality confidence levels and p-values become unknown).
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