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central limit theorem
(redirected from Asymptotic normality)

   Also found in: Financial, Wikipedia 0.01 sec.

central limit theorem

In statistics, any of several fundamental theorems in probability. Originally known as the law of errors, in its classic form it states that the sum of a set of independent random variables will approach a normal distribution regardless of the distribution of the individual variables themselves, given certain general conditions. Further, the mean (see mean, median, and mode) of the normal distribution will coincide with the (arithmetic) mean of the (statistical) means of each random variable.



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GMTs of antibody and their confidence intervals were computed by transforming the results to a logarithmic scale, assuming asymptotic normality conditions were satisfied on the scale and converting back to the original scale.
With the panel KPSS test assuming asymptotic normality and cross-sectional independence, the null of regime-wise stationarity at 1% can be rejected.
The consistency and asymptotic normality of the QMLE has been established only for specific special cases of the ARFIMA and/or FIGARCH model.
 
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