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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Normal Distribution

References in periodicals archive ?

Sweeting, "Uniform asymptotic normality of the maximum likelihood estimator," Annals of Statistics, vol.

Asymptotic normality of the estimators for fractional Brownian motions with discrete data

This approximation is reasonable, since [[??].sup.measure.sub.A] is already strongly consistent by assuming only the ergodicity of [[PHI].sub.1] (which is a milder condition than the ones required for the asymptotic normality of [[??].sub.A,i]).

Intensity estimation of stationary fibre processes from digital images with a learned detector

As in Section 3.1, sufficient conditions for asymptotic normality are: i) [[delta].sup.2.sub.i] > 0, and ii) the SDFs of the different models are distinct.

Chi-squared tests for evaluation and comparison of asset pricing models

[P-values are computed assuming asymptotic normality.] IPS W-Statistic P-value AR Lags Total Taxes -8.663 0.000 0 to 4 Property Tax -6.516 0.000 0 to 4 Total Sales & Gross Receipts Tax -6.839 0.000 0 to 4 Individual Income Tax -4.185 0.000 0 to 4 Corporate Net Income Tax -4.532 0.000 0 to 3 Total Income Tax -2.616 0.005 0 to 3 Direct General Expenditure -6.131 0.000 0 to 4 Education Expenditure -5.377 0.000 0 to 4 Public Welfare Expenditure -0.768 0.221 0 to 4 Health & Hospital Expenditure -1.580 0.057 0 to 4 Highway Expenditure -3.650 0.000 0 to 4 Cross- Sections Obs.

Convergence of state and local fiscal policies: an application of panel unit root tests

The method of moments applies to all these cases and establishes the central limit theorems; similar details are given as in [31] (the asymptotic normality of the number of leaves being already proved there as a special case).

Asymptotic variance of random symmetric digital search trees

Mahmoud, Smythe and Szymanski (12) used a representation with a generalized Polya urn to prove the asymptotic normality (1.5).

Plane recursive trees, Stirling permutations and an urn model

(1996) Consistency and Asymptotic Normality of the Quasi-maximum Likelihood Estimator in IGARCH (1,1).

Predictability in stock returns in an emerging market: evidence from KSE 100 stock price index

The consistency and asymptotic normality of the QMLE has been established only for specific special cases of the ARFIMA and/or FIGARCH model.

Further long memory properties of inflationary shocks

These confidence intervals are constructed based on the asymptotic normality of the estimators for the sub-indices of the [C.sub.pk] index, and the process distribution need not be normal nor be known.

The C sub pk index and its confidence intervals

This assumption can be harder to justify than the asymptotic normality demanded by the t test, and is rarely evaluated (Petranka 1990).

Statistical sirens: the allure of nonparametrics

The topics include algebraic methods, discrete geometric methods, analytic methods, asymptotic normality in enumeration, trees, planar maps, graphs, words, tilings, lattice path enumeration, permutation classes, parking functions, standard Young tableaux, and computer algebra.

Handbook of Enumerative Combinatorics

From Lyapunov's theorem, the asymptotic normality of the penalized spline estimator [??](x) with [[??].sub.opt] can be derived under the same assumption as Theorem 2 and some additional mild conditions.