Central limit theorem

Central limit theorems are a set of weak-convergence results in probability theory. Intuitively, they all express the fact that any sum of many independent identically distributed random variables will tend to be distributed according to a particular "attractor distribution". The most important and famous result is simply called The Central Limit Theorem which states that if the summed variables have a finite variance then they will be approximately normally distributed. Since many real processes yield distributions with finite variance, this explains the ubiquity of the normal distribution.

Classical central limit theorem

The theorem most often called the central limit theorem is the following. Let X1, X2, X3, ... be a sequence of random variables which are defined on the same probability space, share the same probability distribution D and are independent. Assume that both the expected value μ and the standard deviation σ of D exist and are finite.

Related Topics:
Sequence - Probability space - Probability distribution - Independent - Standard deviation

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Consider the sum :Sn = X1 + ... + Xn.

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Then the expected value of Sn is nμ and its standard deviation is σ n½. Furthermore, informally speaking, the distribution of Sn approaches the normal distribution N(nμ,σ2n) as n approaches ∞.

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In order to clarify the word "approaches" in the last sentence, we standardize Sn by setting

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:Z_n = rac{S_n - n mu}{sigma sqrt{n}}.

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Then the distribution of Zn converges towards the standard normal distribution N(0,1)

Related Topics:
Converges - Standard normal distribution

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as n approaches ∞ (this is convergence in distribution). This means: if Φ(z) is the cumulative distribution function of N(0,1), then for every real number z, we have

Related Topics:
Convergence in distribution - Cumulative distribution function - Real number

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:lim_{n o infty} mbox{Pr}(Z_n le z) = Phi(z),

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or, equivalently,

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:lim_{n ightarrowinfty}mbox{Pr}left(rac{overline{X}_n-mu}{sigma/sqrt{n}}leq z ight)=Phi(z)

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where

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:overline{X}_n=S_n/n=(X_1+cdots+X_n)/n

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is the sample mean.

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Proof of the central limit theorem

For a theorem of such fundamental importance to statistics and applied probability, the central limit theorem has a remarkably simple proof using characteristic functions. It is similar to the proof of a (weak) law of large numbers. For any random variable, Y, with zero mean and unit variance (var(Y) = 1), the characteristic function of Y is, by Taylor's theorem,

Related Topics:
Statistics - Applied probability - Characteristic function - Law of large numbers - Mean - Taylor's theorem

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:arphi_Y(t) = 1 - {t^2 over 2} + o(t^2), quad t ightarrow 0

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where o (t2 ) is "little o notation" for some function of t  that goes to zero more rapidly than t2. Letting Yi be (Xi − μ)/σ, the standardised value of Xi, it is easy to see that the standardised mean of the observations X1, X2, ..., Xn is just

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:Z_n = rac{overline{X}_n-mu}{sigma/sqrt{n}} = sum_{i=1}^n {Y_i over sqrt{n}}.

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By simple properties of characteristic functions, the characteristic function of Zn is

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:left^n = left[ 1 - {t^2

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over 2n} + oleft({t^2 over n} ight) ight]^n , ightarrow , e^{-t^2/2}, quad n ightarrow infty.

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But, this limit is just the characteristic function of a standard normal distribution, N(0,1), and the central limit theorem follows from the Lévy continuity theorem, which confirms that the convergence of characteristic functions implies convergence in distribution.

Related Topics:
Lévy continuity theorem - Convergence

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Convergence to the limit

If the third central moment E((X1 − μ)3) exists and is finite, then the above convergence is uniform and the speed of convergence is at least on the order of 1/n½ (see Berry-Esséen theorem).

Related Topics:
Moment - Uniform - Berry-Esséen theorem

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Pictures of a distribution being "smoothed out" by summation (showing original distribution and three subsequent convolutions):

Related Topics:
Summation - Convolution

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(See Illustration of the central limit theorem for further details on these images.)

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An equivalent formulation of this limit theorem starts with An = (X1 + ... + Xn) / n which can be interpreted as the mean of a random sample of size n. The expected value of An is μ and the standard deviation is σ / n½. If we normalize An by setting Zn = (An - μ) / (σ / n½), we obtain the same variable Zn as above, and it approaches a standard normal distribution.

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Note the following apparent "paradox": by adding many independent identically distributed positive variables, one gets approximately a normal distribution. But for every normally distributed variable, the probability that it is negative is non-zero! How is it possible to get negative numbers from adding only positives?

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The reason is simple: the theorem applies to terms centered about the mean. Without that standardization, the distribution would, as intuition suggests, escape away to infinity.

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The Central Limit Theorem, as an approximation for a finite number of observations, provides a reasonable approximation only when close to the peak of the normal distribution; it requires a very large number of observations to stretch into the tails.

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Alternative statements of the theorem

Density functions

The density of the sum of two or more independent variables is the convolution of their densities (if these densities exist).

Related Topics:
Density - Convolution

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Thus the central limit theorem can be interpreted as a statement about the properties of density functions under convolution:

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the convolution of a number of density functions tends to the normal density as the number of density functions increases without bound,

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under the conditions stated above.

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Since the characteristic function of a convolution is the product of the characteristic functions of the densities involved,

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the central limit theorem has yet another restatement:

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the product of the characteristic functions of a number of density functions tends to the characteristic function of the normal density as the number of density functions increases without bound,

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under the conditions stated above.

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An equivalent statement can be made about Fourier transforms,

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since the characteristic function is essentially a Fourier transform.

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Products of random variables

The central limit theorem tells us what to expect about the sum of independent random variables, but what about the product? Well, the logarithm of a product is simply the sum of the logs of the factors, so the log of a product of random variables tends to have a normal distribution, which makes the product itself have a log-normal distribution. Many physical quantities (especially mass or length, which are a matter of scale and cannot be negative) are the product of different random factors, so they follow a log-normal distribution.

Related Topics:
Logarithm - Log-normal distribution - Random

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