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central limit theorem: Definition and Much More from Answers.com

️Wed Jul 01 2015

A central limit theorem is any of a set of weak-convergence results in probability theory. They all express the fact that any sum of many independent and identically-distributed random variables will tend to be distributed according to a particular "attractor distribution". The most important and famous result is called The Central Limit Theorem which states that if the sum of the variables has a finite variance, then it will be approximately normally distributed (i.e., following a Gaussian distribution).

Since many real processes yield distributions with finite variance, this explains the ubiquity of the normal probability distribution.

Several generalizations for finite variance exist which do not require identical distribution but incorporate some condition which guarantees that none of the variables exert a much larger influence than the others. Two such conditions are the Lindeberg condition and the Lyapunov condition. Other generalizations even allow some "weak" dependence of the random variables. Also, a generalization due to Gnedenko and Kolmogorov states that the sum of a number of independent random variables with power-law tail distributions decreasing as 1/|x|^α+1 with 0 < α < 2 (and therefore having infinite variance) will tend to a symmetric stable Lévy distribution as the number of variables grows. This article will only be concerned with the central limit theorem as it applies to distributions with finite variance.

See illustration of the central limit theorem

History

Tijms (2004, p.169) writes:

“

The central limit theorem has an interesting history. The first version of this theorem was postulated by the French-born English mathematician Abraham de Moivre, who, in a remarkable article published in 1733, used the normal distribution to approximate the distribution of the number of heads resulting from many tosses of a fair coin. This finding was far ahead of its time, and was nearly forgotten until the famous French mathematician Pierre-Simon Laplace rescued it from obscurity in his monumental work Théorie Analytique des Probabilités, which was published in 1812. Laplace expanded De Moivre's finding by approximating the binomial distribution with the normal distribution. But as with De Moivre, Laplace's finding received little attention in his own time. It was not until the nineteenth century was at an end that the importance of the central limit theorem was discerned, when, in 1901, Russian mathematician Aleksandr Lyapunov defined it in general terms and proved precisely how it worked mathematically. Nowadays, the central limit theorem is considered to be the unofficial sovereign of probability theory.

”

See Bernstein (1945) for a historical discussion focusing on the work of Pafnuty Chebyshev and his students Andrey Markov and Aleksandr Lyapunov that led to the first proofs of the C.L.T. in a general setting.

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.

Consider the sum S_n = X₁ + ... + X_n. Then the expected value of S_n is nμ and its standard error is σ n^1/2. Furthermore, informally speaking, the distribution of S_n approaches the normal distribution N(nμ,σ²n) as n approaches ∞.

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

$Z_n = \frac{S_n - n \mu}{\sigma \sqrt{n}}.$

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

$\lim_{n \to \infty} \mbox{P}(Z_n \le z) = \Phi(z),$

or, equivalently,

$\lim_{n\rightarrow\infty}\mbox{P}\left(\frac{\overline{X}_n-\mu}{\sigma/\sqrt{n}}\leq z\right)=\Phi(z)$

where

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

is the sample mean.

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,

$\varphi_Y(t) = 1 - {t^2 \over 2} + o(t^2), \quad t \rightarrow 0$

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

$Z_n = \frac{n\overline{X}_n-n\mu}{\sigma\sqrt{n}} = \sum_{i=1}^n {Y_i \over \sqrt{n}}.$

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

$\left[\varphi_Y\left({t \over \sqrt{n}}\right)\right]^n = \left[ 1 - {t^2 \over 2n} + o\left({t^2 \over n}\right) \right]^n \, \rightarrow \, e^{-t^2/2}, \quad n \rightarrow \infty.$

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.

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/n^½ (see Berry-Esséen theorem).

The convergence normal is monotonic, in the sense that the entropy of Z_n increases monotonically to that of the normal distribution, as proven by Artstein, Ball, Barthe and Naor.

Pictures of a distribution being "smoothed out" by summation (showing original density of distribution and three subsequent summations, obtained by convolution of density functions):

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

An equivalent formulation of this limit theorem starts with A_n = (X₁ + ... + X_n) / n which can be interpreted as the mean of a random sample of size n. The expected value of A_n is μ and the standard deviation is σ / n^½. If we standardize A_n by setting Z_n = (A_n - μ) / (σ / n^½), we obtain the same variable Z_n as above, and it approaches a standard normal distribution.

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.

The Central Limit theorem also applies to sums of independent and identical discrete random variables, although in this case the convergence of the sum toward a normal distribution has singular properties: namely, a sum of discrete random variables is still a discrete random variable, so that we are confronted to a series of discrete random variables whose probability distribution converges towards a probability density function corresponding to a continuous variable (namely the normal distribution). This means that if we build a histogram of the realisations of the sum of n independent identical discrete variables, the curve that joins the centers of the upper faces of the rectangles forming the histogram converges toward a gaussian curve as n approaches ∞. The binomial distribution article details such an application of the central limit theorem in the simple case of a discrete variable taking only two possible values.

Relation to the law of large numbers

The law of large numbers as well as The Central Limit Theorem are partial solutions to a general problem: "What is the limiting behavior of S_n as n approaches infinity?" In mathematical analysis, asymptotic series is one of the most popular tools employed to approach such questions.

Suppose we have an asymptotic expansion of f(n):

$f(n)= a_1 \varphi_{1}(n)+a_2 \varphi_{2}(n)+O(\varphi_{3}(n)) \ (n \rightarrow \infty).$

dividing both parts by $\varphi_{1}(n)$ and taking the limit will produce a₁ - the coefficient at the highest-order term in the expansion representing the rate at which f(n) changes in its leading term.

$\lim_{n\to\infty}\frac{f(n)}{\varphi_{1}(n)}=a_1.$

Informally, one can say: "f(n) grows approximately as $a_1 \varphi_{1}(n)$ ". Taking the difference between f(n) and its approximation and then dividing by the next term in the expansion we arrive to a more refined statement about f(n):

$\lim_{n\to\infty}\frac{f(n)-a_1 \varphi_{1}(n)}{\varphi_{2}(n)}=a_2$

here one can say that: "the difference between the function and its approximation grows approximately as $a_2 \varphi_{2}(n)$ " The idea is that dividing the function by appropriate normalizing functions and looking at the limiting behavior of the result can tell us much about the limiting behavior of the original function itself.

Informally, something along these lines is happening when S_n is being studied in classical probability theory. Under certain regularity conditions, by The Law of Large Numbers, $\frac{S_n}{n} \rightarrow \mu$ and by The Central Limit Theorem, $\frac{S_n-n\mu}{\sqrt{n}} \rightarrow \xi$ where ξ is distributed as N(0,σ²) which provide values of first two constants in informal expansion:

$S_n \approx \mu n+\xi \sqrt{n}.$

It could be shown that if X₁, X₂, X₃, ... are i.i.d. and E( | X₁ | )^β < ∞ for some $1 \le \beta <2$ then $\frac{S_n-n\mu}{n^{\frac{1}{\beta}}} \to 0$ hence $\sqrt{n}$ is the largest power of n which if serves as a normalizing function would provide a non-trivial (non-zero) limiting behavior. Interestingly enough, The Law of the Iterated Logarithm tells us what is happening "in between" The Law of Large Numbers and The Central Limit Theorem. Specifically it says that the normalizing function $\sqrt{n\log\log n}$ intermediate in size between n of The Law of Large Numbers and $\sqrt{n}$ of The Central Limit Theorem provides a non-trivial limiting behavior.

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). Thus the central limit theorem can be interpreted as a statement about the properties of density functions under convolution: the convolution of a number of density functions tends to the normal density as the number of density functions increases without bound, under the conditions stated above.

Since the characteristic function of a convolution is the product of the characteristic functions of the densities involved, the central limit theorem has yet another restatement: 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, under the conditions stated above.

An equivalent statement can be made about Fourier transforms, since the characteristic function is essentially a Fourier transform.

Products of positive 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 that take only positive values 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.

Whereas the central limit theorem for sums of random variables requires the condition of finite variance, the corresponding theorem for products requires the corresponding condition that the density function be square-integrable (see Rempala 2002).

Lyapunov condition

Let X_n be a sequence of independent random variables defined on the same probability space. Assume that X_n has finite expected value μ_n and finite standard deviation σ_n. We define

$s_n^2 = \sum_{i = 1}^n \sigma_i^2.$

Assume that the third central moments

$r_n^3 = \sum_{i = 1}^n \mbox{E}\left({\left| X_i - \mu_i \right|}^3 \right)$

are finite for every n, and that

$\lim_{n \to \infty} \frac{r_n}{s_n} = 0.$

(This is the Lyapunov condition). We again consider the sum S_n=X₁+...+X_n. The expected value of S_n is m_n = ∑_i=1..nμ_i and its standard deviation is s_n. If we standardize S_n by setting

$Z_n = \frac{S_n - m_n}{s_n}$

then the distribution of Z_n converges towards the standard normal distribution N(0,1) as above.

Lindeberg condition

In the same setting and with the same notation as above, we can replace the Lyapunov condition with the following weaker one (from Lindeberg in 1920). For every ε > 0

$\lim_{n \to \infty} \sum_{i = 1}^{n} \mbox{E}\left( \frac{(X_i - \mu_i)^2}{s_n^2} : \left| X_i - \mu_i \right| > \varepsilon s_n \right) = 0$

where E( U : V > c) is E( U 1{V > c}), i.e., the expectation of the random variable U 1{V > c} whose value is U if V > c and zero otherwise. Then the distribution of the standardized sum Z_n converges towards the standard normal distribution N(0,1).

Non-independent case

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There are some theorems which treat the case of sums of non-independent variables, for instance the m-dependent central limit theorem, the martingale central limit theorem, the central limit theorem for mixing processes and the central limit theorem for convex bodies.

Applications and examples

There are a number of useful and interesting examples arising from the central limit theorem. Below are brief outlines of two such examples and here are a large number of CLT applications, presented as part of the SOCR CLT Activity.

The probability distribution for total distance covered in a random walk (biased or unbiased) will tend toward a normal distribution.
Flipping a large number of coins will result in a normal distribution for the total number of heads (or equivalently total number of tails).

Signal processing

Signals can be smoothed by applying a Gaussian filter, which is just the convolution of a signal with an appropriately scaled Gaussian function. Due to the central limit theorem this smoothing can be approximated by several filter steps that can be computed much faster, like the simple moving average. From the central limit theorem you know, that for achieving a Gaussian of variance σ² you have to apply n filters with windows of variances $\sigma_1^2,\dots,\sigma_n^2$ with $\sigma^2 = \sigma_1^2+\dots+\sigma_n^2$ .

References

Henk Tijms, Understanding Probability: Chance Rules in Everyday Life, Cambridge: Cambridge University Press, 2004.
S. Artstein, K. Ball, F. Barthe and A. Naor, "Solution of Shannon's Problem on the Monotonicity of Entropy", Journal of the American Mathematical Society 17, 975-982 (2004).
S.N.Bernstein, On the work of P.L.Chebyshev in Probability Theory, Nauchnoe Nasledie P.L.Chebysheva. Vypusk Pervyi: Matematika. (Russian) [The Scientific Legacy of P. L. Chebyshev. First Part: Mathematics] Edited by S. N. Bernstein.] Academiya Nauk SSSR, Moscow-Leningrad, 1945. 174 pp.
G. Rempala and J. Wesolowski, "Asymptotics of products of sums and U-statistics", Electronic Communications in Probability, vol. 7, pp. 47-54, 2002.

External links

Animated examples of the CLT
Central Limit Theorem Java
Central Limit Theorem interactive simulation to experiment with various parameters
CLT in NetLogo (Connected Probability - ProbLab) interactive simulation w/ a variety of modifiable parameters
General Central Limit Theorem Activity & corresponding SOCR CLT Applet (Select the Sampling Distribution CLT Experiment from the drop-down list of SOCR Experiments)

Generate sampling distributions in Excel Specify arbitrary population, sample size, and sample statistic.
[1] Another proof.
CAUSEweb.org is a site with many resources for teaching statistics including the Central Limit Theorem

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