Characteristic function (probability theory)

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In probability theory, the characteristic function of any random variable completely defines its probability distribution. On the real line it is given by the following formula, where X is any random variable with the distribution in question:

where t is a real number, i is the imaginary unit, and E denotes the expected value.

If F_X is the cumulative distribution function, then the characteristic function is given by the Riemann-Stieltjes integral

In cases in which there is a probability density function, f_X, this becomes

If X is a vector-valued random variable, one takes the argument t to be a vector and tX to be a dot product.

Every probability distribution on R or on Rⁿ has a characteristic function, because one is integrating a bounded function over a space whose measure is finite.

Contents 1 The inversion theorem 2 The continuity theorem 3 Uses of characteristic functions 4 Related concepts

[edit] The inversion theorem

More than that, there is a bijection between cumulative probability distribution functions and characteristic functions. In other words, two distinct probability distributions never share the same characteristic function.

Given a characteristic function φ, it is possible to reconstruct the corresponding cumulative probability distribution function F:

In general this is an improper integral; the function being integrated may be only conditionally integrable rather than Lebesgue integrable, i.e. the integral of its absolute value may be infinite.

[edit] The continuity theorem

If the sequence of characteristic functions of distributions F_n converges to the characteristic function of a distribution F, then F_n(x) converges to F(x) at every value of x at which F is continuous.

[edit] Uses of characteristic functions

Characteristic functions are particularly useful for dealing with functions of independent random variables. For example, if X₁, X₂, ..., X_n is a sequence of independent (and not necessarily identically distributed) random variables, and

where the a_i are constants, then the characteristic function for S_n is given by

In particular, . To see this, write out the definition of characteristic function:

.

Observe that the independence of and is required to establish the equality of the third and fourth expressions.

Because of the continuity theorem, characteristic functions are used in the most frequently seen proof of the central limit theorem.

Characteristic functions can also be used to find moments of random variable. Provided that n^th moment exists, characteristic function can be differentiated n times and

Characteristic functions arise in the statement and proof of Bochner's theorem.

[edit] Related concepts

Related concepts include the moment-generating function and the probability-generating function. The characteristic function exists for all probability distributions. However this is not the case for moment generating function.

The characteristic function is closely related to the Fourier transform: the characteristic function of a probability density function is the complex conjugate of the continuous Fourier transform of (according to the usual convention; see [1]).

where denotes the continuous Fourier transform of the probability density function . Likewise, may be recovered from through the inverse Fourier transform:

Indeed, even when the random variable does not have a density, the characteristic function may be seen as the Fourier transform of the measure corresponding to the random variable.

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This page uses content from the English-language version of Wikipedia. The original article was at Characteristic function (probability theory). The list of authors can be seen in the page history. As with Psychology Wiki, the text of Wikipedia is available under the GNU Free Documentation License.