Probability theory and stochastic processes Expectation, moments and transforms Theorem
The characteristic function determines the distribution
Builds on Characteristic functionLaw of a random variableCumulative distribution function
Statement
Let and be real random variables with characteristic functions and . If for all , then and have the same law.
Remarks
The proof uses the Lévy inversion formula. For real that are continuity points of the cumulative distribution function , If , then has the continuous density . The same result holds for random vectors in , with .
Reading path
To understand this page from the ground up, read these 53 pages in order. The foundations come first.
- Complex numbers object
- Construction of the reals from Cauchy sequences concept
- Element definition
- Integers object
- Ordered field definition
- Proposition concept
- Random experiment concept
- Rational numbers object
- Real numbers object
- Euler's formula statement
- Set definition
- Ring of integers statement
- Disjunction definition
- Implication definition
- Predicate definition
- Ordered pair definition
- Union definition
- Sample space definition
- The rationals form a field statement
- Existential quantifier definition
- Universal quantifier definition
- Cartesian product definition
- Subset definition
- The rationals form an ordered field statement
- Uniqueness quantifier definition
- Binary relation definition
- Power set definition
- Universal set definition
- Order on the reals definition
- Function definition
- Complement definition
- Interval definition
- Injective function definition
- Preimage of a set definition
- Sigma-algebra definition
- Countable set definition
- Event definition
- Borel sigma-algebra definition
- Probability axioms concept
- Probability measure definition
- Probability distribution definition
- Probability space definition
- Random variable definition
- Cumulative distribution function definition
- Discrete random variable definition
- Law of a random variable definition
- Random variable with a density definition
- Probability mass function definition
- Expected value definition
- Probability density function definition
- Law of the unconscious statistician statement
- Characteristic function definition
- The characteristic function determines the distribution statement
Where this leads
12 pages build on this one.
- De Moivre–Laplace theorem
- Central limit theorem
- Lévy's continuity theorem
- Sum of independent normal random variables
- Koksma–Hlawka inequality
- Cramér–Wold theorem
- Asymptotic normality of the maximum likelihood estimator
- Edgeworth expansion
- Erdős–Kac theorem
- Monte Carlo integration
- Delta method
- Berry–Esseen theorem
Further reading
About this page
| Tag | 0T8BYAFEXX |
|---|---|
| URI | m2://0T8BYAFEXX |
| Aliases | uniqueness-theorem-for-characteristic-functions, levy-inversion-formula |
| Kind | statement, theorem |
| Writing | W0 stub |
| MSC | 60E10 |
| Status | This copy of the site has no build data. Run m2 status 0T8BYAFEXX. |
| Uses | Characteristic function, Law of a random variable, Cumulative distribution function |
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