Probability theory and stochastic processes Definition
Convergence in distribution
Definition
Let be real random variables, possibly on different probability spaces, with cumulative distribution functions . The sequence converges in distribution to if for each at which is continuous. One writes .
Remarks
By the portmanteau theorem, an equivalent condition is for each bounded continuous . This form extends to random variables in metric spaces. The condition depends only on the laws. So it is the weak convergence of the laws of the to the law of . The restriction to continuity points is necessary. For and , for all , but . The central limit theorem is a result about convergence in distribution.
Reading path
To understand this page from the ground up, read these 41 pages in order. The foundations come first.
- 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
- 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
- Universal set definition
- Order on the reals definition
- Power set definition
- Complement definition
- Function definition
- Interval definition
- Preimage of a set definition
- Sigma-algebra 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
- Convergence in distribution definition
Where this leads
22 pages build on this one.
- De Moivre–Laplace theorem
- Convergence in probability implies convergence in distribution
- Central limit theorem
- Lévy's continuity theorem
- Convergence of random variables
- Poisson limit theorem
- Koksma–Hlawka inequality
- Hodges' superefficient estimator
- Cramér–Wold theorem
- Pickands–Balkema–De Haan theorem
- Asymptotic normality of the maximum likelihood estimator
- Edgeworth expansion
- Erdős–Kac theorem
- Continuous mapping theorem
- Monte Carlo integration
- Delta method
- Hájek–Le Cam convolution theorem
- Kruskal–Wallis test
- Slutsky's theorem
- Berry–Esseen theorem
- Wilks' theorem
- Local asymptotic normality
Further reading
- Web https://en.wikipedia.org/wiki/Convergence_of_random_variables, Convergence in distribution
About this page
| Tag | 0JDAQHWA9N |
|---|---|
| URI | m2://0JDAQHWA9N |
| Kind | definition |
| Writing | W1 stated |
| MSC | 60F05 |
| Defines | Convergence in distribution |
| Uses | Cumulative distribution function, Random variable |
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What links here
Used by
- De Moivre–Laplace theorem
- Convergence in probability implies convergence in distribution
- Central limit theorem
- Lévy's continuity theorem
- Convergence of random variables
- Poisson limit theorem
- Hodges' superefficient estimator
- Pickands–Balkema–De Haan theorem
- Asymptotic normality of the maximum likelihood estimator
- Edgeworth expansion
- Delta method
- Hájek–Le Cam convolution theorem
- Kruskal–Wallis test
- Wilks' theorem
- Local asymptotic normality