Probability theory and stochastic processes Definition
Independence of random variables
Builds on Random variableMutual independence of eventsIndependent sigma-algebras
Definition
Let be a probability space and let be an index set. For each , let be a random variable with values in . The family is independent if for each finite nonempty set and each choice of for ,
Remarks
Equivalently, the σ-algebras are independent. For two random variables this is the independence of two random variables. Mutual independence implies pairwise independence, but the converse is false, as for events. For real random variables , independence holds if and only if the joint cumulative distribution function is the product of the marginal ones.
Reading path
To understand this page from the ground up, read these 43 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 space definition
- Conditional probability definition
- Random variable definition
- Independence of two events definition
- Mutual independence of events definition
- Independent sigma-algebras definition
- Independence of random variables definition
Where this leads
148 pages build on this one.
- Bias of an estimator
- Characteristic function of a sum of independent random variables
- Bernoulli process
- Statistic
- Likelihood function
- Poisson point process
- De Moivre–Laplace theorem
- Kolmogorov's zero–one law
- Statistical hypothesis test
- Method of moments
- Independent and identically distributed random variables
- Weak law of large numbers
- Random walk
- Strong law of large numbers
- Neyman–Pearson lemma
- P-value
- Chi-squared test
- Central limit theorem
- Maximum likelihood estimation
- Galton–Watson process
- Student's t-test
- Sample variance
- Bayesian inference
- Confidence interval
- Coupon collector's problem
- Student's t-distribution
- Sum of independent normal random variables
- Cumulant generating function
- Sample mean
- Type I and type II errors
- Generating function of a sum of independent random variables
- Chi-squared distribution
- Estimator
- Variance of a sum of independent random variables
- Mean squared error
- Gambler's ruin
- Hoeffding's inequality
- Moment-generating function of a sum of independent random variables
- Consistent estimator
- Sum of independent Poisson random variables
- Law of large numbers
- Bernstein–von Mises theorem
- Efficiency of an estimator
- Log-rank test
- Poisson regression
- Sequential analysis
- Ergodic decomposition
- Koksma–Hlawka inequality
- Hodges' superefficient estimator
- Mathematical statistics
- F-test
- Boolean model
- Wald–Wolfowitz optimality of the sequential probability ratio test
- Sequential probability ratio test
- Consistency of the maximum likelihood estimator
- Bayes factor
- Pickands–Balkema–De Haan theorem
- Basu's theorem
- Order statistic
- Lindley's paradox
- M-estimator
- Kernel regression
- Score test
- Kingman's subadditive ergodic theorem
- Multivariate analysis of variance
- McDiarmid's inequality
- Asymptotic normality of the maximum likelihood estimator
- Mann–Whitney U test
- Kolmogorov automorphism
- Score
- F-distribution
- Edgeworth expansion
- Monotone likelihood ratio
- Sample mean and sample covariance
- Wilcoxon signed-rank test
- Minimal sufficient statistic
- Whittle likelihood
- Logistic regression
- Cramér–von Mises criterion
- Azuma's inequality
- Kernel density estimation
- Erdős–Kac theorem
- Robust statistics
- Bias–variance decomposition
- Monte Carlo integration
- Breakdown point
- Delta method
- Noncentral chi-squared distribution
- Hájek–Le Cam convolution theorem
- Behrens–Fisher problem
- Hodges–Lehmann estimator
- Kruskal–Wallis test
- Sampling distribution
- Wishart distribution
- Statistical inference
- Minimum-variance unbiased estimator
- Sign test
- Empirical likelihood
- Dvoretzky–Kiefer–Wolfowitz inequality
- Bayesian information criterion
- Berry–Esseen theorem
- Cochran's theorem
- Jackknife
- Birkhoff's ergodic theorem
- Akaike information criterion
- Hewitt–Savage zero–one law
- Credible interval
- Independence of sample mean and sample variance for normal samples
- Estimating equations
- Anderson–Darling test
- Family-wise error rate
- Dickey–Fuller test
- Generalized method of moments
- Uniformly most powerful test
- Empirical distribution function
- Bootstrap
- Oseledets theorem
- Ancillary statistic
- Complete statistic
- Horvitz–Thompson estimator
- U-statistic
- L-estimator
- Power of a test
- Sufficient statistic
- Missing data mechanisms
- Likelihood-ratio test
- Karlin–Rubin theorem
- Conjugate prior
- Kolmogorov–Smirnov test
- Expectation–maximization algorithm
- Hotelling's T-squared distribution
- Cross-validation
- Wald test
- Generalized linear model
- Box–Jenkins method
- Pesin theory
- Binomial proportion confidence interval
- False discovery rate and the Benjamini–Hochberg procedure
- Neyman construction
- Wilks' theorem
- Permutation test
- Fisher's exact test
- Bonferroni correction
- Frequentist inference
- Likelihood principle
- Fisher–Neyman factorization theorem
- Observed information
- Point estimation
Further reading
- Web https://en.wikipedia.org/wiki/Independence_(probability_theory), More than two random variables
About this page
| Tag | 0XCE070V8B |
|---|---|
| URI | m2://0XCE070V8B |
| Kind | definition |
| Writing | W1 stated |
| MSC | 60A05 |
| Defines | Independence of random variables |
| Uses | Random variable, Mutual independence of events, Independent sigma-algebras |
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What links here
Used by
- Characteristic function of a sum of independent random variables
- Poisson point process
- Kolmogorov's zero–one law
- Independent and identically distributed random variables
- Chi-squared test
- Student's t-distribution
- Generating function of a sum of independent random variables
- Variance of a sum of independent random variables
- Hoeffding's inequality
- Moment-generating function of a sum of independent random variables
- Sum of independent Poisson random variables
- Basu's theorem