Probability theory and stochastic processes Definition
Independent sigma-algebras
Definition
Let be a probability space, let be an index set, and for each let be a σ-algebra on . The family is independent if for each finite nonempty subset and each choice of events for ,
Remarks
Events are mutually independent if and only if the σ-algebras are independent. Random variables are independent when the σ-algebras that they generate are independent. To check independence, it is enough to check the product formula on a generating family of each that is closed under finite intersections. This follows from Dynkin's π-λ theorem.
Reading path
To understand this page from the ground up, read these 32 pages in order. The foundations come first.
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- no evidence level
- Construction of the reals from Cauchy sequences concept
- Element definition
- Integers object
- Ordered field definition
- Proposition concept
- Random experiment concept
- Rational numbers object
- Set definition
- Ring of integers statement
- Disjunction definition
- Implication definition
- Predicate definition
- Union definition
- Sample space definition
- The rationals form a field statement
- Universal quantifier definition
- Subset definition
- The rationals form an ordered field statement
- Power set definition
- Universal set definition
- Order on the reals definition
- Complement definition
- Interval definition
- Sigma-algebra definition
- Event definition
- Probability axioms concept
- Probability measure definition
- Probability space definition
- Conditional probability definition
- Independence of two events definition
- Mutual independence of events definition
- Independent sigma-algebras definition
Where this leads
149 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
- Independence of 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), Independent σ-algebras
About this page
| Tag | 09T8ZHST1V |
|---|---|
| URI | m2://09T8ZHST1V |
| Kind | definition |
| Writing | W1 stated |
| MSC | 60A05 |
| Defines | Independent sigma-algebras |
| Uses | Sigma-algebra, Mutual independence of events |
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