Probability theory and stochastic processes Proposition
Variance of a sum
Statement
Let be real random variables on one probability space with for each . Then In particular, .
Remarks
Proof: for . Expand by the bilinearity of the covariance: . Also . More generally, for real .
Reading path
To understand this page from the ground up, read these 47 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
- 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 space definition
- Random variable definition
- Discrete random variable definition
- Random variable with a density definition
- Expected value definition
- Linearity of expectation statement
- Variance definition
- Covariance definition
- Variance of a sum statement
Where this leads
56 pages build on this one.
- Method of moments
- Weak law of large numbers
- Strong law of large numbers
- Chi-squared test
- Maximum likelihood estimation
- Student's t-test
- Sample variance
- Confidence interval
- Coupon collector's problem
- Student's t-distribution
- Sample mean
- Chi-squared distribution
- Variance of a sum of independent random variables
- Efficiency of an estimator
- Poisson regression
- Ergodic decomposition
- Koksma–Hlawka inequality
- Hodges' superefficient estimator
- Consistency of the maximum likelihood estimator
- M-estimator
- Score test
- Kingman's subadditive ergodic theorem
- Asymptotic normality of the maximum likelihood estimator
- F-distribution
- Sample mean and sample covariance
- Whittle likelihood
- Logistic regression
- Monte Carlo integration
- Breakdown point
- Noncentral chi-squared distribution
- Behrens–Fisher problem
- Kruskal–Wallis test
- Wishart distribution
- Empirical likelihood
- Bayesian information criterion
- Cochran's theorem
- Jackknife
- Birkhoff's ergodic theorem
- Akaike information criterion
- Independence of sample mean and sample variance for normal samples
- Estimating equations
- Generalized method of moments
- Oseledets theorem
- Likelihood-ratio test
- Expectation–maximization algorithm
- Hotelling's T-squared distribution
- Wald test
- Generalized linear model
- Box–Jenkins method
- Pesin theory
- Binomial proportion confidence interval
- Neyman construction
- Wilks' theorem
- Frequentist inference
- Observed information
- Point estimation
Further reading
- Web https://en.wikipedia.org/wiki/Variance, Sum of correlated variables
About this page
| Tag | 0D86GJEZ39 |
|---|---|
| URI | m2://0D86GJEZ39 |
| Kind | statement, proposition |
| Writing | W0 stub |
| MSC | 60A05 |
| Status | This copy of the site has no build data. Run m2 status 0D86GJEZ39. |
| Uses | Variance, Covariance, Linearity of expectation |
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