Question

Why do polynomial regressions have larger variance at the end?

score 784 · Answer 1 · 2026-04-01 18:14:30

784

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Why do polynomial regressions have larger variance at the end?

Published on 01 Apr 2026 - 18:14

score 146 · Answer 2 · 2015-09-04 19:44:34

146

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Question about joint convergence with covariance

Published on 04 Sep 2015 - 19:44

score 265 · Answer 3 · 2026-03-31 12:13:01

265

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Covariance of binomial variables

Published on 31 Mar 2026 - 12:13

score 1.1k · Answer 4 · 2015-09-08 02:21:49

1.1k

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What is the covariance of two dependent normal distributed random variables

Published on 08 Sep 2015 - 2:21

score 182 · Answer 5 · 2026-03-26 14:42:16

182

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Generating from $N_p(\mu,\Sigma)$ and Cholesky decomposition

Published on 26 Mar 2026 - 14:42

score 803 · Answer 6 · 2015-09-09 22:14:18

803

Views

covariance of 2 poisson RVs

Published on 09 Sep 2015 - 22:14

score 36 · Answer 7 · 2015-09-14 12:03:32

36

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number of independent random variables modulo covariance relations

Published on 14 Sep 2015 - 12:03

score 118 · Answer 8 · 2015-09-16 20:09:50

118

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Covariance of conditional expectation is "orthogonal"

Published on 16 Sep 2015 - 20:09

score 352 · Answer 9 · 2015-09-19 20:22:33

352

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How can the variance of data be represented as this product of the principal direction and the covariance matrix?

Published on 19 Sep 2015 - 20:22

score 50 · Answer 10 · 2026-03-27 02:39:36

50

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How to optimize a singular covariance-weighted residual?

Published on 27 Mar 2026 - 2:39

score 1.7k · Answer 11 · 2015-09-23 12:08:18

1.7k

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Variance of a sum of dependent random variables

Published on 23 Sep 2015 - 12:08

score 425 · Answer 12 · 2015-09-25 08:41:12

425

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For the covariance formula, how are subtraction and multiplication defined for real-valued random variables.

Published on 25 Sep 2015 - 8:41

score 137 · Answer 13 · 2015-09-25 12:43:57

137

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Find all solutions of $f$ such that $ \operatorname{Cov}(f(x),x)=c $ where $x \sim N(\mu,\sigma^2)$ and $c$ does not depend on $(\mu,\sigma^2)$

Published on 25 Sep 2015 - 12:43

score 103 · Answer 14 · 2015-09-30 19:47:45

Prove that Cov$(\sum_{k=j}^\infty \rho^k e_{t-k}, \sum_{k=0}^\infty \rho^k e_{t-j-k} ) = \rho^j \frac{\sigma^2}{1-\rho^2}$, where $e_t$ white noise

score 8.8k · Answer 15 · 2015-09-30 22:06:21

8.8k

Views

Is a symmetric positive definite matrix always diagonally dominant?

Published on 30 Sep 2015 - 22:06

List Question

Why do polynomial regressions have larger variance at the end?

Question about joint convergence with covariance

Covariance of binomial variables

What is the covariance of two dependent normal distributed random variables

Generating from $N_p(\mu,\Sigma)$ and Cholesky decomposition

covariance of 2 poisson RVs

number of independent random variables modulo covariance relations

Covariance of conditional expectation is "orthogonal"

How can the variance of data be represented as this product of the principal direction and the covariance matrix?

How to optimize a singular covariance-weighted residual?

Variance of a sum of dependent random variables

For the covariance formula, how are subtraction and multiplication defined for real-valued random variables.

Find all solutions of $f$ such that $ \operatorname{Cov}(f(x),x)=c $ where $x \sim N(\mu,\sigma^2)$ and $c$ does not depend on $(\mu,\sigma^2)$

Prove that Cov$(\sum_{k=j}^\infty \rho^k e_{t-k}, \sum_{k=0}^\infty \rho^k e_{t-j-k} ) = \rho^j \frac{\sigma^2}{1-\rho^2}$, where $e_t$ white noise

Is a symmetric positive definite matrix always diagonally dominant?

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