Question

Finding the covariance matrix of a least squares estimator

score 169 · Answer 1 · 2017-01-31 01:28:42

169

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Finding the covariance matrix of a least squares estimator

Published on 31 Jan 2017 - 1:28

score 542 · Answer 2 · 2017-02-05 07:45:22

542

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Difference between Covariant derivative notations

Published on 05 Feb 2017 - 7:45

score 675 · Answer 3 · 2017-02-06 22:10:04

675

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The Covariance Matrix after a Functional Transformation

Published on 06 Feb 2017 - 22:10

score 212 · Answer 4 · 2017-02-07 01:57:34

212

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Covariance matrices for jointly gaussian distributions

Published on 07 Feb 2017 - 1:57

score 3.9k · Answer 5 · 2017-02-07 23:53:37

3.9k

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Covariance of two dice rolls

Published on 07 Feb 2017 - 23:53

score 66 · Answer 6 · 2017-02-09 10:13:16

66

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Covariance Kernel of $X^1(t)$,where X(t) is a stationary Gaussian Process

Published on 09 Feb 2017 - 10:13

score 58 · Answer 7 · 2017-02-11 00:36:33

58

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Simplification of product of covariances for gaussians

Published on 11 Feb 2017 - 0:36

score 23 · Answer 8 · 2017-02-12 00:39:28

23

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Covariance equality for independent Gaussians

Published on 12 Feb 2017 - 0:39

score 65 · Answer 9 · 2026-03-29 15:33:03

65

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Which is the correct way to calculate the standard deviation in this situation?

Published on 29 Mar 2026 - 15:33

score 2.5k · Answer 10 · 2017-02-13 19:45:57

2.5k

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Covariance between a random variable and a decreasing function of it is negative?

Published on 13 Feb 2017 - 19:45

score 87 · Answer 11 · 2017-02-15 05:04:32

87

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If I have two random variables $X,Y$ , $X>0$ when $Y<0$ and $X<0$ when $Y>0$ then $\operatorname{cov}(X,Y)<0$?

Published on 15 Feb 2017 - 5:04

score 15.4k · Answer 12 · 2026-03-31 22:27:58

15.4k

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Why are the eigenvalues of a covariance matrix equal to the variance of its eigenvectors?

Published on 31 Mar 2026 - 22:27

score 49 · Answer 13 · 2017-02-18 15:16:33

49

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Inference about heterogeneity of sample from normal population looking at covariance matrix

Published on 18 Feb 2017 - 15:16

score 1.2k · Answer 14 · 2017-02-22 18:28:36

How to prove that $\operatorname{Cov}(\cdot, \cdot)$ is symmetric and bilinear, where $(\cdot,\cdot)$ represents the dot product of two vectors

score 1.2k · Answer 15 · 2026-03-28 21:51:46

1.2k

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Derivative of Symmetric Positive Definite Matrix w.r.t. to its Lower Triangular Cholesky Factor

Published on 28 Mar 2026 - 21:51

List Question

Finding the covariance matrix of a least squares estimator

Difference between Covariant derivative notations

The Covariance Matrix after a Functional Transformation

Covariance matrices for jointly gaussian distributions

Covariance of two dice rolls

Covariance Kernel of $X^1(t)$,where X(t) is a stationary Gaussian Process

Simplification of product of covariances for gaussians

Covariance equality for independent Gaussians

Which is the correct way to calculate the standard deviation in this situation?

Covariance between a random variable and a decreasing function of it is negative?

If I have two random variables $X,Y$ , $X>0$ when $Y<0$ and $X<0$ when $Y>0$ then $\operatorname{cov}(X,Y)<0$?

Why are the eigenvalues of a covariance matrix equal to the variance of its eigenvectors?

Inference about heterogeneity of sample from normal population looking at covariance matrix

How to prove that $\operatorname{Cov}(\cdot, \cdot)$ is symmetric and bilinear, where $(\cdot,\cdot)$ represents the dot product of two vectors

Derivative of Symmetric Positive Definite Matrix w.r.t. to its Lower Triangular Cholesky Factor

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