Timeline for how to generate specific random covariance matrices?
Current License: CC BY-SA 4.0
12 events
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| Jul 23, 2020 at 15:27 | comment | added | whuber♦ | It's not clear what you mean by "feasible results." Your examples don't meet the constraints of the question on the correlation coefficients -- what else is there to say? | |
| Jul 23, 2020 at 14:34 | comment | added | AJKOER | So, I have demonstrated theoretical transparency, accessibility of implementation, and feasible results. Yet, the theoretical claim of a Wishart approach is unchallenged, excuse me, I hereby challenge its feasibility and accuracy. | |
| Jul 15, 2020 at 20:58 | history | edited | AJKOER | CC BY-SA 4.0 |
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| Jul 15, 2020 at 20:48 | comment | added | AJKOER | I thanks those who challenged the feasibility of my flexible hands-on approach to generating random matrices capable of addressing my questions on, for example, the robustness of correlation matrices and/or covariance matrix by examining simulation runs with select outlier introduction (by, for example, creating a mixture of distributions in the generating step to introduce a noise model). | |
| Jul 15, 2020 at 20:38 | history | edited | AJKOER | CC BY-SA 4.0 |
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| Jul 15, 2020 at 20:31 | history | edited | AJKOER | CC BY-SA 4.0 |
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| Jun 11, 2020 at 14:32 | history | edited | CommunityBot |
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| Apr 12, 2020 at 12:14 | history | edited | AJKOER | CC BY-SA 4.0 |
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| Apr 12, 2020 at 11:42 | history | edited | AJKOER | CC BY-SA 4.0 |
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| Apr 12, 2020 at 11:06 | comment | added | AJKOER | Note, the problem cites correlations approaching 0.9, do you really expect no numerical analysis outliers to be created to distort the analysis? Construct a theoretical model and do test runs in whatever path you deem acceptable. | |
| Apr 12, 2020 at 10:48 | comment | added | AJKOER | To be clear, if one simulates a theoretical regression relationship between Y and X, one has, per that simulation run, a sample covariance matrix. Repeat the exercise k times and arrive at a set of k random covariance matrices, which should, at some point, meaningfully converge to a mean matrix and with an associated variability. The latter can be performed in a freely available google spreadsheet and can ID a matrix inverse approaching a singularity. In other words, my approach is not only intuitive but also transparent and robust approach to a sample Wishart distribution . | |
| Apr 12, 2020 at 4:12 | history | answered | AJKOER | CC BY-SA 4.0 |