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### Lesson Title
High-dimensional Statistics with R
### Lesson Repository URL
https://github.com/carpentries-incubator/high-dimensional-stats-r
### Lesson Website URL
https://carpentries-incubato…
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related to
#2765 #4143 traditional/old statistics for covariance and correlation structures and tests
#3197 penalized and shrinkage estimator for covariances or inverse covariances
These are ju…
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Submitting Author Name: Kellie J. Archer
Submitting Author Github Handle: @kelliejarcher
Other Package Authors Github handles:
Repository: https://github.com/kelliejarcher/hdcuremodels
Submi…
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In high dimensional statistics, we usually rely on CSC and CSR arrays when matrices are very sparse. It is particularly for speeding up the coordinate descent procedure.
I can implement that in th…
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question on mailing list for sparse VAR
https://groups.google.com/d/msg/pystatsmodels/NWurUtjX9Qg/98xpoTIuBQAJ
reference
Davis, Richard A., Pengfei Zang, and Tian Zheng. 2015. “Sparse Vector Autoregr…
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### Describe the workflow you want to enable
Currently, there is no included implementation of a PCA algorithm made for handling binary data in the scikit-learn library. However, the algorithm for …
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In the README, only basic summary statistics are mentioned: mean, median, mode and I'm guessing std. dev, variance, correlations, etc are also to be included. I'm curious about how far out you are int…
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(parking a reference to computational detail)
how do we normalize a scatter matrix so that it is consistent for specific distribution, commonly the normal
cov = sigma = c scatter -> find "size" c
re…
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The ensemble Kalman filter was introduced in Evans (1994) and has become an important technique in high dimensional forecasting. The method has connections to approximate Bayesian computation (Nott et…
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Sparse Laplacian Shrinkage combines a L1 based penalty and a quadratic informative penalty, similar to glm-net but with structured L2 penalization matrix
Sparse Laplacian Shrinkage is the first stran…