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Results tagged with pca
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user 8101
Principal component analysis (PCA) is a linear dimensionality reduction technique. It reduces a multivariate dataset to a smaller set of constructed variables preserving as much information (as much variance) as possible. These variables, called principal components, are linear combinations of the input variables.
3
votes
1
answer
3k
views
Eigenvectors corresponding to eigenvalues
In R, the eigen() returns descending sorted eigenvalues. However, the eigenvectors do not correspond to these sorted eigenvalues. How do I identify the eigenvector corresponding to the ith sorted eige …
- 3,091
4
votes
0
answers
424
views
Technique to remove factor structure from panel data
My preference is to use factor analysis rather than PCA. …
- 3,091
2
votes
0
answers
490
views
Approximate vs. Strict Factor model specification in R [closed]
Background:
Generally, pooled time-series cross-sectional regressions utilize a strict factor model (i.e. require the covariance of residuals is zero). However, in time series such as security returns …
- 3,091
31
votes
7
answers
41k
views
Testing for linear dependence among the columns of a matrix
I have a correlation matrix of security returns whose determinant is zero. (This is a bit surprising since the sample correlation matrix and the corresponding covariance matrix should theoretically be …
- 3,091