Last updated: 2018-05-15

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Expand here to see past versions:
    File Version Author Date Message
    html e05bc83 LSun 2018-05-12 Update to 1.0
    rmd cc0ab83 Lei Sun 2018-05-11 update
    html 0f36d99 LSun 2017-12-21 Build site.
    html 853a484 LSun 2017-11-07 Build site.
    html 4f032ad LSun 2017-11-05 transfer
    html 155b297 LSun 2017-05-31 synthetic
    rmd ecd13a3 LSun 2017-05-31 synthetic
    html eb58531 LSun 2017-05-30 synthetic
    rmd 7830a82 LSun 2017-05-30 synthetic

Introduction

Following Gao’s suggestion, we investigate whether Gaussian derivatives can fit the empirical distributions of purely synthetic correlated \(z\) scores simulated as follows.

\[ \begin{array}{rcl} z & = & L_{n \times k} x_k / \sqrt{\text{diag}\left(LL^T\right)} \ ;\\ k &\leq& n \ ; \\ l_{ij} & \sim & N\left(0, 1\right) \ ;\\ x_j & \sim & N\left(0, 1\right) \ ;\\ L & = & \begin{bmatrix} l_1^T \\ \vdots \\ l_n^T \\ \end{bmatrix}_{n \times k} \ ; \\ z_i & = & l_{i}^Tx / \sqrt{l_i^Tl_i} \ . \\ \end{array} \]

Fitting

The coefficients are not fitted by convex optimization, but by the method of moments. Namely, if a density \(f\) can be decomposed by Gaussian derivatives, \[ f\left(z\right) = \sum\limits_{l = 0}^L w_l \frac{1}{\sqrt{l!}}\varphi^{\left(l\right)}\left(z\right) \ , \] then due to the orthonormality of normalized Hermite polynomials, \(w_l\) can be expressed as \[ w_l = \left(-1\right)^l\frac{1}{\sqrt{l!}}\int h_l\left(z\right)f\left(z\right)dz \ . \] Since \(h_l\)’s are polynomials, \(w_l\) is a linear combination of moments under \(f\), and can thus be estimated by sample moments, also called Hermite moments.

Examples

Remarks

The coefficients \(\hat w_l\) estimated by the method of moments are not very satisfying even with \(50\) Gaussian derivatives. The reason might be that completely synthetic correlated data are less likely to have samples on the extreme tails, as observed in the histograms, yet these extreme samples are supposed to have disproportional influence on the method of moments estimates. We also tried to estimate \(\hat w_l\) by the convex optimization approach, but the results were even worse, probably due to the same reason. The results might indicate an interesting but often neglected difference between real data and synthetic ones.

Session information

sessionInfo()
R version 3.4.3 (2017-11-30)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS High Sierra 10.13.4

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

loaded via a namespace (and not attached):
 [1] workflowr_1.0.1   Rcpp_0.12.16      digest_0.6.15    
 [4] rprojroot_1.3-2   R.methodsS3_1.7.1 backports_1.1.2  
 [7] git2r_0.21.0      magrittr_1.5      evaluate_0.10.1  
[10] stringi_1.1.6     whisker_0.3-2     R.oo_1.21.0      
[13] R.utils_2.6.0     rmarkdown_1.9     tools_3.4.3      
[16] stringr_1.3.0     yaml_2.1.18       compiler_3.4.3   
[19] htmltools_0.3.6   knitr_1.20

This reproducible R Markdown analysis was created with workflowr 1.0.1