Research map / Probabilistic and scientific modeling / Molecules and proteins
kernelPSI: a Post-Selection Inference Framework for Nonlinear Variable Selection
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Selecting kernels before testing (selective kernelPSI) instead of non-selective testing on all kernels | kernelPSI variants vs KRR/KPCR/HSIC · Arabidopsis FT_GH GWAS and simulations | statistical power / significant genes | Helped |
| Using fewer selected kernels (S'=1 rather than larger S') (ablation) | kernelPSI selection · Gaussian simulated data | statistical power and precision | Helped |
| Using HSIC for kernel selection and inference vs KRR/KPCR prototypes (ablation) | kernelPSI · Gaussian simulated data | statistical power | Helped |
| Adaptive selection of S' instead of fixed S' (ablation) | kernelPSI · Gaussian simulated data | precision/recall of causal kernels | No clear effect |
| Sampling-based conditional p-values under null | kernelPSI · Gaussian simulated data, theta=0 | p-value uniformity | No clear effect |
| Using kernel-based selection (kernelPSI) instead of linear prototype lasso | kernelPSI vs protoLASSO · Gaussian and binary simulated data | statistical power | Helped |
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