A Kernel Independence Test for Random Processes
Authors: Kacper Chwialkowski, Arthur Gretton
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Tests on artificial data and real-world forex data indicate that the new test procedure discovers dependence which is missed by linear approaches, while the earlier bootstrap procedure returns an elevated number of false positives. |
| Researcher Affiliation | Academia | Kacper Chwialkowski KACPER.CHWIALKOWSKI@GMAIL.COM University College London, Computer Science Department Arthur Gretton ARTHUR.GRETTON@GMAIL.COM University College London, Gatsby Computational Neuroscience Unit |
| Pseudocode | Yes | Algorithm 1 Generate innovations |
| Open Source Code | No | The paper does not provide any statements or links indicating that source code for the methodology is openly available. |
| Open Datasets | No | The paper mentions 'artificial data' and 'real-world forex data' but does not provide specific links, DOIs, repository names, or formal citations for publicly available datasets. |
| Dataset Splits | No | The paper mentions 'sample size 1200' and '720 samples' but does not specify any training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers. |
| Experiment Setup | Yes | Processes used in this experiment had an autoregressive component of 0.2, and the radius of the innovation process was 1. We set an extinction rate to 50%. The AR component a in the model (6) controls the memory of a processes the larger this component, the longer the memory. We set an extinction rate to 50%. |