Network Granger Causality with Inherent Grouping Structure
Authors: Sumanta Basu, Ali Shojaie, George Michailidis
JMLR 2015 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The performance of the proposed methodology is assessed through an extensive set of simulation studies and comparisons with existing techniques. The study is illustrated on two motivating examples coming from functional genomics and financial econometrics. |
| Researcher Affiliation | Academia | Sumanta Basu EMAIL Department of Statistics University of Michigan Ann Arbor, MI 48109-1092, USA Ali Shojaie EMAIL Department of Biostatistics University of Washington Seattle, WA, USA George Michailidis EMAIL Department of Statistics University of Michigan Ann Arbor, MI 48109-1092, USA |
| Pseudocode | No | The paper describes mathematical models and theoretical properties but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The R package grpreg (Breheny and Huang, 2009) was used to obtain the group lasso estimates. (The paper mentions using a third-party R package, but does not provide specific access to code developed for the methodology described in this paper by the authors.) |
| Open Datasets | Yes | The data for this application come from Rangel et al. (2004), where expression patterns of genes involved in T-cell activation were studied... The data for this application... were directly obtained from the Federal Deposit Insurance Corporation (FDIC) database (available at www.fdic.gov). |
| Dataset Splits | Yes | Using a 19 : 1 sample-splitting, the tuning parameter λ is chosen from an interval of the form [C1λe, C2λe], C1, C2 > 0, where λe = p 2 log p/n for lasso and p 2 log G/n for group lasso. The thresholding parameters are selected as δgrp = 0.7λσ at the group level and δmisspec = n 0.2 within groups. These parameters are chosen by conducting a 20-fold cross-validation on independent tuning data sets of same sizes, using intervals of the form [C3λ, C4λ] for δgrp and {n δ, δ [0, 1]} for δmisspec. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The R package grpreg (Breheny and Huang, 2009) was used to obtain the group lasso estimates. (The specific version number for the 'grpreg' package is not provided.) |
| Experiment Setup | Yes | Using a 19 : 1 sample-splitting, the tuning parameter λ is chosen from an interval of the form [C1λe, C2λe], C1, C2 > 0, where λe = p 2 log p/n for lasso and p 2 log G/n for group lasso. The thresholding parameters are selected as δgrp = 0.7λσ at the group level and δmisspec = n 0.2 within groups. These parameters are chosen by conducting a 20-fold cross-validation on independent tuning data sets of same sizes, using intervals of the form [C3λ, C4λ] for δgrp and {n δ, δ [0, 1]} for δmisspec. ... The parameters λ and δgrp were chosen using a 19 : 1 sample-splitting method and the misspecification threshold δmisspec was set to zero as the grouping structure was reliable. |