STORE: Sparse Tensor Response Regression and Neuroimaging Analysis
Authors: Will Wei Sun, Lexin Li
JMLR 2017 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate the efficacy of our model through intensive simulations and an analysis of the Autism spectrum disorder neuroimaging data. |
| Researcher Affiliation | Academia | Will Wei Sun EMAIL Department of Management Science University of Miami School of Business Administration Miami, FL 33146, USA; Lexin Li EMAIL Division of Biostatistics University of California Berkeley, CA 94720, USA |
| Pseudocode | Yes | Algorithm 1 Alternating updating algorithm for STORE. |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | The data is from the Autism Brain Imaging Data Exchange (ABIDE), a study for autism spectrum disorder (ASD) (Di Martino et al., 2014). |
| Dataset Splits | No | The paper describes the total number of subjects and their categories (ASD vs. normal controls) for the real data analysis (795 subjects, 362 ASD, 433 normal controls) but does not specify training, validation, or test splits for this data. For simulations, it mentions varying sample sizes n {20, 100}, but not explicit dataset splits. |
| Hardware Specification | Yes | The code is written in R and is implemented on a laptop with 2.5 GHz Intel Core i7 processor. |
| Software Dependencies | No | The code is written in R and is implemented on a laptop with 2.5 GHz Intel Core i7 processor. This mentions R, but no specific version number or other software libraries with their versions are provided. |
| Experiment Setup | Yes | For STORE, the tuning parameters were chosen according to the BIC criterion in Section 3.2 by setting K = {1, 2, . . . , 10} and S = {0.1, 0.2, . . . , 0.9}. The tunings for Sparse OLS and HOLRR were conducted based on their recommended approaches. |