Wavelet regression and additive models for irregularly spaced data
Authors: Asad Haris, Ali Shojaie, Noah Simon
NeurIPS 2018 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We complement our theoretical results with empirical studies comparing wave Mesh to existing methods. (Abstract) and 4 Numerical experiments (Section title) |
| Researcher Affiliation | Academia | Asad Haris Department of Biostatistics University of Washington Seattle, WA 98195 EMAIL Noah Simon Department of Biostatistics University of Washington Seattle, WA 98195 EMAIL Ali Shojaie Department of Biostatistics University of Washington Seattle, WA 98195 EMAIL |
| Pseudocode | No | The paper describes the algorithm in text (Section 2.3) and presents an iterative scheme (Equation 8), but does not provide a formally structured pseudocode or algorithm block. |
| Open Source Code | No | The R package wave Mesh, which implements our methodology, will soon be publicly available on Git Hub. |
| Open Datasets | Yes | We also analyze the motorcycle data studied by Silverman [1985] consisting of 133 head acceleration measurements in a simulated motorcycle accident taken at 94 unequally spaced time points. and For a real world data analysis, we consider the Boston housing data analyzed by Ravikumar et al. [2009]. |
| Dataset Splits | Yes | Selection of tuning parameter for wave Mesh is done via 5-fold cross validation. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | Yes | The former two methods are implemented in the R package wavethres [Nason, 2016] and the latter is implemented in the adlift package [Nunes and Knight, 2017]. and Guy Nason. wavethresh: Wavelets Statistics and Transforms, 2016. URL https://CRAN. R-project.org/package=wavethresh. R package version 4.6.8. |
| Experiment Setup | Yes | We apply our proposal, wave Mesh, the interpolation proposal of Kovac and Silverman [2000] and isometric wavelet proposal of Sardy et al. [1999], for a sequence of 50 λ values linear on the log scale and select the λ value that minimizes the mean square error, MSE = n 1 f 0 b f 2 2. |