Stability and L2-penalty in Model Averaging
Authors: Hengkun Zhu, Guohua Zou
JMLR 2024 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the usefulness of the proposed method with a Monte Carlo simulation and application to a prediction task on the wage1 dataset. Keywords: Model averaging, Stability, Mean squared prediction error, L2-penalty |
| Researcher Affiliation | Academia | Hengkun Zhu EMAIL Guohua Zou EMAIL School of Mathematical Sciences Capital Normal University Beijing 100048, China |
| Pseudocode | Yes | Algorithm 1 Optimal weighting based on cross validation |
| Open Source Code | No | The paper mentions using "regsubsets in leaps package of R language" for one of its analyses, which refers to a third-party tool. However, it does not explicitly state that the authors' own code for the proposed methodology (L2-penalty model averaging) is being released or provide a link to it. |
| Open Datasets | Yes | In this section, we apply the proposed method to the real wage1 dataset in Wooldridge (2003) from the US Current Population Survey for the year 1976. There are 526 observations in this dataset. |
| Dataset Splits | Yes | We randomly divide the data into two parts: a training sample S of n observations for estimating the models and a test sample St of nt = 529 n observations for validating the results. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the simulations or real data analysis, such as GPU or CPU models, memory, or other computer specifications. |
| Software Dependencies | No | The paper mentions "R language" and the "leaps package" (Section 5) but does not specify any version numbers for these software components or any other libraries used. |
| Experiment Setup | Yes | For Algorithm 1, we set L = 100, B = 10, l = 50 and b = 5. |