Group Additive Structure Identification for Kernel Nonparametric Regression
Authors: Chao Pan, Michael Zhu
NeurIPS 2017 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Simulation study and real data applications demonstrate the effectiveness of the proposed method as a general tool for high dimensional nonparametric regression. |
| Researcher Affiliation | Academia | Pan Chao Department of Statistics Purdue University West Lafayette, IN 47906 EMAIL Michael Zhu Department of Statistics, Purdue University West Lafayette, IN 47906 Center for Statistical Science Department of Industrial Engineering Tsinghua University, Beijing, China EMAIL |
| Pseudocode | Yes | The two-step estimation is summarized in Algorithm 1. When a model contains a large number of predictor variables, such exhaustive search suffers high computational cost. In order to apply GASI on a large model, we propose a backward stepwise algorithm which is illustrated in Algorithm 2. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the described methodology. |
| Open Datasets | Yes | In this section, we report the results of applying GASI on the Boston Housing data (another real data application is reported in the supplementary material). |
| Dataset Splits | Yes | Split data into training (T ) and validation (V) sets. the tuning parameters µ and α are selected via 10-fold CV. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | The grid values of µ are equally spaced in [1e 10, 1/64] on a log-scale and each α is an integer in [1, 10]. The noise ϵ is i.i.d. N(0, 0.012). |