Group Additive Structure Identification for Kernel Nonparametric Regression
Authors: Chao Pan, Michael Zhu
NeurIPS 2017 | Conference 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 panchao25@gmail.com Michael Zhu Department of Statistics, Purdue University West Lafayette, IN 47906 Center for Statistical Science Department of Industrial Engineering Tsinghua University, Beijing, China yuzhu@purdue.edu |
| 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). |