Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
On the Asymptotic Normality of an Estimate of a Regression Functional
Authors: László Györfi, Harro Walk
JMLR 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | An estimate of the second moment of the regression function is introduced. Its asymptotic normality is proved such that the asymptotic variance depends neither on the dimension of the observation vector, nor on the smoothness properties of the regression function. The asymptotic variance is given explicitly. Keywords: nonparametric estimation, regression functional, central limit theorem, partitioning estimate |
| Researcher Affiliation | Academia | L aszl o Gy orfi EMAIL Department of Computer Science and Information Theory Budapest University of Technology and Economics Magyar Tud osok k or utja 2., H-1117 Budapest, Hungary Harro Walk EMAIL Department of Mathematics University of Stuttgart Pfaffenwaldring 57, D-70569 Stuttgart, Germany |
| Pseudocode | No | The paper contains mathematical proofs and derivations, but no structured pseudocode or algorithm blocks are present. |
| Open Source Code | No | The paper does not provide any statements regarding the availability of source code, nor does it include links to repositories or supplementary materials containing code. |
| Open Datasets | No | We suppose that the regression estimation problem is based on a sequence (X1, Y1), (X2, Y2), . . . of i.i.d. random vectors distributed as (X, Y ). |
| Dataset Splits | No | The paper is theoretical and does not utilize specific datasets or define any training/test/validation splits. |
| Hardware Specification | No | The paper focuses on theoretical proofs and mathematical analysis; therefore, no hardware specifications for running experiments are provided. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies or versions used for implementation or experimentation. |
| Experiment Setup | No | The paper is theoretical and does not include details on experimental setup, hyperparameters, or training configurations. |