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].
Concentration inequalities for empirical processes of linear time series
Authors: Likai Chen, Wei Biao Wu
JMLR 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The paper considers suprema of empirical processes for linear time series indexed by functional classes. We derive an upper bound for the tail probability of the suprema under conditions on the size of the function class, the sample size, temporal dependence and the moment conditions of the underlying time series. The primary goal of the paper is to establish a concentration inequality for T(z) in (1) for the linear process (3). |
| Researcher Affiliation | Academia | Likai Chen EMAIL Wei Biao Wu EMAIL Department of Statistics The University of Chicago Chicago, IL 60637, USA |
| Pseudocode | No | The paper is highly theoretical, focusing on mathematical derivations, theorems, lemmas, and proofs of concentration inequalities. It does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper includes a copyright and license statement for the paper itself ('c 2018 Likai Chen and Wei Biao Wu. License: CC-BY 4.0, see https://creativecommons.org/licenses/by/4.0/. Attribution requirements are provided at http://jmlr.org/papers/v18/17-012.html.'), but there is no explicit statement or link indicating the release of source code for the methodology described in the paper. |
| Open Datasets | No | The paper introduces theoretical models for time series data, such as the MA( ) process, and discusses properties of random variables (e.g., 'innovations ϵi, i Z, are i.i.d random variables'). It does not involve or refer to any specific publicly available datasets for empirical validation or experimentation. |
| Dataset Splits | No | The paper is theoretical and does not perform experiments on specific datasets. Therefore, there is no mention of dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is purely theoretical, focusing on mathematical proofs and derivations of concentration inequalities. It does not describe any experiments that would require specific hardware, so no hardware specifications are provided. |
| Software Dependencies | No | The paper is purely theoretical and presents mathematical results. It does not describe any computational experiments or implementations that would necessitate the mention of specific software dependencies or their version numbers. |
| Experiment Setup | No | The paper is theoretical, deriving concentration inequalities for time series. It does not include an experimental section, thus no details on hyperparameters, training configurations, or system-level settings for experiments are provided. |