Change-point Detection for Sparse and Dense Functional Data in General Dimensions
Authors: Carlos Misael Madrid Padilla, Daren Wang, Zifeng Zhao, Yi Yu
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive numerical experiments illustrate the effectiveness of FSBS and its advantage over existing methods in the literature under various settings. A real data application is further conducted, where FSBS localises change-points of sea surface temperature patterns in the south Pacific attributed to El Niño. |
| Researcher Affiliation | Academia | Carlos Misael Madrid Padilla Department of Mathematics University of Notre Dame cmadridp@nd.edu Daren Wang Department of Statistics University of Notre Dame dwang24@nd.edu Zifeng Zhao Mendoza College of Business University of Notre Dame zzhao2@nd.edu Yi Yu Department of Statistics University of Warwick yi.yu.2@warwick.ac.uk |
| Pseudocode | Yes | Algorithm 1 Functional Seeded Binary Segmentation. FSBS ((s, e], h, h, τ) |
| Open Source Code | Yes | The code to replicate all of our experiments can be found at https://github.com/cmadridp/FSBS. |
| Open Datasets | Yes | We consider the COBE-SSTE dataset [24], which consists of monthly average sea surface temperature (SST) from 1940 to 2019, on a 1 degree latitude by 1 degree longitude grid (48 30) covering Australia. ... [24] Physical Sciences Laboratory [2020], COBE SST2 and Sea-Ice , https://psl.noaa.gov/ data/gridded/data.cobe2.html. |
| Dataset Splits | Yes | The tuning parameter τ and the bandwidth h are chosen by cross-validation, with evenly-indexed data being the training set and oddly-indexed data being the validation set. |
| Hardware Specification | No | The paper explicitly states: "Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [No] The computation is sufficiently fast and computational cost is not a concern." |
| Software Dependencies | No | For the implementation of FSBS, we adopt the Gaussian kernel. Following the standard practice in kernel density estimation, the bandwidth h is selected by the function Hpi in the R package ks ([9]). The paper mentions the R package 'ks' but does not specify its version number. |
| Experiment Setup | Yes | The tuning parameter τ and the bandwidth h are chosen by cross-validation, with evenly-indexed data being the training set and oddly-indexed data being the validation set. For each pair of candidate (h, τ), we obtain change-point estimators {bηk} b K k=1 on the training set and compute the validation loss P b K k=1 P t [bηk,bηk+1) Pn i=1{(bηk+1 bηk) 1 Pbηk+1 t=bηk+1 Ft,h(xt,i) yt,i}2. The pair (h, τ) is then chosen to be the one corresponding to the lowest validation loss. |