Frontiers

Handling missing data of using the XGBoost-based multiple imputation by chained equations regression method

1 Shangwan Coal Mine, Ejin Horo Banner, Ordos, China 2 CCTEG Xi’an Research Institute Co. Ltd., Xi’an, China This study introduces an XGBoost-MICE (Multiple Imputation by Chained Equations) method for ...
IEEE

MTSVAE: A Traffic Data Imputation Model Considering Different Periodic Temporal and Global Spatial Features

Abstract: Traffic data is widely applied in ITS and is required to be complete. Due to detector failures or signal interference, traffic data missing is inevitable. Therefore, it's necessary to deal ...
GitHub

chlea-schiff/SAITS-fork

[Updates in Jun 2024] 😎 The 1st comprehensive time-seres imputation benchmark paper TSI-Bench: Benchmarking Time Series Imputation now is public available. The code is open source in the repo Awesome ...
GitHub

A deep learning model for Short Tandem Repeat (STR) imputation.

This repository contains the development of SIM, a novel deep learning model designed for the imputation of Short Tandem Repeat (STR) genotypes. The primary goal of this project is to accurately ...
JSTOR Daily

Assessing Model Sensitivity of the Imputation Methods Used in the National Assessment of Educational Progress

The National Assessment of Educational Progress (NAEP) uses latent trait item response models to summarize performance of students on assessments of educational proficiency in different subject areas ...
rmdopen.bmj

Challenge of missing data in observational studies: investigating cross-sectional imputation methods for assessing disease activity in axial spondyloarthritis

Objectives We aimed to compare various methods for imputing disease activity in longitudinally collected observational data of patients with axial spondyloarthritis (axSpA). Methods We conducted a ...