Imputing missing values in unevenly spaced clinical time series data to build an effective temporal classification framework
type: Item
instance of: scholarly article
title: Imputing missing values in unevenly spaced clinical time series data to build an effective temporal classification framework
zbMATH Open document ID: 1464.62136
author: Jane Y. Nancy
author: Nehemiah H. Khanna
author: Kannan Arputharaj
published in: Computational Statistics and Data Analysis
publication date: 2018-08-07T00:00:00Z
Mathematics Subject Classification ID: 62-08
Mathematics Subject Classification ID: 62P10
zbMATH DE Number: 6914549
zbMATH Keywords: time series
zbMATH Keywords: missing value
zbMATH Keywords: tolerance rough set
zbMATH Keywords: particle swarm optimization
zbMATH Keywords: inverse distance weight
describes a project that uses: R
describes a project that uses: OpenMx
describes a project that uses: Matlab
MaRDI profile type: Publication
full work available at URL: j.csda.2017.02.012
OpenAlex ID: W2591512686
cites work: OpenMx: an open source extended structural equation modeling framework
cites work: Q4139463
cites work: A comparison of imputation methods for handling missing scores in biometric fusion
cites work: The comparative efficacy of imputation methods for missing data in structural equation model\-ing.
cites work: Rough sets
cites work: Q4246485
cites work: A hot deck imputation procedure for multiply imputing nonignorable missing data: the proxy pattern-mixture hot deck
DOI: 10.1016/J.CSDA.2017.02.012
description: scientific article; zbMATH DE number 6914549