The performance of multiple imputations for different number of imputations

QR Code

The performance of multiple imputations for different number of imputations

Multiple imputation method is a widely used method in missing data analysis. The method consists of a three-stage process including imputation, analyzing and pooling. The number of imputations to be selected in the imputation step in the first stage is important. Hence, this study aimed to examine t...

Full description

Bibliographic Details
Main Authors:	Ser, Gazel, Keskin, Siddik, Yilmaz, M. Can
Format:	Article
Language:	English
Published:	Penerbit Universiti Kebangsaan Malaysia 2016
Online Access:	http://journalarticle.ukm.my/10397/ http://journalarticle.ukm.my/10397/1/23%20Gazel%20Ser.pdf

Similar Items

ExtraImpute: a novel machine learning method for missing data imputation
by: Alabadla, Mustafa, et al.
Published: (2022)

Model-building with multiply imputed data
by: Pillay, Khuneswari Gopal, et al.
Published: (2018)

Robust regression imputation for analyzing missing data
by: Rana, Md. Sohel, et al.
Published: (2012)

Performance analysis of machine learning algorithms for missing value imputation
by: Zainal Abidin, Nadzurah, et al.
Published: (2018)

Performance of selected imputation techniques for missing variances in meta-analysis
by: Nik Idris, Nik Ruzni, et al.
Published: (2012)

Performance of selected imputation techniques for missing variances in meta-analysis
by: Nik Idris, Nik Ruzni, et al.
Published: (2013)

Artificial neural network forecasting performance with missing value imputations
by: Abd Rahman, Nur Haizum, et al.
Published: (2020)

Imputing missing values in modelling the PM10 concentrations
by: Nuradhiathy Abd Razak,, et al.
Published: (2014)

Missing variability in meta-analysis : is imputing always good?
by: Nik Idris, Nik Ruzni, et al.
Published: (2006)

An Evaluation of Machine Learning Algorithms for Missing Values Imputation
by: Kohbalan, Moorthy, et al.
Published: (2019)

Missing values imputation tool using imputex algorithm
by: Sidi, Fatimah, et al.
Published: (2024)

Robust data assimilation in river flow and stage estimation based on multiple imputation particle filter
by: Ismail, Zool Hilmi, et al.
Published: (2019)

Imputation methods on daily PM10 data (2010-15)
by: Abd Rani, Nurul Latiffah, et al.
Published: (2019)

Systematic review of using machine learning in imputing missing values
by: Alabadla, Mustafa, et al.
Published: (2022)

Missing-values imputation algorithms for microarray gene expression data
by: Moorthy, Kohbalan, et al.
Published: (2019)

Systematic review of using machine learning in imputing missing values
by: Alabadla, Mustafa, et al.
Published: (2022)

Generalized log-rank test for partly interval-censored failure time data via multiple imputation.
by: Ibrahim, Noor Akma, et al.

Cold deck missing value imputation with a trust-based selection method of multiple web donors
by: Mohd Jaya, Mohd Izham
Published: (2018)

Parametric model based on imputations techniques for partly interval censored data
by: Zyoud, Abdallah, et al.
Published: (2017)

Determination of the best single imputation algorithm for missing rainfall data treatment
by: Gamil Abdulraqeb Abdullah Saeed,, et al.
Published: (2016)

Determination of the Best Single Imputation Algorithm for Missing Rainfall Data Treatment
by: Saeed, Gamil Abdulraqeb Abdullah, et al.
Published: (2016)

Estimation of Missing Rainfall Data Using Spatial Interpolation and Imputation Methods
by: Roslinazairimah, Zakaria, et al.
Published: (2015)

Comparative analysis of imputation methods for enhancing predictive accuracy in data models
by: Nurul Aqilah, Zamri, et al.
Published: (2024)

Robust Random Regression Imputation method for missing data in the presence of outliers
by: John, Ahamefule Happy
Published: (2013)

Development of an imputation technique - INI for software metric database with incomplete data
by: Wasito, Ito, et al.
Published: (2007)

Evaluation of knowledge encoded in deep neural network in the imputation of gene expression
by: Chow, Jenn Pang
Published: (2018)

Effects of architecture on imputation of gene expression using deep neural networks
by: Lai, Wing Khang
Published: (2018)

Parametric tests for partly interval-censored failure time data under Weibull distribution via multiple imputation
by: Mohammad Alharpy, Azzah, et al.
Published: (2013)

Trust score measurement method for web donor selection and imputation of missing values
by: Jaya, M. Izham, et al.
Published: (2021)

A comparative study of different imputation methods for daily rainfall data in east-coast Peninsular Malaysia
by: Che Mat Nor, Siti Mariana, et al.
Published: (2020)

Nonparametric estimate based on imputations techniques for interval and partly interval censored data
by: Zyoud, Abdallah, et al.
Published: (2016)

The effects of imputing the missing standard deviations on the standard error of meta analysis estimates
by: Nik Idris, Nik Ruzni, et al.
Published: (2009)

A case study on the effect of imputing the missing variability measures in meta analysis
by: Nik Idris, Nik Ruzni
Published: (2007)

Imputation techniques for incomplete load data based on seasonality and orientation of the missing values
by: Nur Arina Bazilah Kamisan,, et al.
Published: (2020)

A comparison of various imputation methods for missing values in air quality data
by: Nuryazmin Ahmat Zainuri,, et al.
Published: (2015)

A systematic review of recurrent neural network adoption in missing data imputation
by: Nur Aqilah, Fadzil Akbar, et al.
Published: (2025)

Imputation Analysis of Time-Series Data Using a Random Forest Algorithm
by: Nur Najmiyah, Jaafar, et al.
Published: (2024)

Filling the gaps: Imputation of missing metrics’ values in a software quality model
by: Kupinski, S., et al.
Published: (2017)

Comparison of several imputation techniques for log logistic model with covariate and interval censored data
by: Teea, Yuan Xin, et al.
Published: (2024)

Evaluation of missing values imputation methods towards the effectiveness of asset valuation prediction model
by: Mohd Jaya, Mohd Izham, et al.
Published: (2019)