Results 21 to 30 of about 193,873 (254)
Algorithms, 2022 Missing observations in time series will distort the data characteristics, change the dataset expectations, high-order distances, and other statistics, and increase the difficulty of data analysis.
Yufan Qian +4 more
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Analyzing Coarsened and Missing Data by Imputation Methods. [PDF]
Stat MedABSTRACTIn various missing data problems, values are not entirely missing, but are coarsened. For coarsened observations, instead of observing the true value, a subset of values ‐ strictly smaller than the full sample space of the variable ‐ is observed to which the true value belongs.
van der Burg LLJ +6 more
europepmc +4 more sources
The impact of misclassifications and outliers on imputation methods. [PDF]
J Appl StatMany imputation methods have been developed over the years and tested mostly under ideal settings. Surprisingly, there is no detailed research on how imputation methods perform when the idealized assumptions about the distribution of data and/or model assumptions are partly not fulfilled.
Templ M, Ulmer M.
europepmc +3 more sources
Effects of Different Missing Data Imputation Techniques on the Performance of Undiagnosed Diabetes Risk Prediction Models in a Mixed-Ancestry Population of South Africa. [PDF]
PLoS ONE, 2015 Imputation techniques used to handle missing data are based on the principle of replacement. It is widely advocated that multiple imputation is superior to other imputation methods, however studies have suggested that simple methods for filling missing ...
Katya L Masconi +3 more
doaj +1 more source
A Semiparametric Method of Multiple Imputation
Journal of the Royal Statistical Society Series B: Statistical Methodology, 1998 Summary In this paper, we describe how to use multiple imputation semiparametrically to obtain estimates of parameters and their standard errors when some individuals have missing data. The methods given require the investigator to know or be able to estimate the process generating the missing data but requires no full distributional ...
Lipsitz, Stuart R. +2 more
openaire +2 more sources
Imputation with the R Package VIM
Journal of Statistical Software, 2016 The package VIM (Templ, Alfons, Kowarik, and Prantner 2016) is developed to explore and analyze the structure of missing values in data using visualization methods, to impute these missing values with the built-in imputation methods and to verify the ...
Alexander Kowarik, Matthias Templ
doaj +1 more source
Imputation methods for filling missing data in urban air pollution data for Malaysia [PDF]
Urbanism. Arhitectura. Constructii, 2018 The air quality measurement data obtained from the continuous ambient air quality monitoring (CAAQM) station usually contained missing data. The missing observations of the data usually occurred due to machine failure, routine maintenance and human error.
Nur Afiqah Zakaria +1 more
doaj
A new hybrid method for data analysis when a significant percentage of data is missing [PDF]
Journal of HyperstructuresThis article aims to compare the efficiency of different imputation methods with missing data. In this way we use mean, median, Expected-Maximization (EM), regression imputation(RI) and multiple imputations (MI) to replace missing data.In fact, we employ
Behrouz Fathi-Vajargah, Ahmad Nouraldin
doaj +1 more source
SnapFISH-IMPUTE: an imputation method for multiplexed DNA FISH data
Communications BiologyABSTRACT Chromatin spatial organization plays a crucial role in gene regulation. Recently developed and prospering multiplexed DNA FISH technologies enable direct visualization of chromatin conformation in nucleus. However, incomplete data caused by limited detection efficiency can substantially complicate and impair ...
Hongyu Yu +4 more
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A method for increasing the robustness of multiple imputation [PDF]
Computational Statistics & Data Analysis, 2012 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Rhian M. Daniel, Michael G. Kenward
openaire +3 more sources