Results 21 to 30 of about 178,240 (253)

Analyzing Coarsened and Missing Data by Imputation Methods. [PDF]

Stat Med
ABSTRACTIn 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, Böhringer S, Bartlett JW, Bosse T, Horeweg N, de Wreede LC, Putter H.   +6 more
europepmc   +4 more sources

Application of imputation methods to genomic selection in Chinese Holstein cattle

Journal of Animal Science and Biotechnology, 2012
Missing genotypes are a common feature of high density SNP datasets obtained using SNP chip technology and this is likely to decrease the accuracy of genomic selection.
Weng Ziqing, Zhang Zhe, Ding Xiangdong, Fu Weixuan, Ma Peipei, Wang Chonglong, Zhang Qin   +6 more
doaj   +1 more source

The impact of misclassifications and outliers on imputation methods. [PDF]

J Appl Stat
Many 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

Informer-WGAN: High Missing Rate Time Series Imputation Based on Adversarial Training and a Self-Attention Mechanism

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, Limei Tian, Baichen Zhai, Shufan Zhang, Rui Wu   +4 more
doaj   +1 more source

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, Tandi E Matsha, Rajiv T Erasmus, Andre P Kengne   +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., Zhao, Lue Ping, Molenberghs, Geert   +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

A new hybrid method for data analysis when a significant percentage of data is missing [PDF]

Journal of Hyperstructures
This 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

Assessment of the performance of hidden Markov models for imputation in animal breeding

Genetics Selection Evolution, 2018
Background In this paper, we review the performance of various hidden Markov model-based imputation methods in animal breeding populations. Traditionally, pedigree and heuristic-based imputation methods have been used for imputation in large animal ...
Andrew Whalen, Gregor Gorjanc, Roger Ros-Freixedes, John M. Hickey   +3 more
doaj   +1 more source

Efficient Difference and Ratio-Type Imputation Methods under Ranked Set Sampling

Axioms, 2023
It is well known that ranked set sampling (RSS) is more efficient than simple random sampling (SRS). Furthermore, the presence of missing data vitiates the conventional results.
Shashi Bhushan, Anoop Kumar, Tolga Zaman, Aned Al Mutairi   +3 more
doaj   +1 more source