Results 31 to 40 of about 204,036 (213)

Multiple imputation of missing covariates with non-linear effects and interactions: an evaluation of statistical methods. [PDF]

, 2012
BACKGROUND: Multiple imputation is often used for missing data. When a model contains as covariates more than one function of a variable, it is not obvious how best to impute missing values in these covariates.
Bartlett Jonathan W, Bartlett, Jonathan W, White, Ian R, Seaman Shaun R, White Ian R, Ian R White, Seaman, Shaun R, Shaun R Seaman, Jonathan W Bartlett   +8 more
core   +1 more source

Context-Aware Data Imputation: Application of Domain-Agnostic Deep Imputation Network

IEEE Access
Data imputation (DI) is a crucial task to manage missing data across different domains, such as healthcare and finance. Traditional imputation methods often fail to account for contextual nuances within specific domains due to the heterogeneity of data ...
Mohammed Gh. Al Zamil, Omar Khalid Alomeir   +1 more
doaj   +1 more source

Performance of genotype imputation for rare variants identified in exons and flanking regions of genes. [PDF]

, 2011
Genotype imputation has the potential to assess human genetic variation at a lower cost than assaying the variants using laboratory techniques. The performance of imputation for rare variants has not been comprehensively studied.
Margaret Gelder Ehm, Sharon R. Browning, Slater, Andrew J., Browning, Brian L., Aponte, Jennifer L., Browning Sharon R., Ehm, Margaret Gelder, Stephanie L Chissoe, Whittaker John C., Mooser, Vincent E., Sharon R Browning, Li, Li, Vincent E. Mooser, Jennifer L. Aponte, Brian L Browning, Li Yun, Whittaker, John C., Jennifer L Aponte, Browning, Brian L, Ehm Margaret Gelder, Browning, Sharon R, Whittaker, John C, Nelson, Matthew R, Mooser Vincent E., Aponte, Jennifer L, John C. Whittaker, Matthew R Nelson, Andrew J Slater, Vincent E Mooser, Browning Brian L., Mooser, Vincent E, Nelson Matthew R., Nelson, Matthew R., Li, Yun, John C Whittaker, Yun Li, Chissoe Stephanie L., Browning, Sharon R., Kong Xiangyang, Slater, Andrew J, Chissoe, Stephanie L, Matthew R. Nelson, Chissoe, Stephanie L., Li Li, Andrew J. Slater, Stephanie L. Chissoe, Xiangyang Kong, Slater Andrew J., Kong, Xiangyang, Aponte Jennifer L., Brian L. Browning   +50 more
core   +1 more source

Private Data Imputation

CoRR
Data imputation is an important data preparation task where the data analyst replaces missing or erroneous values to increase the expected accuracy of downstream analyses. The accuracy improvement of data imputation extends to private data analyses across distributed databases.
Abdelkarim Kati, Florian Kerschbaum, Marina Blanton   +2 more
openaire   +2 more sources

Reuse of imputed data in microarray analysis increases imputation efficiency [PDF]

BMC Bioinformatics, 2004
Abstract Background The imputation of missing values is necessary for the efficient use of DNA microarray data, because many clustering algorithms and some statistical analysis require a complete data set.
Ki-Yeol Kim, Byoung-Jin Kim, Gwan-Su Yi
openaire   +4 more sources

Missing Categorical Data in Sociological Surveys: An Experimental Evaluation of Imputation Techniques

Соціологічні студії
Missing categorical data presents a persistent challenge to data quality in quantitative sociological research, where simpler approaches can lead to biased estimates and incorrect conclusions.
Yaroslav Kostenko, Andrii Gorbachyk
doaj   +1 more source

Multiple imputation: dealing with missing data [PDF]

Nephrology Dialysis Transplantation, 2013
In many fields, including the field of nephrology, missing data are unfortunately an unavoidable problem in clinical/epidemiological research. The most common methods for dealing with missing data are complete case analysis-excluding patients with missing data--mean substitution--replacing missing values of a variable with the average of known values ...
Goeij, M.C.M. de, Diepen, M. van, Jager, K.J., Tripepi, G., Zoccali, C., Dekker, F.W.   +5 more
openaire   +7 more sources

Imputation of quantitative genetic interactions in epistatic MAPs by interaction propagation matrix completion [PDF]

, 2014
A popular large-scale gene interaction discovery platform is the Epistatic Miniarray Profile (E-MAP). E-MAPs benefit from quantitative output, which makes it possible to detect subtle interactions.
Marinka Žitnik, Zupan, Blaz, Blaž Zupan, Zitnik, Marinka   +3 more
core   +1 more source

A novel scaling methodology to reduce the biases associated with missing data from commercial activity monitors.

PLoS ONE, 2020
BackgroundCommercial physical activity monitors have wide utility in the assessment of physical activity in research and clinical settings, however, the removal of devices results in missing data and has the potential to bias study conclusions.
R O'Driscoll, J Turicchi, C Duarte, J Michalowska, S C Larsen, A L Palmeira, B L Heitmann, G W Horgan, R J Stubbs   +8 more
doaj   +1 more source

Comparison of Genotype Imputation for SNP Array and Low-Coverage Whole-Genome Sequencing Data

Frontiers in Genetics, 2022
Genotype imputation is the term used to describe the process of inferring unobserved genotypes in a sample of individuals. It is a key step prior to a genome-wide association study (GWAS) or genomic prediction.
Tianyu Deng, Pengfei Zhang, Dorian Garrick, Huijiang Gao, Lixian Wang, Fuping Zhao   +5 more
doaj   +1 more source