141 related articles for article (PubMed ID: 37219777)
41. Imputation of data values that are less than a detection limit.
Succop PA; Clark S; Chen M; Galke W
J Occup Environ Hyg; 2004 Jul; 1(7):436-41. PubMed ID: 15238313
[TBL] [Abstract][Full Text] [Related]
42. Imputation of missing values for cochlear implant candidate audiometric data and potential applications.
Pavelchek C; Michelson AP; Walia A; Ortmann A; Herzog J; Buchman CA; Shew MA
PLoS One; 2023; 18(2):e0281337. PubMed ID: 36745652
[TBL] [Abstract][Full Text] [Related]
43. Multiple imputation for assessment of exposures to drinking water contaminants: evaluation with the Atrazine Monitoring Program.
Jones RM; Stayner LT; Demirtas H
Environ Res; 2014 Oct; 134():466-73. PubMed ID: 25461881
[TBL] [Abstract][Full Text] [Related]
44. Impact of imputation methods on the amount of genetic variation captured by a single-nucleotide polymorphism panel in soybeans.
Xavier A; Muir WM; Rainey KM
BMC Bioinformatics; 2016 Feb; 17():55. PubMed ID: 26830693
[TBL] [Abstract][Full Text] [Related]
45. Optimization methods for the imputation of missing values in Educational Institutions Data.
Aureli D; Bruni R; Daraio C
MethodsX; 2021; 8():101208. PubMed ID: 34434731
[TBL] [Abstract][Full Text] [Related]
46. Part 2. Development of Enhanced Statistical Methods for Assessing Health Effects Associated with an Unknown Number of Major Sources of Multiple Air Pollutants.
Park ES; Symanski E; Han D; Spiegelman C
Res Rep Health Eff Inst; 2015 Jun; (183 Pt 1-2):51-113. PubMed ID: 26333239
[TBL] [Abstract][Full Text] [Related]
47. Random Forest Missing Data Algorithms.
Tang F; Ishwaran H
Stat Anal Data Min; 2017 Dec; 10(6):363-377. PubMed ID: 29403567
[TBL] [Abstract][Full Text] [Related]
48. Multinomial Logistic Factor Regression for Multi-source Functional Block-wise Missing Data.
Du X; Jiang X; Lin J;
Psychometrika; 2023 Sep; 88(3):975-1001. PubMed ID: 37268759
[TBL] [Abstract][Full Text] [Related]
49. Collateral missing value imputation: a new robust missing value estimation algorithm for microarray data.
Sehgal MS; Gondal I; Dooley LS
Bioinformatics; 2005 May; 21(10):2417-23. PubMed ID: 15731210
[TBL] [Abstract][Full Text] [Related]
50. Evaluating Methods for Imputing Missing Data from Longitudinal Monitoring of Athlete Workload.
Benson LC; Stilling C; Owoeye OBA; Emery CA
J Sports Sci Med; 2021 Jun; 20(2):188-196. PubMed ID: 33948096
[TBL] [Abstract][Full Text] [Related]
51. Evaluating the impact of multivariate imputation by MICE in feature selection.
Mera-Gaona M; Neumann U; Vargas-Canas R; López DM
PLoS One; 2021; 16(7):e0254720. PubMed ID: 34320016
[TBL] [Abstract][Full Text] [Related]
52. Characterizing and Managing Missing Structured Data in Electronic Health Records: Data Analysis.
Beaulieu-Jones BK; Lavage DR; Snyder JW; Moore JH; Pendergrass SA; Bauer CR
JMIR Med Inform; 2018 Feb; 6(1):e11. PubMed ID: 29475824
[TBL] [Abstract][Full Text] [Related]
53. Predictors of clinical outcome in pediatric oligodendroglioma: meta-analysis of individual patient data and multiple imputation.
Wang KY; Vankov ER; Lin DDM
J Neurosurg Pediatr; 2018 Feb; 21(2):153-163. PubMed ID: 29192869
[TBL] [Abstract][Full Text] [Related]
54. A real data-driven simulation strategy to select an imputation method for mixed-type trait data.
May JA; Feng Z; Adamowicz SJ
PLoS Comput Biol; 2023 Mar; 19(3):e1010154. PubMed ID: 36947561
[TBL] [Abstract][Full Text] [Related]
55. Application of a multi-stage neural network approach for time-series landfill gas modeling with missing data imputation.
Fallah B; Ng KTW; Vu HL; Torabi F
Waste Manag; 2020 Oct; 116():66-78. PubMed ID: 32784123
[TBL] [Abstract][Full Text] [Related]
56. Multiple imputation methods for handling missing values in a longitudinal categorical variable with restrictions on transitions over time: a simulation study.
De Silva AP; Moreno-Betancur M; De Livera AM; Lee KJ; Simpson JA
BMC Med Res Methodol; 2019 Jan; 19(1):14. PubMed ID: 30630434
[TBL] [Abstract][Full Text] [Related]
57. Comparison of imputation methods for missing laboratory data in medicine.
Waljee AK; Mukherjee A; Singal AG; Zhang Y; Warren J; Balis U; Marrero J; Zhu J; Higgins PD
BMJ Open; 2013 Aug; 3(8):. PubMed ID: 23906948
[TBL] [Abstract][Full Text] [Related]
58. Application of machine learning missing data imputation techniques in clinical decision making: taking the discharge assessment of patients with spontaneous supratentorial intracerebral hemorrhage as an example.
Wang H; Tang J; Wu M; Wang X; Zhang T
BMC Med Inform Decis Mak; 2022 Jan; 22(1):13. PubMed ID: 35027065
[TBL] [Abstract][Full Text] [Related]
59. Multiple imputation for handling missing outcome data when estimating the relative risk.
Sullivan TR; Lee KJ; Ryan P; Salter AB
BMC Med Res Methodol; 2017 Sep; 17(1):134. PubMed ID: 28877666
[TBL] [Abstract][Full Text] [Related]
60. An artificial neural network ensemble approach to generate air pollution maps.
Van Roode S; Ruiz-Aguilar JJ; González-Enrique J; Turias IJ
Environ Monit Assess; 2019 Nov; 191(12):727. PubMed ID: 31701254
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]