Iterative stepwise regression imputation using standard and robust methods

Maximum Likelihood from Incomplete Data Via the <i>EM</i> Algorithm

Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444 · see citing works

Multiple Imputation for Nonresponse in Surveys

Rubin, D. B. (1987) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316696 · Cited by 20,701 · see citing works

Statistical Analysis With Missing Data

Lahiff, M., Little, R. J. A., & Rubin, D. B. (1989) · Journal of the American Statistical Association · doi:10.2307/2289883 · Cited by 17,384 · see citing works

Modern Applied Statistics with S

Venables, W., & Ripley, B. (2010) · Statisctics and computing/Statistics and computing · doi:10.1007/978-0-387-21706-2 · Cited by 16,188 · see citing works

Modern Applied Statistics With S

Gloeckner, C. (2003) · Technometrics · doi:10.1198/tech.2003.s33 · Cited by 6,721 · see citing works

Pattern Recognition and Neural Networks

Ripley, B. D. (1996) · Cambridge University Press eBooks · doi:10.1017/cbo9780511812651 · Cited by 6,446 · see citing works

Analysis of Incomplete Multivariate Data

Schafer, J. L. (1997) · doi:10.1201/9781439821862 · Cited by 6,012 · see citing works

Analysis of Incomplete Multivariate Data

Booth, D. E., & Schafer, J. L. (2000) · Technometrics · doi:10.2307/1271466 · Cited by 5,627 · see citing works

Maximum likelihood estimation from incomplete data via the EM algorithm

Dempster, A. P. (1977) · Journal of the Royal Statistical Society Series A (Statistics in Society) · Cited by 4,506 · see citing works

<b>Amelia</b> II: A Program for Missing Data

Honaker, J., King, G., & Blackwell, M. (2011) · Journal of Statistical Software · doi:10.18637/jss.v045.i07 · Cited by 2,552 · see citing works

Multiple Imputation for Nonresponse in Surveys.

Lynn, P. J., & Rubin, D. B. (1988) · Journal of the Royal Statistical Society Series D (The Statistician) · doi:10.2307/2348774 · Cited by 2,330 · see citing works

Graphical Methods for Data Analysis.

Healy, M. J. R., Chambers, J. M., Cleveland, W. S., Kleiner, B., & Tukey, P. A. (1984) · Journal of the Royal Statistical Society Series A (General) · doi:10.2307/2981587 · Cited by 2,315 · see citing works

Statistical Analysis with Missing Data.

Chatfield, C., Little, R. J. A., & Rubin, D. B. (1988) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.2307/2982783 · Cited by 2,203 · see citing works

Graphical Methods for Data Analysis.

Fisher, N. I., Chambers, J. M., Cleveland, W. S., Kleiner, B., & Tukey, P. A. (1984) · Biometrics · doi:10.2307/2531418 · Cited by 1,982 · see citing works

A multivariate technique for multiply imputing missing values using a sequence of regression models

Raghunathan, T. E., Lepkowski, J. M., Hoewyk, J. V., & Solenberger, P. W. (2001) · Survey methodology · Cited by 1,981 · see citing works

Robust Statistics: Theory and Methods

Maronna, R. A., Martin, R. D., & Yohai, V. J. (2006) · Cited by 1,722 · see citing works

High Breakdown-Point and High Efficiency Robust Estimates for Regression

Yohai, V. J. (1987) · The Annals of Statistics · doi:10.1214/aos/1176350366 · Cited by 1,676 · see citing works

Multiple Imputation for Multivariate Missing-Data Problems: A Data Analyst's Perspective

Schafer, J. L., & Olsen, M. K. (1998) · Multivariate Behavioral Research · doi:10.1207/s15327906mbr3304_5 · Cited by 1,518 · see citing works

Pattern Recognition and Neural Networks

Fulkerson, B., & Ripley, B. D. (1997) · Technometrics · doi:10.2307/1270922 · Cited by 830 · see citing works

Multiple Imputation with Diagnostics (mi) inR: Opening Windows into the Black Box

Su, Y., Gelman, A., Hill, J., & Yajima, M. (2011) · Journal of Statistical Software · doi:10.18637/jss.v045.i02 · Cited by 645 · see citing works

Computing LTS Regression for Large Data Sets

Rousseeuw, P. J., & Driessen, K. V. (2006) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-005-0024-4 · Cited by 611 · see citing works

Complex Surveys: A Guide to Analysis Using R

Lumley, T. (2010) · Cited by 571 · see citing works

Robust Inference for Generalized Linear Models

Cantoni, E., & Ronchetti, E. (2001) · Journal of the American Statistical Association · doi:10.1198/016214501753209004 · Cited by 404 · see citing works

Imputation of missing values for compositional data using classical and robust methods

Hron, K., Templ, M., & Filzmoser, P. (2009) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2009.11.023 · Cited by 301 · see citing works

Statistics: Theory and Methods

Harper, W. V., Berry, D. A., & Lindgren, B. W. (1991) · Technometrics · doi:10.1080/00401706.1991.10484855 · Cited by 269 · see citing works

Avoiding bias due to perfect prediction in multiple imputation of incomplete categorical variables

White, I., Daniel, R., & Royston, P. (2010) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2010.04.005 · Cited by 223 · see citing works

Flexible mutlivariate imputation by MICE

van Buuren, S., & Groothuis‐Oudshoorn, C. G. M. (1999) · Cited by 208 · see citing works

Principal component analysis for data containing outliers and missing elements

Serneels, S., & Verdonck, T. (2007) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2007.05.024 · Cited by 174 · see citing works

Alternative Paradigms for the Analysis of Imputed Survey Data

Fay, R. E. (1996) · Journal of the American Statistical Association · doi:10.1080/01621459.1996.10476909 · Cited by 149 · see citing works

Multiple Imputation with Diagnostics (mi) in R: Opening Windows into the Black Box

Su, Y., Gelman, A., Hill, J., & Yajima, M. (2011) · doi:10.7916/d8vq3cd3 · Cited by 115 · see citing works

Sequential imputation for missing values

Verboven, S., Branden, K. V., & Goos, P. (2007) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2007.07.001 · Cited by 62 · see citing works

VIM: Visualization and Imputation of Missing Values

Templ, M., Kowarik, A., Alfons, A., Gussenbauer, J., Niederhametner, N., Vattheuer, E., de Cillia, G., & Rannetbauer, W. (2012) · doi:10.32614/cran.package.vim · Cited by 61 · see citing works

Imputation Methods for Handling Item-Nonresponse in the Social Sciences: A Methodological ReviewPreprint

Durrant, G. B. (2009) · RePEc: Research Papers in Economics · Cited by 57 · see citing works

mitools: Tools for Multiple Imputation of Missing Data

Lumley, T. (2004) · doi:10.32614/cran.package.mitools · Cited by 36 · see citing works

Modeling and imputation of semicontinuous survey variables

Schafer, J. L., Olsen, M. K., & Department Of (2010) · Cited by 17 · see citing works

NCRM Methods Review Papers, NCRM/002.Imputation Methods for Handling Item - Nonresponse in the Social Sciences: A Methodological Review

Durrant, G. B. (2005) · Cited by 14 · see citing works

The Impact of multiple imputation for DACSEIS

Rässler, S. (2004) · Cited by 13 · see citing works

mix: Estimation/Multiple Imputation for Mixed Categorical and Continuous Data

Schafer, J. L., & Ripley, B. (2003) · doi:10.32614/cran.package.mix · Cited by 12 · see citing works

A computational and methodological framework for visualization and imputation of missing values: the R package VIM

Templ, M., Kowarik, A., Peter Filzmoser, A., & Alfons, A. (2011) · Cited by 7 · see citing works

Income imputation and the analysis of consumer expenditure data

Fisher, J. D. (2006) · Cited by 7 · see citing works

Graphical methods for data analysis

Bookstein, A. (1985) · Information Processing & Management · doi:10.1016/0306-4573(85)90092-5 · Cited by 4 · see citing works

Proper imputation of missing income data for the Tuscany Living Condition Survey

Ghellini, G., & Neri, L. (2004) · Use Siena air (University of Siena) · Cited by 2 · see citing works

Imputation with the R Package VIM

Kowarik, A., & Templ, M. (2016) · Journal of Statistical Software · doi:10.18637/jss.v074.i07 · Cited by 659 · see citing works

Gallium, germanium, indium, and other trace and minor elements in sphalerite as a function of deposit type — A meta-analysis

Frenzel, M., Hirsch, T., & Gutzmer, J. (2016) · Ore Geology Reviews · doi:10.1016/j.oregeorev.2015.12.017 · Cited by 525 · see citing works

Predictive mean matching imputation of semicontinuous variables

Vink, G., Frank, L. E., Pannekoek, J., & van Buuren, S. (2014) · Statistica Neerlandica · doi:10.1111/stan.12023 · Cited by 178 · see citing works

NAguideR: performing and prioritizing missing value imputations for consistent bottom-up proteomic analyses

Wang, S., Li, W., Hu, L., Cheng, J., Yang, H., & Liu, Y. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa498 · Cited by 163 · see citing works

Deep learning for missing value imputation of continuous data and the effect of data discretization

Lin, W., Tsai, C., & Zhong, J. R. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.108079 · Cited by 119 · see citing works

Exploring incomplete data using visualization techniques

Templ, M., Alfons, A., & Peter Filzmoser, A. (2011) · Advances in Data Analysis and Classification · doi:10.1007/s11634-011-0102-y · Cited by 113 · see citing works

Risk of methicillin-resistant Staphylococcus aureus surgical site infection in patients with nasal MRSA colonization

Kalra, L., CAMACHO, F. T., Whitener, C., Du, P., Miller, M., Zalonis, C., & Julian, K. G. (2013) · American Journal of Infection Control · doi:10.1016/j.ajic.2013.05.021 · Cited by 104 · see citing works

A review on missing values for main challenges and methods

Ren, L., Wang, T., Sekhari, A., Zhang, H., & Bouras, A. (2023) · Information Systems · doi:10.1016/j.is.2023.102268 · Cited by 93 · see citing works

Dealing with missing values in proteomics data

Kong, W., Hui, H. W. H., Peng, H., & Goh, W. W. B. (2022) · PROTEOMICS · doi:10.1002/pmic.202200092 · Cited by 92 · see citing works

Associations Between Nutrition, Gut Microbiome, and Health in A Novel Nonhuman Primate Model

Clayton, J. B., Al‐Ghalith, G. A., Long, H. T., Tuan, B. V., Cabana, F., Huang, H., Vangay, P., Ward, T., Minh, V. V., Tam, N. A., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-29277-x · Cited by 79 · see citing works

A survey on pre-processing techniques: Relevant issues in the context of environmental data mining

Gibert, K., Sànchez–Marrè, M., & Izquierdo, J. (2016) · AI Communications · doi:10.3233/aic-160710 · Cited by 77 · see citing works

Handling incomplete and missing data in water network database using imputation methods

Kabir, G., Tesfamariam, S., Hemsing, J., & Sadiq, R. (2019) · Sustainable and Resilient Infrastructure · doi:10.1080/23789689.2019.1600960 · Cited by 76 · see citing works

Estimation of Social Exclusion Indicators from Complex Surveys: The R Package Laeken

Alfons, A., & Templ, M. (2012) · Journal of Statistical Software · doi:10.18637/jss.v054.i15 · Cited by 73 · see citing works

Anxiety, not anger, induces inflammatory activity: An avoidance/approach model of immune system activation.

Moons, W. G., & Shields, G. S. (2015) · Emotion · doi:10.1037/emo0000055 · Cited by 64 · see citing works

Using machine learning to predict soil bulk density on the basis of visual parameters: Tools for in-field and post-field evaluation

Bondi, G., Creamer, R., Ferrari, A., Fenton, O. S., & Wall, D. P. (2018) · Geoderma · doi:10.1016/j.geoderma.2017.11.035 · Cited by 65 · see citing works

Imputation of missing sub-hourly precipitation data in a large sensor network: A machine learning approach

Chivers, B. D., Wallbank, J., Cole, S. J., Sebek, O., Stanley, S., Fry, M., & Leontidis, G. (2020) · Journal of Hydrology · doi:10.1016/j.jhydrol.2020.125126 · Cited by 58 · see citing works

Group metacognitive therapy for repetitive negative thinking in primary and non-primary generalized anxiety disorder: An effectiveness trial

McEvoy, P. M., Erceg-Hurn, D. M., Anderson, R. A., Campbell, B. N. C., Swan, A., Saulsman, L. M., Summers, M., & Nathan, P. R. (2015) · Journal of Affective Disorders · doi:10.1016/j.jad.2014.12.046 · Cited by 54 · see citing works

Missing Data Imputation Methods and Their Performance With Biodistance Analyses

Kenyhercz, M. W., & Passalacqua, N. V. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-801966-5.00009-3 · Cited by 54 · see citing works

Classifiers Accuracy Improvement Based on Missing Data Imputation

Jordanov, I., Petrov, N., & Petrozziello, A. (2017) · Journal of Artificial Intelligence and Soft Computing Research · doi:10.1515/jaiscr-2018-0002 · Cited by 53 · see citing works

A novel data condition and performance hybrid imputation method for energy efficient operations of marine systems

Cheliotis, M., Gkerekos, C., Lazakis, I., & Theotokatos, G. (2019) · Ocean Engineering · doi:10.1016/j.oceaneng.2019.106220 · Cited by 48 · see citing works

Recursive partitioning on incomplete data using surrogate decisions and multiple imputation

Hapfelmeier, A., Hothorn, T., & Ulm, K. (2011) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2011.09.024 · Cited by 50 · see citing works

Disease control programme support costs: an update of WHO-CHOICE methodology, price databases and quantity assumptions

Bertram, M., Stenberg, K., Brindley, C., Jina, L., Serje, J., Watts, R., & Edejer, T. T.-T. (2017) · Cost Effectiveness and Resource Allocation · doi:10.1186/s12962-017-0083-6 · Cited by 49 · see citing works

A Review of Imputation Strategies for Isobaric Labeling-Based Shotgun Proteomics

Bramer, L. M., Irvahn, J., Piehowski, P. D., Rodland, K. D., & Webb-Robertson, B.-J. M. (2021) · Journal of Proteome Research · doi:10.1021/acs.jproteome.0c00123 · Cited by 47 · see citing works

Robust Estimation of Multivariate Location and Scatter in the Presence of Missing Data

Danilov, M., Yohai, V. J., & Zamar, R. H. (2012) · Journal of the American Statistical Association · doi:10.1080/01621459.2012.699792 · Cited by 45 · see citing works

Imagery-enhanced cognitive behavioural group therapy for social anxiety disorder: A pilot study

McEvoy, P. M., & Saulsman, L. M. (2014) · Behaviour Research and Therapy · doi:10.1016/j.brat.2014.01.006 · Cited by 44 · see citing works

Mapping associations between polygenic risks for childhood neuropsychiatric disorders, symptoms of attention deficit hyperactivity disorder, cognition, and the brain

Sudre, G. P., Frederick, J., Sharp, W. S., Ishii‐Takahashi, A., Mangalmurti, A., Choudhury, S., & Shaw, P. (2019) · Molecular Psychiatry · doi:10.1038/s41380-019-0350-3 · Cited by 42 · see citing works

Iterative Robust Semi-Supervised Missing Data Imputation

Fazakis, N., Kostopoulos, G., Kotsiantis, S. B., & Mporas, I. (2020) · IEEE Access · doi:10.1109/access.2020.2994033 · Cited by 39 · see citing works

A novel approach for handling missing data to enhance network intrusion detection system

Tahir, M., Abdullah, A., Udzir, N. I., & Kasmiran, K. A. (2024) · Cyber Security and Applications · doi:10.1016/j.csa.2024.100063 · Cited by 36 · see citing works

Modeling Pipe Break Data Using Survival Analysis with Machine Learning Imputation Methods

Xu, H., & Sinha, S. K. (2021) · Journal of Performance of Constructed Facilities · doi:10.1061/(asce)cf.1943-5509.0001649 · Cited by 36 · see citing works

Supervised radar signal classification

Jordanov, I., Petrov, N., & Petrozziello, A. (2016) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2016.7727371 · Cited by 34 · see citing works

Global Ancestry and Cognitive Ability

Lasker, J., Pesta, B. J., Fuerst, J. G. R., & Kirkegaard, E. O. W. (2019) · Psych · doi:10.3390/psych1010034 · Cited by 32 · see citing works

LipidSig 2.0: integrating lipid characteristic insights into advanced lipidomics data analysis

Liu, C.-H., Shen, P.-C., Lin, W.-J., Liu, H.-C., Tsai, M.-H., Huang, T.-Y., Chen, I.-C., Lai, Y.-L., Wang, Y.-D., Hung, M., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae335 · Cited by 32 · see citing works

Increased Levels of Extracellular Microvesicle Markers and Decreased Levels of Endocytic/Exocytic Proteins in the Alzheimer’s Disease Brain

Musunuri, S., Khoonsari, P. E., Mikus, M. S., Wetterhall, M., Månberg, A., Lannfelt, L., Erlandsson, A., Bergquist, J., Ingelsson, M., Shevchenko, G., et al. (2016) · Journal of Alzheimer s Disease · doi:10.3233/jad-160271 · Cited by 32 · see citing works

Missing Data Imputation and Its Effect on the Accuracy of Classification

Hunt, L. (2017) · Studies in classification, data analysis, and knowledge organization · doi:10.1007/978-3-319-55723-6_1 · Cited by 31 · see citing works

Enhancing Precision in Large-Scale Data Analysis: An Innovative Robust Imputation Algorithm for Managing Outliers and Missing Values

Templ, M. (2023) · Mathematics · doi:10.3390/math11122729 · Cited by 29 · see citing works

Robust monitoring of industrial processes using process data with outliers and missing values

Luo, L., Bao, S., & Peng, X. (2019) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2019.103827 · Cited by 27 · see citing works

Discrimination of Vegetation Height Categories With Passive Satellite Sensor Imagery Using Texture Analysis

Petrou, Z. I., Manakos, I., Stathaki, T., Mücher, C. A., & Adamo, M. (2015) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2015.2409131 · Cited by 27 · see citing works

Nonlinear, Non-stationary and Seasonal Time Series Forecasting Using Different Methods Coupled with Data Preprocessing

Stepchenko, A., Chizhov, J., Aleksejeva, L., & Tolujew, J. (2017) · Procedia Computer Science · doi:10.1016/j.procs.2017.01.175 · Cited by 26 · see citing works

Biogeographic Ancestry, Cognitive Ability and Socioeconomic Outcomes

Kirkegaard, E., Woodley of Menie, M., Williams, R., Fuerst, J., & Meisenberg, G. (2019) · Psych · doi:10.3390/psych1010001 · Cited by 25 · see citing works

Application of the Single Imputation Method to Estimate Missing Wind Speed Data in Malaysia

Razali, A. M., Masseran, N., Razali, A. M., Ibrahim, K., Ibrahim, K., Zaharim, A., Zaharim, A., & Sopian, K. (2013) · Research Journal of Applied Sciences Engineering and Technology · doi:10.19026/rjaset.6.3903 · Cited by 25 · see citing works

Predicting postoperative gait in cerebral palsy

C., O. A. G., Vigneron, V., Dorizzi, B., Khouri, N., & Desailly, É. (2016) · Gait & Posture · doi:10.1016/j.gaitpost.2016.11.012 · Cited by 25 · see citing works

Geochemical discrimination of pyrite in diverse ore deposit types through statistical analysis and machine learning techniques

Li, H., Zhang, Z., Zhang, R., Xie, Q., Zhang, L., & Santosh, M. (2023) · American Mineralogist · doi:10.2138/am-2023-8976 · Cited by 23 · see citing works

Enhancing environmental data imputation: A physically-constrained machine learning framework

Pastorini, M., Rodríguez, R., Etcheverry, L., Castro, A., & Gorgoglione, A. (2024) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2024.171773 · Cited by 22 · see citing works

Estimating time-varying treatment effects in longitudinal studies.

Loh, W. W., & Ren, D. (2023) · Psychological Methods · doi:10.1037/met0000574 · Cited by 25 · see citing works

Prevalence of Diabetes and Associated Obesity in Pennsylvania Adults, 1995–2010

Garcia‐Dominic, O., Lengerich, E. J., CAMACHO, F. T., Porter, N. R., Wray, L. A., Ahern, F. M., Bogdan, G., Weinberg, G. B., & Ulbrecht, J. S. (2014) · Preventing Chronic Disease · doi:10.5888/pcd11.130330 · Cited by 21 · see citing works

Predictors of the use and approval of CAM: results from the German General Social Survey (ALLBUS)

Abheiden, H., Teut, M., & Berghöfer, A. (2020) · BMC Complementary Medicine and Therapies · doi:10.1186/s12906-020-02966-9 · Cited by 20 · see citing works

Longitudinal deep learning clustering of Type 2 Diabetes Mellitus trajectories using routinely collected health records

Manzini, E., Vlacho, B., Franch‐Nadal, J., Escudero, J., Génova, A., Reixach, E., Andrés, E., Pizarro, I., Portero, J. L., Mauricio, D., et al. (2022) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2022.104218 · Cited by 22 · see citing works

EPEM: Efficient Parameter Estimation for Multiple Class Monotone Missing Data

Nguyen, T., Nguyen, D. M. H., Nguyen, H. T., Nguyen, B. T., & Wade, B. A. (2021) · Information Sciences · doi:10.1016/j.ins.2021.02.077 · Cited by 18 · see citing works

Significance tests on the output power of a thermally driven rotary nanomotor

Yang, L., Cai, K., Shi, J., & Qin, Q. (2017) · Nanotechnology · doi:10.1088/1361-6528/aa6961 · Cited by 17 · see citing works

Return Migration in Later Life in Mainland China: Motivations, Patterns, and Influences

Liu, Y., Dou, X., & Perry, T. E. (2018) · Ageing International · doi:10.1007/s12126-018-9336-8 · Cited by 17 · see citing works

Bacterial community structure and function distinguish gut sites in captive red‐shanked doucs (Pygathrix nemaeus)

Clayton, J. B., Shields‐Cutler, R. R., Hoops, S., Al‐Ghalith, G. A., Sha, J. C. M., Johnson, T. J., & Knights, D. (2019) · American Journal of Primatology · doi:10.1002/ajp.22977 · Cited by 16 · see citing works

Binned Data Provide Better Imputation of Missing Time Series Data from Wearables

Chakrabarti, S., Biswas, N., Karnani, K., Padul, V. G., Jones, L., Kesari, S., & Ashili, S. (2023) · Sensors · doi:10.3390/s23031454 · Cited by 16 · see citing works

On the Issue of Incomplete and Missing Water-Quality Data in Mine Site Databases: Comparing Three Imputation Methods

Betrie, G., Sadiq, R., Tesfamariam, S., & Morin, K. A. (2014) · Mine Water and the Environment · doi:10.1007/s10230-014-0322-4 · Cited by 17 · see citing works

Uncertainty quantification and integration of machine learning techniques for predicting acid rock drainage chemistry: A probability bounds approach

Betrie, G., Sadiq, R., Morin, K. A., & Tesfamariam, S. (2014) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2014.04.125 · Cited by 16 · see citing works

Generalized Additive Model Multiple Imputation by Chained Equations With Package ImputeRobust

Salfrán, D., & Spieß, M. (2018) · The R Journal · doi:10.32614/rj-2018-014 · Cited by 15 · see citing works

A Transcriptomics-Based Machine Learning Model Discriminating Mild Cognitive Impairment and the Prediction of Conversion to Alzheimer’s Disease

Park, M., Ahn, J., Lim, J., Han, M., Lee, J., Lee, J.-C., Hwang, S., & Kim, K. (2024) · Cells · doi:10.3390/cells13221920 · Cited by 15 · see citing works

Double-cycle weighted imputation method for wastewater treatment process data with multiple missing patterns

Han, H., Sun, M., Wu, X., & Li, F. (2022) · Science China Technological Sciences · doi:10.1007/s11431-022-2163-1 · Cited by 14 · see citing works

Advanced machine learning for missing petrophysical property imputation applied to improve the characterization of carbonate reservoirs

Abdulkhaleq, H. B., Khalil, K. A., Al‐Mudhafar, W. J., & Wood, D. A. (2024) · Geoenergy Science and Engineering · doi:10.1016/j.geoen.2024.212900 · Cited by 13 · see citing works

Visual Assessment of Clustering Tendency for Incomplete Data

Park, L. A. F., Bezdek, J. C., Leckie, C. A., Ramamohanarao, K., Bailey, J., & Palaniswami, M. S. (2016) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2016.2608821 · Cited by 13 · see citing works

A macrophyte bioassessment approach linking taxon-specific tolerance and abundance in north temperate lakes

Mikulyuk, A., Barton, M., Hauxwell, J. A., Hein, C. L., Kujawa, E. R., Minahan, K. L., Nault, M. E., Oele, D. L., & Wagner, K. I. (2017) · Journal of Environmental Management · doi:10.1016/j.jenvman.2017.05.012 · Cited by 13 · see citing works

Fusing voice and query data for non-invasive detection of laryngeal disorders

Vaičiukynas, E., Verikas, A., Gelžinis, A., Bačauskienė, M., Minelga, J., Hållander, M., Padervinskis, E., & Uloza, V. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.07.001 · Cited by 12 · see citing works

Effective Machine Learning Techniques for Dealing with Poor Credit Data

Nkambule, D. S., Twala, B., & Pretorius, J. H. C. (2024) · Risks · doi:10.3390/risks12110172 · Cited by 11 · see citing works

A Bayesian approach to analysing cortico-cortical associative stimulation induced increases in the excitability of corticospinal projections in humans

Carson, R. G., Capozio, A., McNickle, E., & Sack, A. T. (2020) · Experimental Brain Research · doi:10.1007/s00221-020-05943-3 · Cited by 11 · see citing works

Bias Reduction through Analysis of Competing Events (BRACE) Correction to Address Cancer Treatment Selection Bias in Observational Data

Williamson, C. W., Nelson, T. J., Thompson, C. A., Vitzthum, L. K., Zakeri, K., Riviere, P. J., Bryant, A. K., Sharabi, A. B., Zou, J., & Mell, L. K. (2022) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-21-2468 · Cited by 11 · see citing works

A Diabetes Prediction System Based on Incomplete Fused Data Sources

Yuan, Z., Ding, H., Chao, G., Song, M., Wang, L., Ding, W., & Chu, D. (2023) · Machine Learning and Knowledge Extraction · doi:10.3390/make5020023 · Cited by 9 · see citing works

Outcomes from a 3-fraction high-dose-rate brachytherapy regimen for patients with cervical cancer

Williamson, C. W., Kotha, N. V., Zou, J., Brown, D., Scanderbeg, D. J., Rash, D., Einck, J. P., Yashar, C. M., Mell, L. K., & Mayadev, J. (2023) · Brachytherapy · doi:10.1016/j.brachy.2022.12.005 · Cited by 9 · see citing works

Handling Incomplete and Missing Data in Corrosion Pit Measurement Database Using Imputation Methods: Model Development Using Artificial Neural Network

Woldesellasse, H., & Tesfamariam, S. (2021) · Journal of Pipeline Systems Engineering and Practice · doi:10.1061/(asce)ps.1949-1204.0000572 · Cited by 8 · see citing works

Environmental Dynamism as a Moderator of Familiness and Performance in Mexican SMEs

Rodriguez-Aceves, L. A., Baños-Monroy, V. I., & Ramírez-Solís, E. R. (2018) · Latin American Business Review · doi:10.1080/10978526.2018.1534546 · Cited by 8 · see citing works

Two directional Laplacian pyramids with application to data imputation

Rabin, N., & Fishelov, D. (2019) · Advances in Computational Mathematics · doi:10.1007/s10444-019-09697-7 · Cited by 8 · see citing works

Multivariate Farm Debt Imputation in the Agricultural Resource Management Survey (ARMS)

Morehart, M., Milkove, D., & Xu, Y. (2014) · PubMed · doi:10.22004/ag.econ.169401 · Cited by 8 · see citing works

Comparison of alternative imputation methods for ordinal data

Cugnata, F., & Salini, S. (2014) · Communications in Statistics - Simulation and Computation · doi:10.1080/03610918.2014.963611 · Cited by 8 · see citing works

Building stock energy modeling: Feasibility study on selection of important input parameters using stepwise regression

Arababadi, R., Naganathan, H., Pour, M. S., Dadvar, A., Parrish, K., & Chong, O. (2020) · Energy Science & Engineering · doi:10.1002/ese3.847 · Cited by 8 · see citing works

Using in vivo intact structure for system-wide quantitative analysis of changes in proteins

Son, A., Kim, H., Diedrich, J. K., Bamberger, C., McClatchy, D. B., Lipton, S. A., & Yates, J. R. (2024) · Nature Communications · doi:10.1038/s41467-024-53582-x · Cited by 9 · see citing works

MCQR : Enhancing the Processing and Analysis of Quantitative Proteomics Data by Incorporating Chromatography and Mass Spectrometry Information

Balliau, T., Frambourg, A., Langella, O., Martin‐Magniette, M., Zivy, M., & Blein‐Nicolas, M. (2025) · Journal of Proteome Research · doi:10.1021/acs.jproteome.4c01119 · Cited by 7 · see citing works

Peatfr: An R package to forecast tropical peatland fire risk with stochastic, machine learning, and optimisation methods

Mahdiyasa, A. W., Melly, M., Pasaribu, U. S., Taufik, M., & Muljadi, B. P. (2025) · Ecological Informatics · doi:10.1016/j.ecoinf.2025.103532 · Cited by 7 · see citing works

Evaluation of missing data imputation methods for human osteometric measurements

Pang, J., & Liu, X. (2023) · American Journal of Biological Anthropology · doi:10.1002/ajpa.24787 · Cited by 7 · see citing works

Cardiac Autonomic Dysfunction and Incidence of de novo Atrial Fibrillation: Heart Rate Variability vs. Heart Rate Complexity

Wessel, N., Berg, K., Kraemer, J. F., Gapelyuk, A., Rietsch, K., Hauser, T., Kurths, J., Wenzel, D., Klein, N., Kolb, C., et al. (2020) · Frontiers in Physiology · doi:10.3389/fphys.2020.596844 · Cited by 7 · see citing works

Associations Between Nutrition, Gut Microbiome, and Health in A Novel Nonhuman Primate ModelPreprint

Clayton, J. B., Al‐Ghalith, G. A., Long, H. T., Tuan, B. V., Cabana, F., Huang, H., Vangay, P., Ward, T., Minh, V. V., Tam, N. A., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/177295 · Cited by 7 · see citing works

The R Package Ecosystem for Robust Statistics

Todorov, V. (2024) · Wiley Interdisciplinary Reviews Computational Statistics · doi:10.1002/wics.70007 · Cited by 6 · see citing works

Estimating curvilinear time-varying treatment effects: Combining g-estimation of structural nested mean models with time-varying effect models for longitudinal causal inference.

Loh, W. W. (2024) · Psychological Methods · doi:10.1037/met0000637 · Cited by 6 · see citing works

The Software Environment R for Official Statistics and Survey Methodology

Templ, M., & Todorov, V. (2016) · Austrian Journal of Statistics · doi:10.17713/ajs.v45i1.100 · Cited by 6 · see citing works

Data mining and machine learning approaches for prediction modelling of schistosomiasis disease vectors

Fusco, T., Bi, Y., Wang, H., & Browne, F. (2019) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-019-01029-x · Cited by 6 · see citing works

Robust regression with compositional covariates including cellwise outliers

Štefelová, N., Alfons, A., Palarea‐Albaladejo, J., Peter Filzmoser, A., & Hron, K. (2021) · Advances in Data Analysis and Classification · doi:10.1007/s11634-021-00436-9 · Cited by 6 · see citing works

Research on modulus temperature correction of asphalt layer based on deflection equivalence and measured temperature field

Yang, R., Sun, L., Hu, Y. H., Jin, T. G., & Liu, L. (2024) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2024.138020 · Cited by 5 · see citing works

Applied Machine Learning for the Imputation of Missing Core Petrophysical Property Data in Clastic and Carbonate Reservoirs

Abdulkhaleq, H. B., Al‐Mudhafar, W. J., Wood, D. A., & Sausan, S. (2024) · SPE Western Regional Meeting · doi:10.2118/218890-ms · Cited by 5 · see citing works

Robust multiple imputation with GAM

Templ, M. (2024) · Statistics and Computing · doi:10.1007/s11222-024-10429-1 · Cited by 6 · see citing works

Reconstruction of Continuous Brachial Arterial Pressure from Continuous Finger Arterial Pressure Using a Two-level Optimization Strategy

Zhang, P., Liu, C., Chen, H., & Liu, J. (2020) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2020.2979249 · Cited by 5 · see citing works

Impacts of Missing Data Imputation on Resilience Evaluation for Water Distribution System

Ghimire, A. B., Magar, B. A., Parajuli, U., & Shin, S. (2024) · Urban Science · doi:10.3390/urbansci8040177 · Cited by 5 · see citing works

Predicting postoperative knee flexion during gait of cerebral palsy children

C., O. A. G., Vigneron, V., Dorizzi, B., Khouri, N., & Desailly, É. (2015) · Computer Methods in Biomechanics & Biomedical Engineering · doi:10.1080/10255842.2015.1070583 · Cited by 5 · see citing works

Principled Data Preprocessing: Application to Biological Aquatic Indicators of Water Pollution

Balderas, E. C. S., Berti‐Équille, L., Armienta, M. A., & Grac, C. (2017) · International Workshop on Database and Expert Systems Applications (DEXA) · doi:10.1109/dexa.2017.27 · Cited by 5 · see citing works

Inequality across US counties: an S factor analysis

Kirkegaard, E. O. W. (2016) · Open Quantitative Sociology & Political Science · doi:10.26775/oqsps.2016.05.23 · Cited by 5 · see citing works

Optimized Linear Imputation

Resheff, Y. S., & Weinshall, D. (2017) · International Conference on Pattern Recognition Applications and Methods · doi:10.5220/0006092900170025 · Cited by 6 · see citing works

Analysis of missing data with random forests

Hapfelmeier, A. (2012) · doi:10.5282/edoc.15058 · Cited by 5 · see citing works

Efficient imputation of missing data using the information of local space defined by the geometric one-class classifier

Kim, D. G., & Choi, J. Y. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122775 · Cited by 4 · see citing works

Stature and the Neolithic transition– Skeletal evidence from southern Sweden

Tornberg, A. (2017) · Journal of Archaeological Science Reports · doi:10.1016/j.jasrep.2017.10.031 · Cited by 5 · see citing works

Using Statistical Techniques and Replication Samples for Missing Values Imputation with an Application on Metabolomics

Yazdani, A., & Yazdani, A. (2018) · Journal of Biometrics & Biostatistics · doi:10.4172/2155-6180.1000393 · Cited by 4 · see citing works

M-estimation with incomplete and dependent multivariate data

Frahm, G., Nordhausen, K., & Oja, H. (2019) · Journal of Multivariate Analysis · doi:10.1016/j.jmva.2019.104569 · Cited by 4 · see citing works

The Utility of Nonparametric Transformations for Imputation of Survey Data

Robbins, M. W. (2014) · Journal of Official Statistics · doi:10.2478/jos-2014-0043 · Cited by 4 · see citing works

Patterns of Care for Metastatic Colorectal Cancer in Appalachia, and the Clinical, Sociodemographic, and Service Provider Determinants

Fleming, S. T., Mackley, H. B., CAMACHO, F. T., Yao, N. A., Gusani, N. J., Seiber, E. E., Matthews, S. A., Yang, T., & Hwang, W. (2015) · The Journal of Rural Health · doi:10.1111/jrh.12132 · Cited by 4 · see citing works

The impact of misclassifications and outliers on imputation methods

Templ, M., & Ulmer, M. (2024) · Journal of Applied Statistics · doi:10.1080/02664763.2024.2325969 · Cited by 3 · see citing works

Robust regression imputation for analyzing missing data

Rana, S., John, A. H., & Midi, H. (2012) · International Conference on Statistics in Science, Business and Engineering · doi:10.1109/icssbe.2012.6396621 · Cited by 19 · see citing works

A hybrid method for missing value imputation

Karanikola, A., & Kotsiantis, S. B. (2019) · Panhellenic Conference on Informatics · doi:10.1145/3368640.3368653 · Cited by 2 · see citing works

Fast and Robust Missing Data Imputation

Anastasopoulos, L. J., Hunter, S. G., & Poole, K. T. (2020) · doi:10.13140/rg.2.2.28637.44002

Robust multiple imputation with GAM

Templ, M. (2024) · Statistics and Computing · doi:10.1007/s11222-024-10429-1 · Cited by 6 · see citing works

Multiple Imputation: An Iterative Regression Imputation

Traore, B., & Adeleke, A. (2020)