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1. Full Text Recent Developments in Dealing with Item Non‐response in Surveys: A Critical Review
by Chen, Sixia and Haziza, David
International Statistical Review, ISSN 0306-7734, 05/2019, Volume 87, Issue S1, pp. S192 - S218
Summary The most common way for treating item non‐response in surveys is to construct one or more replacement values to fill in for a missing value. This... 
non‐parametric imputation | semi‐parametric imputation | Estimating equations | multiply robust inference | variance estimation | multiple imputation | single imputation | semi-parametric imputation | non-parametric imputation | JACKKNIFE VARIANCE-ESTIMATION | EFFICIENT RANDOM IMPUTATION | FRACTIONAL IMPUTATION | STATISTICS & PROBABILITY | ROBUST IMPUTATION PROCEDURES | COMPOSITE IMPUTATION | RANDOM FOREST | MISSING-DATA | HOT-DECK IMPUTATION | MULTIPLE-IMPUTATION | EMPIRICAL LIKELIHOOD
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2. Full Text Advanced Statistics: Missing Data in Clinical Research-Part 1: An Introduction and Conceptual Framework
by Haukoos, Jason S and Newgard, Craig D
Academic Emergency Medicine, ISSN 1069-6563, 07/2007, Volume 14, Issue 7, pp. 662 - 668
Missing data are commonly encountered in clinical research. Unfortunately, they are often neglected or not properly handled during analytic procedures, and... 
last observation carried forward | regression imputation | worst case analysis | imputation | clinical research | bias | complete-case analysis | mean imputation | single imputation | statistical analysis | hot deck imputation | missing data | REGRESSION | TRIALS | WORST | EMERGENCY MEDICINE | VALUES | MULTIPLE IMPUTATION
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3. Full Text An embedded imputation method via Attribute-based Decision Graphs
by Bertini Junior, João Roberto and Nicoletti, Maria do Carmo and Zhao, Liang
Expert Systems With Applications, ISSN 0957-4174, 09/2016, Volume 57, pp. 159 - 177
The performance of classification algorithms is highly dependent on the quality of training data. Missing attribute values are quite common in many real world... 
Attribute-based Decision Graphs | Missing attribute value | Data imputation | Single imputation | Machine learning based imputation Methods | REGRESSION | ALGORITHM | Machine learning based imputation | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | PREDICTION | ENGINEERING, ELECTRICAL & ELECTRONIC | FORESTS | MISSING VALUE IMPUTATION | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | TREES | DISCRETE | VALUES | ERROR | MULTIPLE IMPUTATION | Methods | Computer science | Analysis | Machine learning | Training | Algorithms | Expenses | Data sets | Classification | Graphs | Computational efficiency | Proposals
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4. Full Text Improving the Quality of Web-based Data Imputation with Crowd Intervention
by Gu, Binbin and Li, Zhixu and Liu, An and Xu, Jiajie and Zhao, Lei and Zhou, Xiaofang
IEEE Transactions on Knowledge and Data Engineering, ISSN 1041-4347, 11/2019, pp. 1 - 1
Data incompleteness is a common data quality problem in databases. Recent work proposes to retrieve missing string values from the World Wide Web for higher... 
Crowd | Web | Data Imputation
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5. Full Text Imputation with the R package VIM
by Kowarik, Alexander and Templ, Matthias
Journal of Statistical Software, ISSN 1548-7660, 2016, Volume 74, Issue 7, pp. 1 - 16
The package VIM (Templ, Alfons, Kowarik, and Prantner 2016) is developed to explore and analyze the structure of missing values in data using visualization... 
Imputation methods | Missing values | imputation methods | MISSING DATA | STATISTICS & PROBABILITY | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | MULTIPLE IMPUTATION | missing values | R
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6. Multiple imputation for nonresponse in surveys
by Rubin, Donald B
1987, Wiley series in probability and mathematical statistics. Applied probability and statistics, ISBN 9780471087052, xxix, 258 p. --
Multiple imputation (Statistics)
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7. Full Text Preserving the distribution function in surveys in case of imputation for zero inflated data
by Gelein, Brigitte and Chauvet, Guillaume
Journal of Statistical Planning and Inference, ISSN 0378-3758, 05/2020, Volume 206, pp. 84 - 99
Item non-response in surveys is usually handled by single imputation, whose main objective is to reduce the non-response bias. Imputation methods need to be... 
Methodology | Statistics
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8. Full Text Dealing with missing outcome data in randomized trials and observational studies
by Groenwold, R.H and Donders, A.R.T and Roes, C.B and Harrell Jr, F.E and Moons, K.G
American Journal of Epidemiology, ISSN 0002-9262, 2012, Volume 175, Issue 3, pp. 210 - 217
Although missing outcome data are an important problem in randomized trials and observational studies, methods to address this issue can be difficult to apply.... 
randomized trials | multiple imputation | confounding | missing data | loss to follow-up | REGRESSION | IMPUTATION | PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH | CLINICAL-TRIALS | COVARIATE ADJUSTMENT | Data Interpretation, Statistical | Models, Statistical | Outcome Assessment (Health Care) | Computer Simulation | Humans | Randomized Controlled Trials as Topic | Missing observations (Statistics) | Multiple imputation (Statistics) | Management | Analysis
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9. Full Text Tuning multiple imputation by predictive mean matching and local residual draws
by Morris, Tim P and White, Ian R and Royston, Patrick
BMC Medical Research Methodology, ISSN 1471-2288, 06/2014, Volume 14, Issue 1, pp. 75 - 75
Background: Multiple imputation is a commonly used method for handling incomplete covariates as it can provide valid inference when data are missing at random.... 
Missing data | Predictive mean matching | Local residual draws | Multiple imputation | Imputation model | REGRESSION-MODELS | COVARIATE | SOFTWARE | MISSING-DATA | HEALTH CARE SCIENCES & SERVICES | VARIABLES | VALUES | SELECTION | Neoplasms, Glandular and Epithelial - blood | Data Interpretation, Statistical | Neoplasms, Glandular and Epithelial - mortality | Computer Simulation | Humans | Carcinoma, Ovarian Epithelial | Ovarian Neoplasms - blood | Biomedical Research - methods | Albumins - analysis | Models, Statistical | Ovarian Neoplasms - mortality | Serum Albumin - analysis | Studies | Confidence intervals | Variables | Parameter estimation | Sparsity | Bias | Clinical trials | Models | Methods
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10. Full Text 3D-MICE: Integration of cross-sectional and longitudinal imputation for multi-analyte longitudinal clinical data
by Luo, Yuan and Szolovits, Peter and Dighe, Anand S and Baron, Jason M
Journal of the American Medical Informatics Association, ISSN 1067-5027, 06/2018, Volume 25, Issue 6, pp. 645 - 653
Objective: A key challenge in clinical data mining is that most clinical datasets contain missing data. Since many commonly used machine learning algorithms... 
Missing data | Gaussian process | Machine learning | Imputation | Electronic health record | Multiple imputation with chained equations | Data mining | Computational pathology | EHR | computational pathology | MEDICAL INFORMATICS | data mining | SURVIVAL ANALYSIS | COMPUTER SCIENCE, INFORMATION SYSTEMS | machine learning | missing data | MISSING VALUE IMPUTATION | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | imputation | HEALTH CARE SCIENCES & SERVICES | INFORMATION SCIENCE & LIBRARY SCIENCE | VALUES | MULTIPLE IMPUTATION | multiple imputation with chained equations | electronic health record
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11. Full Text Fractional imputation in survey sampling: A comparative review
by Yang, Shu and Kim, Jae Kwang
Statistical Science, ISSN 0883-4237, 2016, Volume 31, Issue 3, pp. 415 - 432
Fractional imputation (FI) is a relatively new method of imputation for handling item nonresponse in survey sampling. In FI, several imputed values with their... 
Multiple imputation | Missing at random | Synthetic imputation | Item nonresponse | Monte Carlo EM | INCOMPLETE DATA | HOT DECK IMPUTATION | DATA AUGMENTATION | REPLICATION VARIANCE-ESTIMATION | multiple imputation | NEAREST-NEIGHBOR IMPUTATION | STATISTICS & PROBABILITY | NONIGNORABLE MISSING DATA | ROBUST INFERENCE | missing at random | MULTIPLE-IMPUTATION | EMPIRICAL LIKELIHOOD | synthetic imputation | LIKELIHOOD-BASED INFERENCE | Statistics - Methodology
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12. Full Text Strategies for imputation to whole genome sequence using a single or multi-breed reference population in cattle
by Brøndum, Rasmus F and Guldbrandtsen, Bernt and Sahana, Goutam and Lund, Mogens S and Su, Guosheng
BMC Genomics, ISSN 1471-2164, 08/2014, Volume 15, Issue 1, pp. 728 - 728
Background: The advent of low cost next generation sequencing has made it possible to sequence a large number of dairy and beef bulls which can be used as a... 
Pre-phasing | Imputation | Allele frequency | Next generation sequencing | Cross-validation | FORMAT | MARKER IMPUTATION | BULLS | NORDIC HOLSTEIN | ACCURACY | PHASE | PANELS | GENOTYPE IMPUTATION | BIOTECHNOLOGY & APPLIED MICROBIOLOGY | GENETICS & HEREDITY | Breeding | Gene Frequency | Genotyping Techniques - standards | Male | Sequence Analysis, DNA | Cattle - genetics | Reference Standards | Animals | High-Throughput Nucleotide Sequencing | Polymorphism, Single Nucleotide | Software | Genome | Genotyping Techniques - methods | Animal genetics | Usage | Genetic research | Multiple imputation (Statistics) | Nucleotide sequencing | Research | Methods | DNA sequencing | Studies | Haplotypes | Accuracy | Cattle | Genomics | Population | Genomes | Deoxyribonucleic acid--DNA
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13. Full Text A comparison of inclusive and restrictive strategies in modern missing data procedures
by Collins, LM and Schafer, JL and Kam, CM
PSYCHOLOGICAL METHODS, ISSN 1082-989X, 12/2001, Volume 6, Issue 4, pp. 330 - 351
Two classes of modern missing data procedures, maximum likelihood (ML) and multiple imputation (MI), tend to yield similar results when implemented in... 
PSYCHOLOGY, MULTIDISCIPLINARY | MULTIPLE-IMPUTATION
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14. Full Text Real‐World Evidence: What It Is and What It Can Tell Us According to the International Society for Pharmacoepidemiology (ISPE) Comparative Effectiveness Research (CER) Special Interest Group (SIG)
by Yuan, Hongbo and Ali, M. Sanni and Brouwer, Emily S and Girman, Cynthia J and Guo, Jeff J and Lund, Jennifer L and Patorno, Elisabetta and Slaughter, Jonathan L and Wen, Xuerong and Bennett, Dimitri and ISPE Comparative Effectiveness Res and ISPE Comparative Effectiveness Research Special Interest Group and the ISPE Comparative Effectiveness Research Special Interest Group
Clinical Pharmacology & Therapeutics, ISSN 0009-9236, 08/2018, Volume 104, Issue 2, pp. 239 - 241
On December 8, 2016, the New England Journal of Medicine published a sounding board on Real World Evidence (RWE)1 by the US Food and Drug Administration (FDA)... 
PHARMACOLOGY & PHARMACY | MULTIPLE IMPUTATION
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15. Matched sampling for causal effects
by Rubin, Donald B
2006, ISBN 9780521674362, ix, 489
Matched sampling is often used to help assess the causal effect of some exposure or intervention, typically when randomized experiments are not available or... 
Sampling (Statistics) | Statistical matching | Mathematics
Book
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16. Full Text The rise of multiple imputation: A review of the reporting and implementation of the method in medical research Data collection, quality, and reporting
by Hayati Rezvan, Panteha and Lee, Katherine J and Simpson, Julie A
BMC Medical Research Methodology, 04/2015, Volume 15, Issue 1
Missing data | Sensitivity analysis | Multiple imputation | Reporting
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17. Full Text Impact of imputation of missing values on classification error for discrete data
by Farhangfar, Alireza and Kurgan, Lukasz and Dy, Jennifer
Pattern Recognition, ISSN 0031-3203, 2008, Volume 41, Issue 12, pp. 3692 - 3705
Numerous industrial and research databases include missing values. It is not uncommon to encounter databases that have up to a half of the entries missing,... 
Multiple imputations | Missing values | Single imputation | Classification | Imputation of missing values | multiple imputations | DATABASES | single imputation | classification | imputation of missing values | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | missing values | ENGINEERING, ELECTRICAL & ELECTRONIC
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18. Full Text A unified approach to linearization variance estimation from survey data after imputation for item nonresponse
by JAE KWANG KIM and J. N. K. RAO
Biometrika, ISSN 0006-3444, 12/2009, Volume 96, Issue 4, pp. 917 - 932
Variance estimation after imputation is an important practical problem in survey sampling. When deterministic imputation or stochastic imputation is used, we... 
Statistical variance | Estimation bias | Determinism | Data imputation | Linear regression | Population estimates | Unbiased estimators | Estimators | Linearization | Estimation methods | Regression imputation | Composite imputation | Fractional imputation | Multiple imputation | Imputed estimator | HOT DECK IMPUTATION | BIOLOGY | MATHEMATICAL & COMPUTATIONAL BIOLOGY | STATISTICS & PROBABILITY | Sampling | Data processing | Stochasticity
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19. Capture-recapture methods for the social and medical sciences
by Heijden, P. G. M. van der editor. (Peter G. M.) and Bunge, John, 1957- editor and Böhning, Dankmar editor
2018, 1, Chapman & Hall/CRC Interdisciplinary Statistics Series, ISBN 9781315151939, Volume 1, xxxii, 429 pages
Capture-recapture methods have been used in biology and ecology for more than 100 years. However, it is only recently that these methods have become popular in... 
Missing observations (Statistics) | Population forecasting | Social sciences | Sampling (Statistics) | Multiple imputation (Statistics) | Statistical methods | Medical statistics | Statistical Theory & Methods | Psychiatry | Psychological Methods & Statistics
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