Complete Case and Imputation-Based Procedures
The accuracy of estimation procedures based on the imputation of plausible values
... 5.1 Conclusion The first part of this research consisted of a literature study, of which the focus was on several often used estimation procedures. Marginal Maximum Likelihood (MML) and Markov Chain Monte Carlo ...
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Illuminate the unknown: Evaluation of imputation procedures based on the SAVE Survey
... multivariate procedures are used to analyze certain effects, all the variables of each unit (household or individual) must be ...estimations based on observed cases might lead to biased ...
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Imputation of missing network data: Some simple procedures
... Abstract Analysis of social network data is often hampered by non-response and missing data. Recent studies show the negative effects of missing actors and ties on the structural properties of social networks. This means ...
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Efficient Pedigree-Based Imputation
... 4 Imputation 4.1 Population-Based Imputation Population-based imputation is based almost entirely on comparing phased haplo- ...pedigree-based imputation methods ...
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Taking "Don't Knows" as Valid Responses: A Complete Random Imputation of Missing Data
... The method avoids problems related to data missing at random (MAR) when incomplete data results from inapplicable survey questions. By including all cases in the analysis, the estimation of statistical models becomes ...
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A comparison of imputation procedures and statistical tests for the analysis of two-dimensional electrophoresis data
... Besides the obvious loss of information due to missing values, data analysis is also hampered by missing values. Clustering techniques (e.g., k-means, hierarchical) and various statistical approaches (such as principal ...
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Missing data imputation based on probabilistic data
... resulting complete dataset Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or ...
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Missing Values Imputation Based on Iterative Learning
... values imputation methods as well as many learning ...values imputation problem as a special case of learning ...values imputation and learning ...values imputation, which presents a ...
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Genotype imputation based on discriminant and cluster analysis
... of complete data analysis, applying Imputation in row data also has the important advantage of allowing the use of row information available from data collector but not available to an external data ...
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Model-Based Imputation for Multilevel Interaction Effects
... the complete data (represented by a circle) and one imputed data set (represented by a plus), with regression lines for each within cluster regression (solid for complete, dashed for ...an imputation ...
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Dealing with indeterminate outcomes in antimalarial drug efficacy trials: a comparison between complete case analysis, multiple imputation and inverse probability weighting
... a complete case (CC) analysis ...single imputation approaches consider the imputed datum as the ‘ known observed ’ value and uncertainty regarding not knowing the reason for parasite recurrence isn ’ ...
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Dealing with indeterminate outcomes in antimalarial drug efficacy trials: a comparison between complete case analysis, multiple imputation and inverse probability weighting.
... all imputation models are likely to be mis-specified to some ...correct imputation model [ 9 , 40 ], thus making the IPW approach a feasible alternative for handling indetermin- ate outcomes in estimation ...
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Classifiers accuracy improvement based on missing data imputation
... the complete subset with X c , for each sample con- taining a missing point, the procedure will search the K most similar records in the X c dataset, accord- ing to the adopted distance measure, before imput- ing ...
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MIAEC: Missing data imputation based on the evidence chain
... is based on distance data classification algorithm, which searches the entire dataset to find the k data tuples closest to a given data ...the complete data tuple in the dataset is used as the training ...
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Missing data imputation: focusing on single imputation
... Abstract: Complete case analysis is widely used for handling missing data, and it is the default method in many statistical ...many imputation methods are developed to make gap ...like ...
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Multiple imputation in veterinary epidemiological studies: a case study and simulation
... a case study based on research into dairy producers' attitudes toward mastitis control procedures, combined with two simulation studies to evaluate the use of MI and compare results with a CC ...
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Missing Data Imputation with OLS-based Autoencoder for Intelligent Manufacturing
... 2) Evaluation criteria In the experiment, the datasets D 2 , D 5 , D 10 , D 15 ,D 20 and D 25 with different percentages of missing data are used. Taking D 2 for instance, the data set is divided into two data sets in ...
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Imputation method based on recurrent neural networks for the internet of things
... value imputation in the context of the ...top-2 imputation in 83% of the experiments presented in those ...training procedures I defined in chapters 3 and ...as imputation method obtained for ...
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Probability based Missing Value Imputation Method and its Analysis
... Another imputation method is missing value imputation algorithm based on evidence chain ...a case has missing data for any of the variables, then simply delete that case from the ...
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Correlation Analysis for Tensor-based Traffic Data Imputation Method
... Various imputation methods have been studied for addressing missing data ...of imputation methods based on the form of traffic data model: vector based method, matrix based method and ...
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