Results 11 to 20 of about 6,412,013 (303)
Capturing missing tuples and missing values [PDF]
Proceedings of the twenty-ninth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, 2010 Databases in real life are often neither entirely closed-world nor entirely open-world. Indeed, databases in an enterprise are typically partially closed, in which a part of the data is constrained by master data that contains complete information about the enterprise in certain aspects [21].
Wenfei Fan, Floris Geerts
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Capturing Missing Tuples and Missing Values [PDF]
ACM Transactions on Database Systems, 2016 Databases in real life are often neither entirely closed-world nor entirely open-world. Databases in an enterprise are typically partially closed , in which a part of the data is constrained by master data that contains complete information about the enterprise in certain aspects. It has been
Ting Deng, Wenfei Fan, Floris Geerts
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Random Forest variable importance with missing data [PDF]
, 2012 Random Forests are commonly applied for data prediction and interpretation. The latter purpose is supported by variable importance measures that rate the relevance of predictors. Yet existing measures can not be computed when data contains missing values.
Hapfelmeier, Alexander +2 more
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CoRR, 2019 Preprint submitted to Decision Support Systems ...
Fernando Martínez-Plumed +3 more
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Missing-Values Adjustment for Mixed-Type Data
Journal of Probability and Statistics, 2011 We propose a new method of single imputation, reconstruction, and estimation of nonreported, incorrect, implausible, or excluded values in more than one field of the record.
Agostino Tarsitano, Marianna Falcone
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missMDA: A Package for Handling Missing Values in Multivariate Data Analysis
Journal of Statistical Software, 2016 We present the R package missMDA which performs principal component methods on incomplete data sets, aiming to obtain scores, loadings and graphical representations despite missing values.
Julie Josse, François Husson
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Computing missing values in time series [PDF]
, 1993 This work presents two algorithms to estimate missing values in time series. The first is the Kalman Filter, as developed by Kohn and Ansley (1986) and others. The second is the additive outlier approach, developed by Pefia, Ljung and Maravall.
Maravall, Agustín +2 more
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Sensitivity Analysis of Longitudinal Data with Intermittent Missing Values: Application in a Clinical Trial [PDF]
The Egyptian Statistical Journal, 2017 We conduct two types of sensitivity analyses of study conclusion , in intermittent setting : the first is fitting different to the response variable. The marginal distribution of the response is assumed to be an extreme valuea logistic distribution, the ...
Abeer S. Ahmed
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Optimal Recovery of Missing Values for Non-Negative Matrix Factorization
IEEE Open Journal of Signal Processing, 2021 Missing values imputation is often evaluated on some similarity measure between actual and imputed data. However, it may be more meaningful to evaluate downstream algorithm performance after imputation than the imputation itself.
Rebecca Chen Dean, Lav R. Varshney
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Variable selection with Random Forests for missing data [PDF]
, 2013 Variable selection has been suggested for Random Forests to improve their efficiency of data prediction and interpretation. However, its basic element, i.e.
Hapfelmeier, Alexander, Ulm, Kurt
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