Results 11 to 20 of about 1,660,493 (261)
A Comparison of Different Methods for Rainfall Imputation: A Galician Case Study
Applied Sciences, 2023 With the ultimate goal of developing models that involve the use of environmental variables, a GIS-based application is being developed that is circumscribed to the region of Galicia (Spain). Ten-minute data of six meteorological variables were collected
José Vidal-Paz +2 more
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Learning with Missing Data [PDF]
2020 IEEE International Conference on Big Data (Big Data), 2020 Many real-world data sets contain missing values, therefore, learning with incomplete data sets is a common challenge faced by data scientists. Handling them in an intelligent way is important to develop robust data models, since there is no perfect approach to compensate for the missing values.
Carlos A. Escobar +3 more
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Improving accuracy of missing data imputation in data mining
Kurdistan Journal of Applied Research, 2017 In fact, raw data in the real world is dirty. Each large data repository contains various types of anomalous values that influence the result of the analysis, since in data mining, good models usually need good data, databases in the world are not always
Nzar A. Ali, Zhyan M. Omer
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Joint Models for Incomplete Longitudinal Data and Time-to-Event Data
Mathematics, 2022 Clinical studies often collect longitudinal and time-to-event data for each subject. Joint modeling is a powerful methodology for evaluating the association between these data.
Yuriko Takeda +2 more
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Diseases of the Colon & Rectum, 2015 restriction
Jeonghyun, Kang, Kang Young, Lee
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Electronic Physician, 2017 Background: Missing values in data are found in a large number of studies in the field of medical sciences, especially longitudinal ones, in which repeated measurements are taken from each person during the study.
Amin Golabpour +4 more
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Traffic Flow Prediction With Missing Data Imputed by Tensor Completion Methods
IEEE Access, 2020 Missing data is inevitable and ubiquitous in intelligent transportation systems (ITSs). A handful of completion methods have been proposed, among which the tensor-based models have been shown to be the most advantageous for missing traffic data ...
Qin Li +4 more
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Traffic Missing Data Imputation: A Selective Overview of Temporal Theories and Algorithms
Mathematics, 2022 A great challenge for intelligent transportation systems (ITS) is missing traffic data. Traffic data are input from various transportation applications. In the past few decades, several methods for traffic temporal data imputation have been proposed.
Tuo Sun +4 more
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Using paradata for imputation of missing values in sociological survey data: results of statistical modeling (case of Croatia and Slovakia) [PDF]
СоціологіяMissing values are a common issue in quantitative social researches. One of the ways to handle missing data is by data imputation. This article outlines the challenges of traditional data imputation methods, which often introduce biases, and presents an ...
Andrii Gorbachyk, Yaroslav Kostenko
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On the Joys of Missing Data [PDF]
Journal of Pediatric Psychology, 2013 We provide conceptual introductions to missingness mechanisms--missing completely at random, missing at random, and missing not at random--and state-of-the-art methods of handling missing data--full-information maximum likelihood and multiple imputation--followed by a discussion of planned missing designs: Multiform questionnaire protocols, 2-method ...
Little, Todd D. +3 more
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