Results 11 to 20 of about 1,743,603 (263)
Patterns, 2022 Our “digified” lives have provided researchers with an unprecedented opportunity to study society at a much higher frequency and granularity. Such data can have a large sample size but can be sparse, biased, and exclusively contributed by the users of the technologies.
Basiri, Anahid, Brunsdon, Chris
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Missing data: the impact of what is not there [PDF]
European Journal of Endocrinology, 2020 The validity of clinical research is potentially threatened by missing data. Any variable measured in a study can have missing values, including the exposure, the outcome, and confounders. When missing values are ignored in the analysis, only those subjects with complete records will be included in the analysis. This may lead to biased results and loss
Groenwold, R.H.H., Dekkers, O.M.
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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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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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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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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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Diseases of the Colon & Rectum, 2015 restriction
Jeonghyun, Kang, Kang Young, Lee
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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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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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INFERENCE AND MISSING DATA [PDF]
ETS Research Bulletin Series, 1975 ABSTRACTTwo results are presented concerning inference when data may be missing. First, ignoring the process that causes missing data when making sampling distribution inferences about the parameter of the data, θ, is generally appropriate if and only if the missing data are “missing at random” and the observed data are “observed at random,” and then ...
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