Results 31 to 40 of about 287,343 (334)
The Impact of Missing Data and Imputation Methods on the Analysis of 24-Hour Activity Patterns
Clocks & Sleep, 2022 The purpose of this study is to characterize the impact of the timing and duration of missing actigraphy data on interdaily stability (IS) and intradaily variability (IV) calculation.
Lara Weed +3 more
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Nearest neighbours in least-squares data imputation algorithms with different missing patterns [PDF]
, 2006 Methods for imputation of missing data in the so-called least-squares approximation approach, a non-parametric computationally efficient multidimensional technique, are experimentally compared.
Atkeson +30 more
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Missing Data Imputation for Categorical Variables [PDF]
Statistika: Statistics and Economy Journal, 2022 Dealing with missing data is a crucial part of everyday data analysis. The IMIC algorithm is a missing data imputation method that can handle mixed numerical and categorical datasets. However, the categorical data are crucial for this work.
Jaroslav Horníček, Hana Řezanková
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Dealing with Missing Responses in Cognitive Diagnostic Modeling
Psych, 2022 Missing data are a common problem in educational assessment settings. In the implementation of cognitive diagnostic models (CDMs), the presence and/or inappropriate treatment of missingness may yield biased parameter estimates and diagnostic information.
Shenghai Dai, Dubravka Svetina Valdivia
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A Noise-Aware Multiple Imputation Algorithm for Missing Data
Mathematics, 2022 Missing data is a common and inevitable phenomenon. In practical applications, the datasets usually contain noises for various reasons. Most of the existing missing data imputing algorithms are affected by noises which reduce the accuracy of the ...
Fangfang Li, Hui Sun, Yu Gu, Ge Yu
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Methods to Handle Incomplete Data
MAMC Journal of Medical Sciences, 2020 Context: The major question for data analysis is determining the appropriate analytic approach in the presence of incomplete observations. The most common solution to handle missing data in a data set is imputation, where missing values are estimated and
Vinny Johny +2 more
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Algorithms, 2023 Ground meteorological observation data (GMOD) are the core of research on earth-related disciplines and an important reference for societal production and life.
Cong Li, Xupeng Ren, Guohui Zhao
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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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Missing Categorical Data Imputation and Individual Observation Level Imputation
Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 2014 Traditional missing data techniques of imputation schemes focus on prediction of the missing value based on other observed values. In the case of continuous missing data the imputation of missing values often focuses on regression models.
Pavel Zimmermann +2 more
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Impact of Missing Data on Data Quality in Social Research
Соціологічні студіїMissing data is a common issue in quantitative social research that negatively affects the data quality. This article explores the consequences of missing data, outlining the potential issues it may pose and emphasizing the importance of properly ...
Yaroslav Kostenko
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