Results 41 to 50 of about 318,466 (281)
Sensors, 2020 Building operation data are important for monitoring, analysis, modeling, and control of building energy systems. However, missing data is one of the major data quality issues, making data imputation techniques become increasingly important.
Liang Zhang
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MissForest - nonparametric missing value imputation for mixed-type data
, 2011 Modern data acquisition based on high-throughput technology is often facing the problem of missing data. Algorithms commonly used in the analysis of such large-scale data often depend on a complete set.
D. J. Stekhoven +11 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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Imputation methods for filling missing data in urban air pollution data for Malaysia [PDF]
Urbanism. Arhitectura. Constructii, 2018 The air quality measurement data obtained from the continuous ambient air quality monitoring (CAAQM) station usually contained missing data. The missing observations of the data usually occurred due to machine failure, routine maintenance and human error.
Nur Afiqah Zakaria +1 more
doaj
Comparison of Performance of Data Imputation Methods for Numeric Dataset
Applied Artificial Intelligence, 2019 Missing data is common problem faced by researchers and data scientists. Therefore, it is required to handle them appropriately in order to get better and accurate results of data analysis.
Anil Jadhav +2 more
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Multiple Imputation: Theory and Method
International Statistical Review, 2003 SummaryIn this review paper, we discuss the theoretical background of multiple imputation, describe how to build an imputation model and how to create proper imputations. We also present the rules for making repeated imputation inferences. Three widely used multiple imputation methods, the propensity score method, the predictive model method and the ...
openaire +3 more sources
GSimp: A Gibbs sampler based left-censored missing value imputation approach for metabolomics studies. [PDF]
PLoS Computational Biology, 2018 Left-censored missing values commonly exist in targeted metabolomics datasets and can be considered as missing not at random (MNAR). Improper data processing procedures for missing values will cause adverse impacts on subsequent statistical analyses ...
Runmin Wei +5 more
doaj +1 more source
Imputation of truncated p-values for meta-analysis methods and its genomic application
, 2014 Microarray analysis to monitor expression activities in thousands of genes simultaneously has become routine in biomedical research during the past decade.
Ding, Ying +5 more
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Data Imputation Methods and Technologies [PDF]
International Journal of Trend in Scientific Research and Development, 2018 We introduce a class of linear quantile regression estimators for panel data. Our framework contains dynamic autoregressive models, models with general predetermined regressors, and models with multiple individual e ects as special cases. We follow a correlated random e ects approach, and rely on additional layers of quantile regressions as a flexible ...
Ritesh Kumar Pandey, Dr Asha Ambhaikar
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Molecular Oncology, EarlyView.This work identified serum proteins associated with pancreatic epithelial neoplasms (PanINs) and early‐stage PDAC. Proteomics screens assessed genetically engineered mice with abundant PanINs, KPC mice (Lox‐STOP‐Lox‐KrasG12D/+ Lox‐STOP‐Lox‐Trp53R172H/+ Pdx1‐Cre) before PDAC development and also early‐stage PDAC patients (n = 31), compared to benign ...
Hannah Mearns +10 more
wiley +1 more source