Missing data imputation of high-resolution temporal climate time series data
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This article is published in Meteorological Applications.The article was published on 2020-01-01 and is currently open access. It has received 47 citations till now. The article focuses on the topics: Imputation (statistics).read more
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Forecasting, Structural Time Series Models and the Kalman Filter
TL;DR: In this paper, the authors provide a unified and comprehensive theory of structural time series models, including a detailed treatment of the Kalman filter for modeling economic and social time series, and address the special problems which the treatment of such series poses.
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Machine learning-based imputation soft computing approach for large missing scale and non-reference data imputation
A.H. Alamoodi,B. B. Zaidan,B. B. Zaidan,A. A. Zaidan,Osamah Shihab Albahri,Juliana Chen,Juliana Chen,M. A. Chyad,Salem Garfan,A. M. Aleesa +9 more
TL;DR: This study conducted two experiments using BMI datasets with more than 80% of missing values, derived from the National Child Development Centre at Sultan Idris Education University (UPSI), Malaysia, and shows that the proposed imputation approach has capability in reconstructing datasets with huge missing values.
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Assessing the Impact of Land Use and Climate Change on Surface Runoff Response Using Gridded Observations and SWAT
TL;DR: In this article, the authors used the new generation of soil and water assessment tool (SWAT) dubbed SWAT+ to assess the viability of using high resolution gridded data as an alternative to station observations to investigate surface runoff response to continuous land use change and future climate change.
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Towards missing electric power data imputation for energy management systems
TL;DR: The experimental results, based on electric power data for a two-year period in Taiwan, show that the machine learning methods generally perform better than the statistical ones, with K-NN and SVR performing the best.
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Trade-off assessments between reading cost and accuracy measures for digital camera monitoring of recreational boating effort
TL;DR: In an a posteriori analysis, trade-offs between the reading cost and accuracy measures of estimates of boat retrievals obtained at various sampling proportions for low, moderate and high traffic boat ramps in Western Australia are investigated.
References
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Maximum likelihood from incomplete data via the EM algorithm
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A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix
Whitney K. Newey,Kenneth D. West +1 more
TL;DR: In this article, a simple method of calculating a heteroskedasticity and autocorrelation consistent covariance matrix that is positive semi-definite by construction is described.
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mice: Multivariate Imputation by Chained Equations in R
TL;DR: Mice adds new functionality for imputing multilevel data, automatic predictor selection, data handling, post-processing imputed values, specialized pooling routines, model selection tools, and diagnostic graphs.
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Inference and missing data
TL;DR: In this article, it was shown that 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 such inferences are generally conditional on the observed pattern of missing data.