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Missing Data
A general framework for describing and handling missing data is presented. Methodology is categorized according to its validity under various...
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Relationship between reasons for intermittent missing patient-reported outcomes data and missing data mechanisms
PurposeNon-response (NR) to patient-reported outcome (PRO) questionnaires may cause bias if not handled appropriately. Collecting reasons for NR is...
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Missing data imputation and classification of small sample missing time series data based on gradient penalized adversarial multi-task learning
In practice, time series data obtained is usually small and missing, which poses a great challenge to data analysis in different domains, such as...
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M-Mix: Patternwise Missing Mix for filling the missing values in traffic flow data
Real-world traffic flow data often contain missing values, which can limit its usability. Although existing deep learning-based imputation methods...
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Imputation Methods for Missing Hydrometeorological Data Estimation
Missing data is a ubiquitous problem that plagues many hydrometeorological datasets. Objective and robust spatial and temporal imputation methods are...
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Generative Models for Missing Data
Missing data poses an ubiquitous challenge across a wide range of applications, stemming from a multitude of causes that are both diverse and...
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Data Mining of Missing Persons Data
This paper presents the results of analysis to evaluate the effectiveness of data mining techniques to predict the outcome for missing persons cases....
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Missing Data Imputation: A Practical Guide
This chapter demonstrates handling missing values in data analysis aimed at practitioners who seek a hands-on approach. The methods are presented...
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Random Subspace Sampling for Classification with Missing Data
Many real-world datasets suffer from the unavoidable issue of missing values, and therefore classification with missing data has to be carefully...
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Data triangulation and machine learning: a hybrid approach to fill missing climate data
Historical data in climatology is important for recognizing patterns and discovering trends. However, data gaps often occur in some weather station...
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Identifying missing data handling methods with text mining
Missing data is an inevitable aspect of every empirical research. Researchers developed several techniques to handle missing data to avoid...
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Fitting copulas in the case of missing data
In this paper we deal with parametric estimation of the copula in the case of missing data. The data items with the same pattern of complete and...
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Maximum likelihood estimation of missing data probability for nonmonotone missing at random data
In general, statistical analysis with missing data requires specification of a model for the missing data probability and/or the covariate...
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Model-based clustering with missing not at random data
Model-based unsupervised learning, as any learning task, stalls as soon as missing data occurs. This is even more true when the missing data are...
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On the use of multiple imputation to address data missing by design as well as unintended missing data in case-cohort studies with a binary endpoint
BackgroundCase-cohort studies are conducted within cohort studies, with the defining feature that collection of exposure data is limited to a subset...
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Discrimination of missing data types in metabolomics data based on particle swarm optimization algorithm and XGBoost model
In the field of data analysis, it is often faced with a large number of missing values, especially in metabolomics data, this problem is more...
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Multilevel Reliabilities with Missing Data
Reliabilities are widely used in social and behavioral sciences. The main purpose of this study was to investigate the performance of reliabilities...
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cnnImpute: missing value recovery for single cell RNA sequencing data
The advent of single-cell RNA sequencing (scRNA-seq) technology has revolutionized our ability to explore cellular diversity and unravel the...
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A Note on Ising Network Analysis with Missing Data
The Ising model has become a popular psychometric model for analyzing item response data. The statistical inference of the Ising model is typically...
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Bayesian pattern-mixture models for dropout and intermittently missing data in longitudinal data analysis
Valid inference can be drawn from a random-effects model for repeated measures that are incomplete if whether the data are missing or not, known as...
