Results 41 to 50 of about 24,713,244 (334)

A Nonconvex Low-Rank Tensor Completion Model for Spatiotemporal Traffic Data Imputation [PDF]

Transportation Research Part C: Emerging Technologies, 2020
Sparsity and missing data problems are very common in spatiotemporal traffic data collected from various sensing systems. Making accurate imputation is critical to many applications in intelligent transportation systems.
Xinyu Chen, Jin-Ming Yang, Lijun Sun
semanticscholar   +1 more source

Multiple imputation of maritime search and rescue data at multiple missing patterns.

PLoS ONE, 2021
Based on the missing situation and actual needs of maritime search and rescue data, multiple imputation methods were used to construct complete data sets under different missing patterns.
Guobo Wang, Minglu Ma, Lili Jiang, Fengyun Chen, Liansheng Xu   +4 more
doaj   +1 more source

Improved Imputation of Common and Uncommon Single Nucleotide Polymorphisms (SNPs) with a New Reference Set [PDF]

, 2011
Statistical imputation of genotype data is an important technique for analysis of genome-wide association studies (GWAS). We have built a reference dataset to improve imputation accuracy for studies of individuals of primarily European descent using ...
Amy Hutchinson, Ann W. Hsing, Brian E. Henderson, Charles C. Chung, Christopher M. Haiman, Daniel Stram, Demetrius Albanes, Jarmo Virtamo, Jennifer L. Stone, Joshua Sampson, Kevin Jacobs, Lauren R. Teras, Margaret Tucker, Mark P. Purdue, Meredith Yeager, Michael A. Eberle, Nilanjan Chatterjee, Phil Taylor, Sonja I. Berndt, Stephen Chanock, Susan M. Gabstur, W. Ryan Diver, Xiang Deng, Zhaoming Wang   +23 more
core   +2 more sources

Comparison of Missing Data Imputation Methods in Time Series Forecasting

Computers Materials & Continua, 2022
: Time series forecasting has become an important aspect of data analysis and has many real-world applications. However, undesirable missing values are often encountered, which may adversely affect many forecasting tasks.
Hyun Ahn, Kyunghee Sun, Kwanghoon Pio Kim   +2 more
semanticscholar   +1 more source

Data Imputation and Body Weight Variability Calculation Using Linear and Nonlinear Methods in Data Collected From Digital Smart Scales: Simulation and Validation Study

JMIR mHealth and uHealth, 2020
BackgroundBody weight variability (BWV) is common in the general population and may act as a risk factor for obesity or diseases. The correct identification of these patterns may have prognostic or predictive value in clinical and research settings. With
Turicchi, Jake, O'Driscoll, Ruairi, Finlayson, Graham, Duarte, Cristiana, Palmeira, A L, Larsen, Sofus C, Heitmann, Berit L, Stubbs, R James   +7 more
doaj   +1 more source

Accuracy of random-forest-based imputation of missing data in the presence of non-normality, non-linearity, and interaction

BMC Medical Research Methodology, 2020
Background Missing data are common in statistical analyses, and imputation methods based on random forests (RF) are becoming popular for handling missing data especially in biomedical research.
Shangzhi Hong, Henry S. Lynn
doaj   +1 more source

Multiply-Imputed Synthetic Data: Advice to the Imputer [PDF]

Journal of Official Statistics, 2017
Abstract Several statistical agencies have started to use multiply-imputed synthetic microdata to create public-use data in major surveys. The purpose of doing this is to protect the confidentiality of respondents’ identities and sensitive attributes, while allowing standard complete-data analyses of microdata.
Loong, Bronwyn, Rubin, Donald B
openaire   +2 more sources

Odyssey: a semi-automated pipeline for phasing, imputation, and analysis of genome-wide genetic data [PDF]

, 2019
BACKGROUND: Genome imputation, admixture resolution and genome-wide association analyses are timely and computationally intensive processes with many composite and requisite steps.
Eller, Ryan J., Janga, Sarath C., Walsh, Susan   +2 more
core   +1 more source

An Improved Air Quality Index Machine Learning-Based Forecasting with Multivariate Data Imputation Approach

Atmosphere, 2022
Accurate, timely air quality index (AQI) forecasting helps industries in selecting the most suitable air pollution control measures and the public in reducing harmful exposure to pollution.
Hanin Alkabbani, A. Ramadan, Qinqin Zhu, A. Elkamel   +3 more
semanticscholar   +1 more source

Impact of Missing Data on Data Quality in Social Research

Соціологічні студії
Missing 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
doaj   +1 more source