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63 results for “missing data”
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Research ArticleOpen accessIn the abstractGeneral Linear Models in a Missing Outcome Environment of Clinical Trials Incorporating with Splines for Time-Invariant Continuous AdjustmentMinjeong Park, Dejian Lai, Xianglin L. Du et al.Missing data is a common occurrence in longitudinal studies of health care research. Although many studies have shown the potential usefulness of current missing analyses, e.g., (1) Complete Case (CC) analysis; (2) imputation methods such as Last Observation Carried Forward (LOCF), multiple imputations, Expectation-Maximization algorithm approach; and (3) methods using all available data such as linear mixed model and generalized … …Current Research in Biostatistics, Vol. 5, Issue 1 · 18 Aug 2015 · 7,401 accessesView PDFCite
Research ArticleOpen accessIn the abstractAN EFFECTIVE TECHNIQUE OF MULTIPLE IMPUTATION IN NONPARAMETRIC QUANTILE REGRESSIONYanan Hu, Qianqian Zhu, Maozai TianIn this study, we consider the nonparametric quantile regression model with the covariates Missing at Random (MAR). Multiple imputation is becoming an increasingly popular approach for analyzing missing data, which combined with quantile regression is not well-developed. We propose an effective and accurate two-stage multiple imputation method for the model based on the quantile regression, which consists of initial imputation … …Journal of Mathematics and Statistics, Vol. 10, Issue 1 · 4 Jan 2014 · 6,813 accessesView PDFCite
Research ArticleOpen accessIn the abstractRadial Basis Function Network Dependent Exclusive Mutual Interpolation for Missing Value ImputationR. S. Somasundaram, R. NedunchezhianThe success of data mining relies on the purity of the data set. Before performing the data mining, the data has to be cleaned. An unprocessed data set may contain noisy or missing values which is a critical research issue in the pre-processing stage. Imputation methods are being used to solve the missing value problems. In this proposed work, a … …Journal of Computer Science, Vol. 9, Issue 3 · 24 Apr 2013 · 7,002 accessesView PDFCite
Research ArticleOpen accessIn the abstractPartial Least Squares Regression Based Variables Selection for Water Level PredictionsNoraini Ibrahim, Antoni WibowoFloods are common phenomenon in the state of Kuala Krai, specifically in Kelantan-Malaysia. Every year, floods affecting biodiversity on this region and also causing property loss of this residential area. The residents in Kelantan always suffered from floods since the water overflows to the areas adjoining to the rivers, lakes or dams. Months, average monthly rainfall, temperature, relative humidity and … …American Journal of Applied Sciences, Vol. 10, Issue 4 · 7 Apr 2013 · 6,621 accessesView PDFCite
Research ArticleOpen accessIn the abstractBayesian Exploration of Multilocus Interactions on the Genome-Wide ScaleIvan Kozyryev, Jing ZhangProblem statement: Recent technological and scientific advances propelled the field of Genome-Wide Association Study (GWAS), which promises to be instrumental in linking many common complex diseases to their genetic origin. While so far such large-scale surveys have been moderately successful in identifying disease related genetic variants, much of disease heritability is still not accounted for by the discovered loci. There … …Current Research in Bioinformatics, Vol. 1, Issue 1 · 2 Jul 2012 · 7,401 accessesView PDFCite
Research ArticleOpen accessIn the abstractHigh-Order Interactions in Rheumatoid Arthritis Detected by Bayesian Method using Genome-Wide Association Studies DataJing Zhang, Zheyang Wu, Chao Gao, Michael Q. ZhangProblem statement: In order to reveal the missing genetic component of Rheumatoid Arthritis (RA) susceptibility, we carried out a genome-wide high-order epistatic interaction study for RA. Approach: A powerful Bayesian strategy was applied to analyze the data of Genome-Wide Association Studies (GWAS) from the Welcome Trust Case Control Consortium (WTCCC), where 319 high-order interactions were found across the whole genome … …Current Research in Medicine, Vol. 3, Issue 1 · 30 Mar 2012 · 8,379 accessesView PDFCite
Research ArticleOpen accessIn the abstractOn Three-Way Unbalance Nested Analysis of VarianceSmaila S. Sanni, Eugene C. UkaegbuProblem statement: In this study, we give a simple analytically tractable procedure for solving three-way unbalanced nested Analysis of Variance (ANOVA). In many realistic situations, unbalanced design was unavoidable due to natural constraints and missing data. Approach: Here, we present a comprehensive approach for addressing solutions of problems arising from unbalanced nested ANOVA. We consider the F-statistics under the different … …Journal of Mathematics and Statistics, Vol. 8, Issue 1 · 10 Dec 2011 · 7,717 accessesView PDFCite
Research ArticleOpen accessIn the abstractRisk Factors to Alcohol Law Violations in the Community: Quasi-Experimental StudyWuttiphong Phakdeekul, Pramote Thongkrajai, Peem Eiamprapai, Manop KanatoProblem statement: Alcohol-related health problems such as chronic and acute diseases, accidents/injuries and social problems are well recognized. Although many countries including Thailand enact laws to control alcohol problems, missing data in Thailand cannot reveal efficacy of law enforcement and compliance with the public. The aim of this study was to investigate the proportion of Thai people violating alcohol laws … …American Journal of Applied Sciences, Vol. 8, Issue 12 · 22 Oct 2011 · 7,236 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Novel Technique for Web Log mining with Better Data Cleaning and Transaction IdentificationJ. Vellingiri, S. Chenthur PandianProblem statement: In the internet era web sites on the internet are useful source of information for almost every activity. So there is a rapid development of World Wide Web in its volume of traffic and the size and complexity of web sites. Web mining is the application of data mining, artificial intelligence, chart technology and so on to the … …Journal of Computer Science, Vol. 7, Issue 5 · 7 May 2011 · 6,889 accessesView PDFCite
Research ArticleOpen accessIn the abstractPredicting Missing Attribute Values Using k-Means ClusteringNambiraj Suguna, Keppana Gowder ThanushkodiProblem statement: Predicting the value for missing attributes is an important data preprocessing problem in data mining and knowledge discovery tasks. Several methods have been proposed to treat missing data and the one used more frequently is deleting instances containing at least one missing value of a feature. When the dataset has minimum number of missing attribute values then we … …Journal of Computer Science, Vol. 7, Issue 2 · 25 Feb 2011 · 7,873 accessesView PDFCite