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63 results for “missing data”
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Review ArticleOpen accessPhrase in the titleReview of Zero-Inflated Models with Missing DataT. Martin Lukusa, Shen-Ming Lee, Chin-Shang LiThe literature of count regression models covers a large scope of studies and applications that implemented simple and standard models for count response variables by using Poisson regression models, binomial regression models, negative binomial regression models, geometric regression models, or generalized Poisson regression models. These regression models have received considerable attention in various situations. Nevertheless in many fields, the distribution … …Current Research in Biostatistics, Vol. 7, Issue 1 · 31 Oct 2017 · 14,406 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleMISSING DATA IMPUTATION USING WEIGHTED OF REGIME SWITCHING MEAN AND REGRESSIONJumlong Vongprasert, Bhusana PremanodeMissing data imputation is an important task in cases where it is crucial to use all available data and not discard records with missing values. The purpose of this work were first to develop the Weighted of Regime Switching Mean and Regression (WRSMRI) for missing data estimation and secondly to compare its efficiency of estimation and statistical power of a … …Journal of Mathematics and Statistics, Vol. 10, Issue 2 · 3 May 2014 · 6,704 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleA framework to Deal with Missing Data in Data SetsLuai A. Shalabi, Mohannad Najjar, Ahmad A. KayedMost information systems usually have some missing values due to unavailable data. Missing values minimizing the quality of classification rules generated by a data mining system. Missing vales also affecting the quantity of classification rules achieved by the data mining system. Missing values could influence the coverage percentage and number of reducts generated. Missing values lead to the difficulty of … …Journal of Computer Science, Vol. 2, Issue 9 · 30 Sep 2006 · 7,134 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleParameter-Expanded Data Augmentation for Analyzing Nominal Data With Missing Values Using Multinomial Probit ModelsSuwash Silwal, Xiao ZhangNominal data occur in many scientific fields, such as health-related studies, transportation, and econometrics. Due to the ubiquitousness of missing data, it is inevitable to analyze nominal data with missing values. As is well known, statistical methods for analyzing nominal variables are quite limited. The Multinomial Probit (MNP) model has been an essential tool for analyzing nominal categorical data, but … …Journal of Mathematics and Statistics, Vol. 22, Issue 1 · 23 May 2026 · 1,804 accessesView PDFCite
Review ArticleOpen accessIn the keywordsBibliometric Analysis of Missing Value Imputation (MVI) Research Using Python and VOSviewer: Trends and Future DirectionsSamsul Arifin, Muhammad Faisal, Edwin Kristianto Sijabat et al.Incomplete data is a pervasive issue in data science, posing significant challenges for statistical analysis and machine learning applications. Missing Value Imputation (MVI) has thus emerged as a critical area of research aimed at mitigating data quality issues and improving decision-making accuracy. This study presents a comprehensive bibliometric analysis of MVI research using Scopus-indexed publications from 2000 to 2024. The … …Journal of Computer Science, Vol. 21, Issue 12 · 20 Jan 2026 · 1,961 accessesView PDFCite
Research ArticleOpen accessIn the keywordsOn the Comparison of Methods of Estimating Missing Values in Rectangular Lattice DesignsAbimibola V. Oladugba, Emmanuel O. Ossai, Tobias E. UgahMissing values occur in almost all research which lead to ambiguity in data analysis. It becomes necessary that appropriate consideration is made in order to provide an efficient and valid analysis. Researchers have developed and compared a variety of methods of estimating missing values in experimental designs; however, no research work has derived and compared methods of estimating missing values, … …Journal of Mathematics and Statistics, Vol. 14, Issue 1 · 11 Oct 2018 · 7,044 accessesView PDFCite
Research ArticleOpen accessIn the keywordsTrial Design and Analysis with Incomplete Paired DataMark Chang, Jing WangFor a clinical trial design with paired data, it often involves missing observations. In such a case, the data from the trial become a mixture of paired and unpaired data. A commonly used approach for the analysis of the trial data is to ignore the incomplete pairs. Such a treatment of missing data is not statistically efficient. We propose a … …Current Research in Biostatistics, Vol. 5, Issue 2 · 12 Nov 2015 · 7,889 accessesView PDFCite
Research ArticleOpen accessIn the keywordsNeural Networks Based Nonlinear Time Series Regression for Water Level Forecasting of Dungun RiverSiti Hajar Arbain, Antoni WibowoThe Department of Irrigation and Drainage (DID) Malaysia and Meteorological Malaysia Department (MMD) has been measured the flood characteristics benchmark which included water level, area inundation, peak inundation, peak discharge, volume of flow and duration of flooding. In terms of water levels, DID have introduced three categories of critical level stages namely normal, alert and danger levels. One of the … …Journal of Computer Science, Vol. 8, Issue 9 · 13 Aug 2012 · 7,792 accessesView PDFCite
Research ArticleOpen accessIn the keywordsImproving Accuracy and Coverage of Data Mining Systems that are Built from Noisy Datasets: A New ModelLuai A. Al ShalabiProblem statement: Noise within datasets has to be dealt with under most circumstances. This noise includes misclassified data or information as well as missing data or information. Simple human error is considered as misclassification. These errors will decrease the accuracy of the data mining system so it will not be likely to be used. The objective was to propose an … …Journal of Computer Science, Vol. 5, Issue 2 · 28 Feb 2009 · 7,820 accessesView PDFCite
Research ArticleOpen accessIn the keywordsModified Human Development Index using Data Envelopment Analysis ApproachYasmine Refai Salama, Ramadan Hamed, Mahmoud RashwanComposite Indicator is considered themathematical aggregation which has wide usage for monitoring performances,conducting benchmarks, analyzing policies, and communicating publicly. HumanDevelopment Index (HDI) is the most popular index which measures humandevelopment through average achievement in its main dimensions: Health status,education status, and living standard but it is faced with several critiques,positive and negative. Moreover, HDI was tested to have a positive … …Journal of Mathematics and Statistics, Vol. 18, Issue 1 · 26 Sep 2022 · 6,088 accessesView PDFCite