Results 281 to 290 of about 199,171 (299)
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Imputation for statistical inference with coarse data
Canadian Journal of Statistics, 2012AbstractCoarse data is a general type of incomplete data that includes grouped data, censored data, and missing data. The likelihoodâbased estimation approach with coarse data is challenging because the likelihood function is in integral form. The Monte Carlo EM algorithm of Wei & Tanner [Wei & Tanner (1990).Journal of the American Statistical ...
Kim, Jae Kwang, Hong, Minki
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Pooling test statistics across multiply imputed datasets for nonnormal items
Behavior Research Methods, 2023In structural equation modeling, when multiple imputation is used for handling missing data, model fit evaluation involves pooling likelihood-ratio test statistics across imputations. Under the normality assumption, the two most popular pooling approaches were proposed by Li et al.
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Small area statistics from survey and imputed data [PDF]
Statistical Journal of the United Nations Economic Commission for Europe, 1999 This paper discusses improvement of statistics for small areas and groups utilizing sample survey data and data imputed by intensive use of background data from available census or administrative register sources. Reliable accuracy prediction for such statistics is important and is also discussed and investigated in the paper.
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Statistically Evaluating the Imputation Accuracy of Highway Agencies
Applications of Advanced Technologies in Transportation Engineering (2004), 2004Data imputation has been practiced by traffic engineers since traffic data programs were established in the 1930s. However, a literature review indicates that no research has been conducted to assess imputation accuracy. An examination indicates that current practices are varied and intuitive in nature. Typical methods used by highway agencies are thus
Ming Zhong, Satish Sharma
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Introduction to Statistical Imputation
2018Chapters 11 and 12 are concerned with imputation methods and tools. The first of them gives an introduction that explains the term itself and looks again at missingness issues that have been considered already. Now the main concern is whether to use imputation.
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Multiple Imputation for Nonresponse and Statistical Disclosure Control
2011Most if not all surveys are subject to item nonresponse, and even registers can contain missing values, if implausible values are set to missing during the data-editing process. Since the generation of synthetic datasets is based on the ideas of multiple imputation, it is reasonable to use the approach to impute missing values and generate synthetic ...
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The broad role of multiple imputation in statistical science
2000Nearly a quarter century ago, the basic idea of multiple imputation was proposed as a way to deal with missing values due to nonresponse in sample surveys. Since that time, the essential formulation has expanded to be proposed for use in a remarkably broad range of empirical problems, from many standard social science and biomedical applications ...
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SLICE: generalised software for statistical data editing and imputation
2000Statistical offices have to face the problem that data collected by surveys or obtained from administrative registers generally contain errors. Another problem they have to face is that values in data sets obtained from these sources may be missing. To handle such errors and missing data efficiently, Statistics Netherlands is currently developing a ...
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