Results 111 to 120 of about 175,878 (142)
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WIREs Computational Statistics, 2012
AbstractMissing data are a common problem in statistics. Imputation, or filling in the missing values, is an intuitive and flexible way to address the resulting incomplete data sets. We focus on multiple imputation, which, when implemented correctly, can be a statistically valid strategy for handling missing data.
Rässler, Susanne +2 more
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AbstractMissing data are a common problem in statistics. Imputation, or filling in the missing values, is an intuitive and flexible way to address the resulting incomplete data sets. We focus on multiple imputation, which, when implemented correctly, can be a statistically valid strategy for handling missing data.
Rässler, Susanne +2 more
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Abstract This chapter underscores the importance of being able to the norms of practical philosophy in concrete circumstances, particularly as relevant to Kant’s moral theory as expounded in the Groundwork and second Critique. Notably absent in these works is a comprehensive theory or even reflection on the application of moral laws ...
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Scandinavian Journal of Statistics, 1998
The method of likelihood imputation is devised under the framework of latent structure models where the observation is a statistic of the complete data which can only be specified on a latent basis. The imputed data set is chosen to differ least from the observed one in their information contents—a concept with general implications for the analysis of ...
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The method of likelihood imputation is devised under the framework of latent structure models where the observation is a statistic of the complete data which can only be specified on a latent basis. The imputed data set is chosen to differ least from the observed one in their information contents—a concept with general implications for the analysis of ...
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Computational Biology and Chemistry, 2009
Single imputation methods have been wide-discussed topics among researchers in the field of bioinformatics. One major shortcoming of methods proposed until now is the lack of robustness considerations. Like all data, gene expression data can possess outlying values.
vanden Branden, Karlien +1 more
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Single imputation methods have been wide-discussed topics among researchers in the field of bioinformatics. One major shortcoming of methods proposed until now is the lack of robustness considerations. Like all data, gene expression data can possess outlying values.
vanden Branden, Karlien +1 more
openaire +3 more sources
Multiple Imputation: An Iterative Regression Imputation
International Journal of Mathematical Sciences and Optimization: Theory and Applications, 2023Multiple imputation (MI) is a commonly applied method of statistically handling missing data. It involves imputing missing values repeatedly to account for the variability due to imputations. There are different techniques of MI that have proven to be effective and available in many statistical software packages.
Bintou, T., Ismaila, A. A.
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Retail and Distribution Management, 1973
From time to time we have had occasion to refer to earnings per share in terms of the new ‘imputation’ system of company taxation. This is a somewhat complex system and we have asked our Financial Correspondent to explain in some detail what is involved.
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From time to time we have had occasion to refer to earnings per share in terms of the new ‘imputation’ system of company taxation. This is a somewhat complex system and we have asked our Financial Correspondent to explain in some detail what is involved.
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Journal of the American Academy of Child & Adolescent Psychiatry, 2004
Calvin D, Croy, Douglas K, Novins
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Calvin D, Croy, Douglas K, Novins
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