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1988
This paper deals with incomplete data sets. This subject is analyzed and some methods for estimating missing values are presented. Furthermore, the results of a simulation experiment are presented.
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This paper deals with incomplete data sets. This subject is analyzed and some methods for estimating missing values are presented. Furthermore, the results of a simulation experiment are presented.
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1995
In most statistical applications, we expect that a set of relevant variables is available for each subject. However, human subjects are notorious for imperfect cooperation with surveys, especially when they have little or no stake in the outcome of the data collection exercise.
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In most statistical applications, we expect that a set of relevant variables is available for each subject. However, human subjects are notorious for imperfect cooperation with surveys, especially when they have little or no stake in the outcome of the data collection exercise.
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Clustering Mixed Incomplete Data
2002In this chapter, we expose the possibilities of the Logical Combinatorial Pattern Recognition (LCPR) tools for Clustering Large and Very Large Mixed Incomplete Data (MID) Sets. We start from the real existence of a number of complex structures of large or very large data sets.
Jose Ruiz-Shulcloper +2 more
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An overview of real‐world data sources for oncology and considerations for research
Ca-A Cancer Journal for Clinicians, 2022Lynne Penberthy +2 more
exaly
Incompleteness of Lamotrigine Data
Drug Safety, 2001N V, Acharya, L V, Wilton, S A, Shakir
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2010
The most common classification setting is to predict labels from real-valued vectors, e.g. logistic regression or SupportVector Machines (SVM) are designed for this purpose. Our task differs from this: (1) The variables in our settings are defined over categorical domains with very many levels and there is no a priori knowledge about the space the ...
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The most common classification setting is to predict labels from real-valued vectors, e.g. logistic regression or SupportVector Machines (SVM) are designed for this purpose. Our task differs from this: (1) The variables in our settings are defined over categorical domains with very many levels and there is no a priori knowledge about the space the ...
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Innovations in research and clinical care using patient‐generated health data
Ca-A Cancer Journal for Clinicians, 2020H S L Jim +2 more
exaly