Results 241 to 250 of about 4,270,888 (278)
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Biclustering with missing data
Information Sciences, 2020Abstract Biclustering is a statistical learning methodology that simultaneously partitions rows and columns of a rectangular data array into homogeneous subsets. Biclustering is known to be an NP-hard problem, and therefore various heuristic approaches have been proposed.
Jing Li +4 more
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Journal of the American Geriatrics Society, 2010
Longitudinal study designs are indispensable for investigating age‐related functional change. There now are well‐established methods for addressing missing data in longitudinal studies. Modern missing data methods not only minimize most problems associated with missing data (e.g., loss of power and biased parameter estimates), but also have valuable ...
Raymond F, Palmer, Donald R, Royall
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Longitudinal study designs are indispensable for investigating age‐related functional change. There now are well‐established methods for addressing missing data in longitudinal studies. Modern missing data methods not only minimize most problems associated with missing data (e.g., loss of power and biased parameter estimates), but also have valuable ...
Raymond F, Palmer, Donald R, Royall
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Social Science Computer Review, 2013
Experience sampling research measures people’s thoughts, feelings, and actions in their everyday lives by repeatedly administering brief questionnaires throughout the day. Nonresponse—failing to respond to these daily life questionnaires—has been a vexing source of missing data.
Paul J. Silvia +3 more
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Experience sampling research measures people’s thoughts, feelings, and actions in their everyday lives by repeatedly administering brief questionnaires throughout the day. Nonresponse—failing to respond to these daily life questionnaires—has been a vexing source of missing data.
Paul J. Silvia +3 more
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Journal of Clinical Psychology, 1968
Abstract : The report deals with a multiple regression approach to the estimation of missing elements in a data matrix. Three types of missing data matrices are discussed and methods for their analysis are presented. Computational equations together with mathematical proofs are included. (Author)
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Abstract : The report deals with a multiple regression approach to the estimation of missing elements in a data matrix. Three types of missing data matrices are discussed and methods for their analysis are presented. Computational equations together with mathematical proofs are included. (Author)
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Missing inaction: the dangers of ignoring missing data
Trends in Ecology & Evolution, 2008The most common approach to dealing with missing data is to delete cases containing missing observations. However, this approach reduces statistical power and increases estimation bias. A recent study shows how estimates of heritability and selection can be biased when the 'invisible fraction' (missing data due to mortality) is ignored, thus ...
Shinichi, Nakagawa, Robert P, Freckleton
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International Journal of Decision Support System Technology, 2022
Many real world datasets may contain missing values for various reasons. These incomplete datasets can pose severe issues to the underlying machine learning algorithms and decision support systems. It may result in high computational cost, skewed output and invalid deductions. Various solutions exist to mitigate this issue; the most popular strategy is
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Many real world datasets may contain missing values for various reasons. These incomplete datasets can pose severe issues to the underlying machine learning algorithms and decision support systems. It may result in high computational cost, skewed output and invalid deductions. Various solutions exist to mitigate this issue; the most popular strategy is
openaire +1 more source
Aphasiology, 1995
Abstract It seems to me that one of the crucial facts disclosed by the lead paper (which is well organized and comprehensive) is that there remains a lack of basic information and/or hard data on some specific aspects of the subject-matter. That is to say, there seem to be many stones left unturned as yet to be able to solve the ‘puzzle’, i.e.
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Abstract It seems to me that one of the crucial facts disclosed by the lead paper (which is well organized and comprehensive) is that there remains a lack of basic information and/or hard data on some specific aspects of the subject-matter. That is to say, there seem to be many stones left unturned as yet to be able to solve the ‘puzzle’, i.e.
openaire +1 more source