Results 221 to 230 of about 1,743,603 (263)
Some of the next articles are maybe not open access.
2005
Abstract Every now and then the best laid plans of rats and sophomores go awry, and a carefully designed study ends up with missing scores. An animal may die, or a human may not show up, and the researcher must deal with a reduced set of data.
Molenberghs, G. +5 more
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Abstract Every now and then the best laid plans of rats and sophomores go awry, and a carefully designed study ends up with missing scores. An animal may die, or a human may not show up, and the researcher must deal with a reduced set of data.
Molenberghs, G. +5 more
openaire +3 more sources
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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Principled Missing Data Treatments
Prevention Science, 2016 We review a number of issues regarding missing data treatments for intervention and prevention researchers. Many of the common missing data practices in prevention research are still, unfortunately, ill-advised (e.g., use of listwise and pairwise deletion, insufficient use of auxiliary variables).
Kyle M Lang, Todd D. Little
exaly +5 more sources
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
openaire +1 more source
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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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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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 +2 more sources
Organizational Research Methods, 2014
Missing data (a) reside at three missing data levels of analysis (item-, construct-, and person-level), (b) arise from three missing data mechanisms (missing completely at random, missing at random, and missing not at random) that range from completely random to systematic missingness, (c) can engender two missing data problems (biased parameter ...
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Missing data (a) reside at three missing data levels of analysis (item-, construct-, and person-level), (b) arise from three missing data mechanisms (missing completely at random, missing at random, and missing not at random) that range from completely random to systematic missingness, (c) can engender two missing data problems (biased parameter ...
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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