Results 221 to 230 of about 1,667,708 (262)
Annual Review of Clinical PsychologyMethods for handling missing data in clinical psychology studies are reviewed. Missing data are defined, and a taxonomy of main approaches to analysis is presented, including complete-case and available-case analysis, weighting, maximum likelihood, Bayes, single and multiple imputation, and augmented inverse probability weighting.
Roderick Little
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NEJM Evidence, 2022
The Case of the Missing DataThis animated video explores how investigators approach missing data in clinical trials.
Katherine S, Takvorian +2 more
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The Case of the Missing DataThis animated video explores how investigators approach missing data in clinical trials.
Katherine S, Takvorian +2 more
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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
openaire +1 more source
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
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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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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 ...
openaire +1 more source