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Results 1 - 10
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46,897
Missing data: Our view of the state of the art
- Psychological Methods
, 2002
"... Statistical procedures for missing data have vastly improved, yet misconception and unsound practice still abound. The authors frame the missing-data problem, review methods, offer advice, and raise issues that remain unresolved. They clear up common misunderstandings regarding the missing at random ..."
Abstract
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Cited by 689 (1 self)
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most data analysis proce-dures were not designed for them. Missingness is usu-ally a nuisance, not the main focus of inquiry, but
The particel swarm: Explosion, stability, and convergence in a multi-dimensional complex space
- IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTION
"... The particle swarm is an algorithm for finding optimal regions of complex search spaces through interaction of individuals in a population of particles. Though the algorithm, which is based on a metaphor of social interaction, has been shown to perform well, researchers have not adequately explained ..."
Abstract
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Cited by 822 (10 self)
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The particle swarm is an algorithm for finding optimal regions of complex search spaces through interaction of individuals in a population of particles. Though the algorithm, which is based on a metaphor of social interaction, has been shown to perform well, researchers have not adequately
Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation
- American Political Science Review
, 2000
"... We propose a remedy for the discrepancy between the way political scientists analyze data with missing values and the recommendations of the statistics community. Methodologists and statisticians agree that "multiple imputation" is a superior approach to the problem of missing data scatter ..."
Abstract
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Cited by 389 (49 self)
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We propose a remedy for the discrepancy between the way political scientists analyze data with missing values and the recommendations of the statistics community. Methodologists and statisticians agree that "multiple imputation" is a superior approach to the problem of missing data
Missing value estimation methods for DNA microarrays
, 2001
"... Motivation: Gene expression microarray experiments can generate data sets with multiple missing expression values. Unfortunately, many algorithms for gene expression analysis require a complete matrix of gene array values as input. For example, methods such as hierarchical clustering and K-means clu ..."
Abstract
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Cited by 476 (26 self)
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-means clustering are not robust to missing data, and may lose effectiveness even with a few missing values. Methods for imputing missing data are needed, therefore, to minimize the effect of incomplete data sets on analyses, and to increase the range of data sets to which these algorithms can be applied
Discovering Statistically Significant Biclusters in Gene Expression Data
- In Proceedings of ISMB 2002
, 2002
"... In gene expression data, a bicluster is a subset of the genes exhibiting consistent patterns over a subset of the conditions. We propose a new method to detect significant biclusters in large expression datasets. Our approach is graph theoretic coupled with statistical modelling of the data. Under p ..."
Abstract
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Cited by 299 (4 self)
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In gene expression data, a bicluster is a subset of the genes exhibiting consistent patterns over a subset of the conditions. We propose a new method to detect significant biclusters in large expression datasets. Our approach is graph theoretic coupled with statistical modelling of the data. Under
Understanding Normal and Impaired Word Reading: Computational Principles in Quasi-Regular Domains
- PSYCHOLOGICAL REVIEW
, 1996
"... We develop a connectionist approach to processing in quasi-regular domains, as exemplified by English word reading. A consideration of the shortcomings of a previous implementation (Seidenberg & McClelland, 1989, Psych. Rev.) in reading nonwords leads to the development of orthographic and phono ..."
Abstract
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Cited by 583 (94 self)
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in subsequent simulations, including an attractor network that reproduces the naming latency data directly in its time to settle on a response. Further analyses of the network's ability to reproduce data on impaired reading in surface dyslexia support a view of the reading system that incorporates a graded
A solution to Plato’s problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge
- PSYCHOLOGICAL REVIEW
, 1997
"... How do people know as much as they do with as little information as they get? The problem takes many forms; learning vocabulary from text is an especially dramatic and convenient case for research. A new general theory of acquired similarity and knowledge representation, latent semantic analysis (LS ..."
Abstract
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Cited by 1772 (10 self)
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(LSA), is presented and used to successfully simulate such learning and several other psycholinguistic phenomena. By inducing global knowledge indirectly from local co-occurrence data in a large body of representative text, LSA acquired knowledge about the full vocabulary of English at a comparable
A Meta-Analytic Review of Experiments Examining the Effects of Extrinsic Rewards on Intrinsic Motivation
"... A meta-analysis of 128 studies examined the effects of extrinsic rewards on intrinsic motivation. As predicted, engagement-contingent, completion-contingent, and performance-contingent rewards signifi-cantly undermined free-choice intrinsic motivation (d =-0.40,-0.36, and-0.28, respectively), as did ..."
Abstract
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Cited by 602 (16 self)
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A meta-analysis of 128 studies examined the effects of extrinsic rewards on intrinsic motivation. As predicted, engagement-contingent, completion-contingent, and performance-contingent rewards signifi-cantly undermined free-choice intrinsic motivation (d =-0.40,-0.36, and-0.28, respectively
Time Discounting and Time Preference: A Critical Review
- Journal of Economic Literature
, 2002
"... www.people.cornell.edu/pages/edo1/. ..."
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