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Maximum likelihood from incomplete data via the EM algorithm
 JOURNAL OF THE ROYAL STATISTICAL SOCIETY, SERIES B
, 1977
"... A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value situat ..."
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Cited by 11972 (17 self)
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A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value
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 Kmeans clu ..."
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Cited by 477 (24 self)
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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
Missing value estimation . . .
, 2004
"... Motivation: Gene expression data often contain missing expression values. Effective missing value estimation methods are needed since many algorithms for gene expression data analysis require a complete matrix of gene array values. In this paper, imputation methods based on the least squares formul ..."
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Motivation: Gene expression data often contain missing expression values. Effective missing value estimation methods are needed since many algorithms for gene expression data analysis require a complete matrix of gene array values. In this paper, imputation methods based on the least squares
Imputation Framework for Missing Values
"... AbstractMissing values may occur for several reasons and affects the quality of data, such as malfunctioning of measurement equipment, changes in experimental design during data collection, collation of several similar but not identical datasets and also when respondents in a survey may refuse to ..."
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AbstractMissing values may occur for several reasons and affects the quality of data, such as malfunctioning of measurement equipment, changes in experimental design during data collection, collation of several similar but not identical datasets and also when respondents in a survey may refuse
On Missing Values and Fuzzy Rules
"... Numerous learning tasks involve incomplete or conflicting attributes. Most algorithms that automatically find a set of fuzzy rules are not well suited to tolerate missing values in the input vector, and the usual technique to substitute missing values by their mean or another constant value can be q ..."
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Numerous learning tasks involve incomplete or conflicting attributes. Most algorithms that automatically find a set of fuzzy rules are not well suited to tolerate missing values in the input vector, and the usual technique to substitute missing values by their mean or another constant value can
Ordered Estimation of Missing Values
, 1999
"... . When attempting to discover by learning concepts embedded in data, it is not uncommon to find that information is missing from the data. Such missing information can diminish the confidence on the concepts learned from the data. This paper describes a new approach to fill missing values in exa ..."
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Cited by 4 (0 self)
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. When attempting to discover by learning concepts embedded in data, it is not uncommon to find that information is missing from the data. Such missing information can diminish the confidence on the concepts learned from the data. This paper describes a new approach to fill missing values
Framework for Missing Value Imputation
"... Abstract — Missing values may occur due to several reasons. In this paper, data is imputed by comparing the two most popular techniques. Mean Substitution the traditional method replaces mean value in Kmeans Clustering and in groups of kNN classifier. When compared in terms of accuracy of imputing ..."
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Abstract — Missing values may occur due to several reasons. In this paper, data is imputed by comparing the two most popular techniques. Mean Substitution the traditional method replaces mean value in Kmeans Clustering and in groups of kNN classifier. When compared in terms of accuracy of imputing
Exploring Missing Values 2
"... Using data from the "Armed Forces 2002 Sexual Harassment Survey, " this paper analyzed the extent to which respondents refused to report experiences of sexual harassment (i.e., the responses were missing for that question). Specifically, the total percentages reporting personally experienc ..."
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with missing values on the sexual harassment question. Data analysis revealed that the units in the “worst ” quartile (i.e., highest reports of sexist behavior) had the most missing
Missing Values in Epidemiological Studies
"... this paper we restrict ourselves to item nonresponse. Item nonresponse may arise because a person refuses to answer to certain questions, e.g. if the question is too sensitive or is regarded as too private (e.g. alcohol consumption, sexual behavior, income, health related questions). What is regar ..."
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not be willing to report their income. Another reason for missing values is that subjects do not know the answer because they are unable to recall certain events in their past. It also happens that a given answer is inconsistent with other answers and can therefore not be used in the analysis (e.g. if a persons
Accuracy in Imputing Missing Values
"... For a joint distribution P(A,B,C) and an evidence B=true, marginal inference calculation is: P(A  B = true) ∝∑ C P(A, B = true,C). To impute missing values, we draw samples under given evidence from consistent junction tree using BRMLToolbox. Comma in labels in Xaxis separates dimensions of two da ..."
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For a joint distribution P(A,B,C) and an evidence B=true, marginal inference calculation is: P(A  B = true) ∝∑ C P(A, B = true,C). To impute missing values, we draw samples under given evidence from consistent junction tree using BRMLToolbox. Comma in labels in Xaxis separates dimensions of two
Results 1  10
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5,551