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Correcting sample selection bias by unlabeled data
"... We consider the scenario where training and test data are drawn from different distributions, commonly referred to as sample selection bias. Most algorithms for this setting try to first recover sampling distributions and then make appropriate corrections based on the distribution estimate. We prese ..."
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Cited by 207 (11 self)
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We consider the scenario where training and test data are drawn from different distributions, commonly referred to as sample selection bias. Most algorithms for this setting try to first recover sampling distributions and then make appropriate corrections based on the distribution estimate. We
Sample Selection Bias and Time
"... Key words: paintings; hedonic price; price index. Abstract: The uniqueness of art objects need to be taken into account in the construction of any art market price index. Yet the most widely used methods typically rely on biased samples, discarding a very large proportion of the information availa ..."
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Key words: paintings; hedonic price; price index. Abstract: The uniqueness of art objects need to be taken into account in the construction of any art market price index. Yet the most widely used methods typically rely on biased samples, discarding a very large proportion of the information
• Sample selectivity bias
"... Regression is a useful technique for summarizing data and is widely used to test hypotheses and to quantify the influence of independent variables on a dependent variable. This chapter first reviews the vocabulary used in regression analysis, and then uses an example to introduce the key notions of ..."
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” and “independent” variables may in fact be determined simultaneously, the sample on which the estimation is based may be biased, independent variables may be correlated (“multicolinearity”), the error term may not have a constant variance, and outliers may have a strong influence on the results. The chapter
Correcting Sample Selection Bias in Maximum
"... We study the problem of maximum entropy density estimation in the presence of known sample selection bias. We propose three bias correction approaches. The first one takes advantage of unbiased sufficient statistics which can be obtained from biased samples. The second one estimates the biased d ..."
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We study the problem of maximum entropy density estimation in the presence of known sample selection bias. We propose three bias correction approaches. The first one takes advantage of unbiased sufficient statistics which can be obtained from biased samples. The second one estimates the biased
Sample Selection Bias Correction Theory
"... Abstract. This paper presents a theoretical analysis of sample selection bias correction. The sample bias correction technique commonly used in machine learning consists of reweighting the cost of an error on each training point of a biased sample to more closely reflect the unbiased distribution. T ..."
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Cited by 27 (3 self)
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Abstract. This paper presents a theoretical analysis of sample selection bias correction. The sample bias correction technique commonly used in machine learning consists of reweighting the cost of an error on each training point of a biased sample to more closely reflect the unbiased distribution
Sample Selection Bias in the Pathways to Adult Health Inequalities
, 2009
"... Sample selection bias is a chronic problem in longitudinal studies that is particularly problematic for studies concerning the relationship between health and socioeconomic status. This paper adopts two alternate methods for handling sample selection bias attributable to survey attrition and item n ..."
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Sample selection bias is a chronic problem in longitudinal studies that is particularly problematic for studies concerning the relationship between health and socioeconomic status. This paper adopts two alternate methods for handling sample selection bias attributable to survey attrition and item
Sample Selection Bias in Models of Commuting Time
"... Summary. This research conceptualises, measures and evaluates the effects of sample selection bias on models of commuting time. Data are drawn from the Public Use Microdata Sample of the 1990 US Census for the Boston metropolitan area. The major ® nding of the analysis is that the process that deter ..."
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Cited by 1 (0 self)
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Summary. This research conceptualises, measures and evaluates the effects of sample selection bias on models of commuting time. Data are drawn from the Public Use Microdata Sample of the 1990 US Census for the Boston metropolitan area. The major ® nding of the analysis is that the process
YTS, employment and sample selection bias
 Oxford Economic Papers
, 1994
"... IN THIS paper, data from the first Youth Cohort Study (YCS) are used to analyse the effectiveness of the Youth Training Scheme (YTS) in England and Wales in terms of its impact, at an individual level, on the employment prospects of young people. The analysis goes beyond previous work in that it exp ..."
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Cited by 3 (0 self)
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IN THIS paper, data from the first Youth Cohort Study (YCS) are used to analyse the effectiveness of the Youth Training Scheme (YTS) in England and Wales in terms of its impact, at an individual level, on the employment prospects of young people. The analysis goes beyond previous work in that it explicitly deals
Learning and Evaluating Classifiers under Sample Selection Bias
 In International Conference on Machine Learning ICML’04
, 2004
"... Classifier learning methods commonly assume that the training data consist of randomly drawn examples from the same distribution as the test examples about which the learned model is expected to make predictions. ..."
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Cited by 118 (2 self)
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Classifier learning methods commonly assume that the training data consist of randomly drawn examples from the same distribution as the test examples about which the learned model is expected to make predictions.
Results 1  10
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1,554,679