Discriminant Analysis When a Block of Observations is Missing

Chung, Hie Choon; Han, Chien-Pai

doi:10.1023/a:1004129706000

Cited by 9 publications

(26 citation statements)

References 11 publications

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“…We consider the problem of classifying an unlabeled observation vector ( ) , when monotone missing training data are present, where i µ and Σ are the th i population mean vector and common covariance matrix, respectively. Here, we re-compare two linear classification procedures for block monotone missing (BMM) training data: one classifier is from [1], and the other classifier employs the maximum likelihood estimator (MLE).…”

Section: Introductionmentioning

confidence: 99%

“…Monotone missing data occur for an observation vector j x when, if ji x is missing, then jk x is missing for all k i > . The authors [1] claim that their "linear combination classification procedure is better than the substitution methods (MLE) as the proportion of missing observations gets larger" when block monotone missing data are present in the training data. Specifically, [1] has performed a Monte Carlo simulation and has concluded that their classifier performs better in terms of the expected error rate (EER) than the MLE substitution (MLES) classifier formulated by [2] as the proportion of missing observations increases.…”

Section: Introductionmentioning

confidence: 99%

“…The authors [1] claim that their "linear combination classification procedure is better than the substitution methods (MLE) as the proportion of missing observations gets larger" when block monotone missing data are present in the training data. Specifically, [1] has performed a Monte Carlo simulation and has concluded that their classifier performs better in terms of the expected error rate (EER) than the MLE substitution (MLES) classifier formulated by [2] as the proportion of missing observations increases. However, we demonstrate that for intra-class covariance training data with at least small correlations among the variables, the MLES classifier can significantly outperform the classifier from [1], which we refer to as the C-H classifier, in terms of their respective EERs.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Comparison of Two Linear Discriminant Analysis Methods That Use Block Monotone Missing Training Data

Young

Ounpraseuth

2016

OJS

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We revisit a comparison of two discriminant analysis procedures, namely the linear combination classifier of Chung and Han (2000) and the maximum likelihood estimation substitution classifier for the problem of classifying unlabeled multivariate normal observations with equal covariance matrices into one of two classes. Both classes have matching block monotone missing training data. Here, we demonstrate that for intra-class covariance structures with at least small correlation among the variables with missing data and the variables without block missing data, the maximum likelihood estimation substitution classifier outperforms the Chung and Han (2000) classifier regardless of the percent of missing observations. Specifically, we examine the differences in the estimated expected error rates for these classifiers using a Monte Carlo simulation, and we compare the two classifiers using two real data sets with monotone missing data via parametric bootstrap simulations. Our results contradict the conclusions of Chung and Han (2000) that their linear combination classifier is superior to the MLE classifier for block monotone missing multivariate normal data.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Comparison of Two Linear Discriminant Analysis Methods That Use Block Monotone Missing Training Data

Young

Ounpraseuth

2016

OJS

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“…Schafer, 1997, p. 218). There is an increasing interest in the development of statistical methods for handling monotone missing data from normal or elliptical populations (cf., for instance, Batsidis and Zografos, 2006;Chung and Han, 2000;Hao and Krishnamoorthy, 2001;Kanda and Fujikoshi, 1998;Krishnamoorthy and Pannala, 1998;and references therein).…”

Section: Introductionmentioning

confidence: 99%

Multivariate Linear Regression Model with Elliptically Contoured Distributed Errors and Monotone Missing Dependent Variables

Batsidis

Zografos

2008

Communications in Statistics - Theory and Methods

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In this article, the multivariate linear regression model is studied under the assumptions that the error term of this model is described by the elliptically contoured distribution and the observations on the response variables are of a monotone missing pattern. It is primarily concerned with estimation of the model parameters, as well as with the development of the likelihood ratio test in order to examine the existence of linear constraints on the regression coefficients. An illustrative example is presented for the explanation of the results.

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“…From the simulations, Chung and Han (2000) showed that the linear combination classification is better than Anderson's procedure (Anderson, 1957), the EM algorithm (Dempster et al, 1977), and Hocking and Smith procedure (Hocking and Smith, 1968) as the proportion of missing observation gets lager.…”

mentioning

confidence: 99%

Conditional bootstrap confidence intervals for classification error rate when a block of observations is missing

Chung¹,

Han²

2013

Journal of the Korean Data and Information Science Society

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In this paper, it will be assumed that there are two distinct populations which are multivariate normal with equal covariance matrix. We also assume that the two populations are equally likely and the costs of misclassification are equal. The classification rule depends on the situation whether the training samples include missing values or not. We consider the conditional bootstrap confidence intervals for classification error rate when a block of observation is missing.

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Discriminant Analysis When a Block of Observations is Missing

Cited by 9 publications

References 11 publications

A Comparison of Two Linear Discriminant Analysis Methods That Use Block Monotone Missing Training Data

A Comparison of Two Linear Discriminant Analysis Methods That Use Block Monotone Missing Training Data

Multivariate Linear Regression Model with Elliptically Contoured Distributed Errors and Monotone Missing Dependent Variables

Conditional bootstrap confidence intervals for classification error rate when a block of observations is missing

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