Phenotype misclassification in genetic studies can decrease the power to detect association between a disease locus and a marker locus. To date, studies of misclassification have focused primarily on case-control designs. The purpose of this work is to quantify the effects of phenotype misclassification on the transmission disequilibrium test (TDT) applied to affected child trios, where both pa...

Cost-sensitive specialization is a generic technique for misclassification cost sensitive induction. This technique involves specializing aspects of a classifier associated with high misclassification costs and generalizing those associated with low misclassification costs. It is widely applicable and simple to implement. It could be used to augment the effect of standard cost-sensitive inducti...

Dot grids are often used to estimate the proportion of land cover belonging to some class in an aerial photograph. Interpreter misclassification is an often-ignored source of error in dot-grid sampling that has the potential to significantly bias proportion estimates. For the case when the true class of items is unknown, we present a maximum-likelihood estimator of misclassification probability...

AdaCost, a variant of AdaBoost, is a misclassification cost-sensitive boosting method. It uses the cost of misclassifications to update the training distribution on successive boosting rounds. The purpose is to reduce the cumulative misclassification cost more than AdaBoost. We formally show that AdaCost reduces the upper bound of cumulative misclassification cost of the training set. Empirical...

We study the effect of misclassification of a binary covariate on the parameters of a logistic regression model. In particular we consider 2 2 2 tables. We assume that a binary covariate is subject to misclassification that may depend on the observed outcome. This type of misclassification is known as (outcome dependent) differential misclassification. We examine the resulting asymptotic bias o...

Dot grids are often used to estimate the proportion of land cover belonging to some class in an aerial photograph. Interpreter misclassification is an often-ignored source of error in dot-grid sampling that has the potential to significantly bias proportion estimates. For the case when the true class of items is unknown, we present a maximum-likelihood estimator of misclassification probability...

BACKGROUND After 34 weeks gestation, summary measures of location for birthweight (e.g means and centiles) increase more slowly for Australian Aborigines than for whites. A similar pattern has been observed for blacks in the US. This study tests whether the reported pattern is due to differential misclassification of gestational age. METHODS Simulation was used to measure the potential effect...

The authors extend previous results on nondifferential exposure misclassification to the situation in which multilevel exposure and covariables are both misclassified. They show that if misclassification is nondifferential and the predictive value matrices are independent of other predictor variables it is possible to recover the true relative risks as a function of the biased estimates and the...

Previously, estimation of genotype misclassification of single nucleotide polymorphisms (SNPs) as encountered in epidemiologic practice and involving thousands of subjects was lacking. The authors collected representative data on approximately 14,000 subjects from 8 studies and 646,558 genotypes assessed in 2005 by means of matrix-assisted laser desorption ionization time-of-flight mass spectro...

Previously, estimation of genotype misclassification of single nucleotide polymorphisms (SNPs) as encountered in epidemiologic practice and involving thousands of subjects was lacking. The authors collected representative data on approximately 14,000 subjects from 8 studies and 646,558 genotypes assessed in 2005 by means of matrix-assisted laser desorption ionization time-of-flight mass spectro...