Add to ORKGEstimation of models for transitions between a set of states could be severely biased if units are incorrectly classified. In the paper a bayesian strategy to deal with misclassification is proposed
Includes bibliographical references (p. 106-109).Binary misclassification is a common occurrence in ...
Count data are subject to considerable sources of what is often referred to as non-sampling error. E...
This paper provides conditions for identification and estimation of the conditional or unconditional...
This paper considers estimation of success probabilities of categorical binary data subject to miscl...
Survey data are often subject to various types of errors such as misclassification. In this article,...
Survival data analysis aims at collecting data on durations spent in a state by a sample of units, i...
Estimated associations between an outcome variable and misclassified covariates tend to be biased wh...
Covariate misclassification is well known to yield biased estimates in single level regression model...
Misclassification probability densities for the a. miscoded observations and b. correctly coded obse...
We consider Bayesian inference about the mean of a binary variable that is subject to misclassificat...
Misclassification of epidemiological and observational data is a problem that commonly arises and ca...
Three Bayesian approaches are considered for the selection of binomial proportion parameters when da...
Abstract: Count data are subject to considerable sources of what is often referred to as non-samplin...
Summary. Motivated by a study of human papillomavirus infection in women, we present a Bayesian bino...
Includes bibliographical references (p. ).Mismeasurment, and specifically misclassification, are ine...
Includes bibliographical references (p. 106-109).Binary misclassification is a common occurrence in ...
Count data are subject to considerable sources of what is often referred to as non-sampling error. E...
This paper provides conditions for identification and estimation of the conditional or unconditional...
This paper considers estimation of success probabilities of categorical binary data subject to miscl...
Survey data are often subject to various types of errors such as misclassification. In this article,...
Survival data analysis aims at collecting data on durations spent in a state by a sample of units, i...
Estimated associations between an outcome variable and misclassified covariates tend to be biased wh...
Covariate misclassification is well known to yield biased estimates in single level regression model...
Misclassification probability densities for the a. miscoded observations and b. correctly coded obse...
We consider Bayesian inference about the mean of a binary variable that is subject to misclassificat...
Misclassification of epidemiological and observational data is a problem that commonly arises and ca...
Three Bayesian approaches are considered for the selection of binomial proportion parameters when da...
Abstract: Count data are subject to considerable sources of what is often referred to as non-samplin...
Summary. Motivated by a study of human papillomavirus infection in women, we present a Bayesian bino...
Includes bibliographical references (p. ).Mismeasurment, and specifically misclassification, are ine...
Includes bibliographical references (p. 106-109).Binary misclassification is a common occurrence in ...
Count data are subject to considerable sources of what is often referred to as non-sampling error. E...
This paper provides conditions for identification and estimation of the conditional or unconditional...