Regression with an imputed dependent variable

Regression with imputed covariates: A generalized missing-indicator approach

A common problem in applied regression analysis is that covariate values may be missing for some observations but imputed values may be available. This situation generates a trade-off between bias and precision: the complete cases are often disarmingly few, but replacing the missing observations with the imputed values to gain precision may lead to bias. In this paper, we formalize this tradeof...

Regression with Missing Y’s: an Improved Strategy for Analyzing Multiply Imputed Data

Linear regression, the normal distribution of error values or normal distribution of the dependent variable?

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Binary Regression With a Misclassified Response Variable in Diabetes Data

Objectives: The categorical data analysis is very important in statistics and medical sciences. When the binary response variable is misclassified, the results of fitting the model will be biased in estimating adjusted odds ratios.  The present study aimed to use a method to detect and correct misclassification error in the response variable of Type 2 Diabetes Mellitus (T2DM), applying binary ...

Semiparametric Estimation of a Censored Regression Model with an Unknown Transformation of the Dependent Variable

In this paper we develop semiparametric estimators of and in the model (Y ) = min[ X + U;C], where Y is a nonnegative dependent variable, X is a vector of explanatory variables, U is an unobserved random \error" term with unknown distribution function , C is a random censoring variable, is an unknown parameter vector, and is an unknown strictly increasing function. This model includes as a spec...