This paper considers identification and estimation of a nonparametric regression model with an unobserved discrete covariate. The sample consists of a dependent variable and a set of covariates, one of which is discrete and arbitrarily correlates with the unobserved covariate. The observed discrete covariate has the same support as the unobserved covariate, and can be interpreted as a proxy or ...

Censored quantile regression has become an important alternative to the Cox proportional hazards model in survival analysis. In contrast to the central covariate effect from the meanbased hazard regression, quantile regression can effectively characterize the covariate effects at different quantiles of the survival time. When covariates are measured with errors, it is known that naively treatin...

soil temperature is one of the most effective parameters for plant growth. although the spatiotemporal monitoring of soil temperature is important for providing an optimum condition to maximize the yield efficiency, indirect measurements for zoning  of soil temperature are essential due to difficulties in direct methods of measuring it and also the limited measurement points. thus, the efficien...

Methods are devised for estimating the parameters of a prospective logistic model in a case{control study with dichotomous response D which depends on a covariate X. For a portion of the sample, both the gold standard X and a surrogate covariate W are available; however, for the greater portion of the data only the surrogate covariate W is available. By using a mixture model, the relationship b...

This paper presents a simulation study to investigate the behavior of estimates of covariate effects in a Bayesian hierarchical changepoint model. The model is a segmented linear regression model with one changepoint within a fully Bayesian framework. We introduce covariate effects into the model in three ways: as an affect on the interaction term, an affect on the slope after the changepoint, ...

Censored quantile regression is an important alternative to the Cox proportional hazards model in survival analysis. In contrast to the usual central covariate effects, quantile regression can effectively characterize the covariate effects at different quantiles of the survival time. When covariates are measured with errors, it is known that naively treating mismeasured covariates as error-free...

We consider estimation of linear functionals of the error distribution for two regression models: parametric and nonparametric, and for two types of errors: independent of the covariate and centered (type I), and conditionally centered given the covariate (type II). We show that the residual-based empirical estimators for the nonparametric type I model remain efficient in the type II model. For...

Attrition is a common occurrence in cluster randomised trials which leads to missing outcome data. Two approaches for analysing such trials are cluster-level analysis and individual-level analysis. This paper compares the performance of unadjusted cluster-level analysis, baseline covariate adjusted cluster-level analysis and linear mixed model analysis, under baseline covariate dependent missin...