Censored quantile regression based on multiply robust propensity scores

Censored quantile regression has elicited extensive research interest in recent years. One class of methods is based on an informative subset of a sample, selected via the propensity score. Propensity score can either be estimated using parametric methods, which poses the risk of misspecification or obtained using nonparametric approaches, which suffer from “curse of dimensionality.” In this study, we propose a new estimation method based on multiply robust propensity score for censored quantile regression. This method only requires one of the multiple candidate models for propensity score to be correctly specified, and thus, it provides a certain level of resistance to the misspecification of parametric models. Large sample properties, such as the consistency and asymptotic normality of the proposed estimator, are thoroughly investigated. Extensive simulation studies are conducted to assess the performance of the proposed estimator. The proposed method is also applied to a study on human immunodeficiency viruses.

The comparison of censored quantile regression methods in prognosis factors of breast cancer survival

The Cox proportional hazards model is a widely used statistical method for the censored data that model the hazard rate rather than survival time. To overcome complexity of interpreting hazard ratio, quantile regression was introduced for censored data with more straightforward interpretation. Different methods for analyzing censored data using quantile regression model, have been introduced. The quantile regression approach models the quantile function of failure time and investigates the covariate effects in different quantiles. In this model, the covariate effects can be changed for patients with different risk and is a flexible model for controlling the heterogeneity of covariate effects. We illustrated and compared five methods in quantile regression for right censored data included Portnoy, Wang and Wang, Bottai and Zhang, Yang and De Backer methods. The comparison was made through the use of these methods in modeling the survival time of breast cancer. According to the results of quantile regression models, tumor grade and stage of the disease were identified as significant factors affecting 20th percentile of survival time. In Bottai and Zhang method, 20th percentile of survival time for a case with higher unit of stage decreased about 14 months and 20th percentile of survival time for a case with higher grade decreased about 13 months. The quantile regression models acted the same to determine prognostic factors of breast cancer survival in most of the time. The estimated coefficients of five methods were close to each other for quantiles lower than 0.1 and they were different from quantiles upper than 0.1.

Household level heterogeneity in the income elasticities of demand for international leisure travel

This article deepens the understanding of household level heterogeneity of income elasticities of demand for international leisure travel. This is done through the use of Swedish household level expenditure data which together with censored quantile regression allows for estimation of income elasticities based on relative consumption levels. In addition, an analysis of how the distribution of income elasticities was affected by the 2008 financial crisis is made. Results show a great heterogeneity in the estimated income elasticities, with income elasticities being the largest for the households who consume relatively little of the good, and a small positive effect of the financial crisis on the estimated distribution of income elasticities. These results can be used by policy makers, as well as managers in the tourism industry, to predict and influence the demand of international tourism at a more detailed level. The results also go in line with theoretical predictions and give further insight in market penetration as well as an ongoing structural change in the demand for international tourism.

DGQR estimation for interval censored quantile regression with varying-coefficient models

This paper propose a direct generalization quantile regression estimation method (DGQR estimation) for quantile regression with varying-coefficient models with interval censored data, which is a direct generalization for complete observed data. The consistency and asymptotic normality properties of the estimators are obtained. The proposed method has the advantage that does not require the censoring vectors to be identically distributed. The effectiveness of the method is verified by some simulation studies and a real data example.

Quantile Regression Approach to Model Censored Data

The censored quantile regression model is derived from the censored model. This method is used to overcome problems in modeling censored data as well as to overcome the assumptions of linear models that are not met. The purpose of this study is to compare the results of the analysis of the quantile regression method with the censored quantile regression method for censored data. Both methods were applied to generated data of 150, 500, and 3000 sample size. The best model is then chosen based on the smallest absolute bias and the smallest standard error as an indicator of the goodness of the model. This study proves that the censored quantile regression method tends to produce smaller absolute bias and a smaller standard error than the quantile regression method for all three group data specified. Thus it can be concluded that the censored quantile regression method could result in acceptable model for censored data. Keywords: Censored data; quantile regression; quantile regression censored; standard error; absolute bias.