Errors-in-variables problemRegression quantilesSemiparametric methodSurvival dataArticleWe study censored quantile regression with covariates measured with errors. We propose a composite quantile objective func
In some contexts, however, quantile regression can be challenging. The Method of Moments-Quantile Regression estimator can be useful in some of these cases. xtqreg and ivqreg2 make it easy to estimate quantile regressions with "…xed e¤ects" or endogenous variables. 22 References Abrevaya, J....
In this notebook we develop in detailQuantile Regression(QR) examples over 3D data via the functions of the paclet“AntonAntonov/QuantileRegression”, [AAp1]. The QR examples demonstrate using: QuantileRegressionwith “automatic”,NURBSfunctions (created viaBSplineFunction.) QuantileRegressionFitwith c...
With mean instrumental variables regression,k-class estimators have the potential to reduce bias, which is larger with weak instruments. With instrumental variables quantile regression, weak instrument-robust estimation is even more important because there is less guidance for assessing instrument strength....
In many statistical applications, the research question is more complicate than just a few moments, and there may be valuable information about the relationship between random variables that cannot be discovered based on a simple conditional mean analysis. Quantile regression-based methods provide a ...
Martina Pons Minimum distance quantile regression November 18, 2022 1 / 17 Introduction Model and estimator Asymptotics Traditional Panel Grouped Stata commands Application Summary • We are interested in the effect of some treatment variables on the distribution of an outcome =⇒ quantile regression...
In the median regression the constant is the median of the sample while in the .75 quantile regression the constant is the 75th percentile for the sample.Next, we'll add the binary predictor female to the model.qreg write female Iteration 1: WLS sum of weighted deviations = 1543....
Jiang R, Qian WM, Zhou ZG (2012b) Variable selection and coefficient estimation via composite quantile regression with randomly censored data. Stat Probab Lett 2:308–317 CrossRef Jiang R, Yang XH, Qian WM (2012c) Random weighting m-estimation for linear errors-in-variables models. J Korea...
Predictive regression models have been popular in empirical economics. A common example is to infer the predictive relation between financial returns and economic state variables. Nonstationary predictors in predictive regressions typically lead to a spurious inference. The conventional t-test based approach...
errors. Wei [7] developed an iterative imputation procedure for the covariates with missing values in a linear QR model that is valid under non-i.i.d. error terms. Lv [5] discussed smoothed empirical likelihood analysis with missing response in partially linear quantile regression. Sherwood [6...