Interquantile Shrinkage in Regression Models
نویسندگان
چکیده
منابع مشابه
Interquantile Shrinkage in Regression Models.
Conventional analysis using quantile regression typically focuses on fitting the regression model at different quantiles separately. However, in situations where the quantile coefficients share some common feature, joint modeling of multiple quantiles to accommodate the commonality often leads to more efficient estimation. One example of common features is that a predictor may have a constant e...
متن کاملInterquantile shrinkage and variable selection in quantile regression
Examination of multiple conditional quantile functions provides a comprehensive view of the relationship between the response and covariates. In situations where quantile slope coefficients share some common features, estimation efficiency and model interpretability can be improved by utilizing such commonality across quantiles. Furthermore, elimination of irrelevant predictors will also aid in...
متن کاملShrinkage structure in biased regression
Biased regression is an alternative to ordinary least squares (OLS) regression, especially when explanatory variables are highly correlated. In this paper, we examine the geometrical structure of the shrinkage factors of biased estimators. We show that, in most cases, shrinkage factors cannot belong to [0, 1] in all directions. We also compare the shrinkage factors of ridge regression (RR), pri...
متن کاملShrinkage Estimation of Regression Models with Multiple Structural Changes
In this paper we consider the problem of determining the number of structural changes in multiple linear regression models via group fused Lasso (least absolute shrinkage and selection operator). We show that with probability tending to one our method can correctly determine the unknown number of breaks and the estimated break dates are sufficiently close to the true break dates. We obtain esti...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Computational and Graphical Statistics
سال: 2013
ISSN: 1061-8600,1537-2715
DOI: 10.1080/10618600.2012.707454