Bayesian variable selection in quantile regression
نویسندگان
چکیده
منابع مشابه
Bayesian variable selection in quantile regression
In many applications, interest focuses on assessing relationships between predictors and the quantiles of the distribution of a continuous response. For example, in epidemiology studies, cutoffs to define premature delivery have been based on the 10th percentile of the distribution for gestational age at delivery. Using quantile regression, one can assess how this percentile varies with predict...
متن کاملVariable Selection in Quantile Regression
After its inception in Koenker and Bassett (1978), quantile regression has become an important and widely used technique to study the whole conditional distribution of a response variable and grown into an important tool of applied statistics over the last three decades. In this work, we focus on the variable selection aspect of penalized quantile regression. Under some mild conditions, we demo...
متن کاملBayesian Parameter Estimation and Variable Selection for Quantile Regression
The principal goal of this work is to provide efficient algorithms for implementing the Bayesian approach to quantile regression. There are two major obstacles to overcome in order to achieve this. Firstly, it is necessary to specify a suitable likelihood given that the frequentist approach generally avoids such specifications. Secondly, sampling methods are usually required as analytical expre...
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 کاملVariable Selection in Single Index Quantile Regression for Heteroscedastic Data
Quantile regression (QR) has become a popular method of data analysis, especially when the error term is heteroscedastic, due to its relevance in many scientific studies. The ubiquity of high dimensional data has led to a number of variable selection methods for linear/nonlinear QR models and, recently, for the single index quantile regression (SIQR) model. We propose a new algorithm for simult...
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: Statistics and Its Interface
سال: 2013
ISSN: 1938-7989,1938-7997
DOI: 10.4310/sii.2013.v6.n2.a9