منابع مشابه
Quantile Uncorrelation and Instrumental Regressions
We introduce a notion of median uncorrelation that is a natural extension of mean (linear) uncorrelation. A scalar random variable Y is median uncorrelated with a k-dimensional random vector X if and only if the slope from an LAD regression of Y on X is zero. Using this simple definition, we characterize properties of median uncorrelated random variables, and introduce a notion of multivariate ...
متن کاملFunctional Data Analysis of Generalized Quantile Regressions
[To be revised.] Quantile and expectile regression are tail oriented conditional regression. They can be transformed as generalized quantile regression. Traditional generalized quantile regression focuses on a single curve. When more random curves are available, we can estimate the single curves jointly by using the information from all subjects instead of estimate it individually. To avoid too...
متن کاملImproving the computation of censored quantile regressions
Censored quantile regressions (CQR) are a valuable tool in economics and engineering. The computation of estimators is highly complex and the performance of standard methods is not satisfactory, in particular if a high degree of censoring is present. Due to an interpolation property the computation of CQR estimates corresponds to the solution of a large scale discrete optimization problem. This...
متن کاملMixtures of Self-Modelling Regressions
A shape invariant model for functions f1,...,fn specifies that each individual function fi can be related to a common shape function g through the relation fi(x)=aig(cix + di) + bi. We consider a flexible mixture model that allows multiple shape functions g1,...,gK, where each fi is a shape invariant transformation of one of those gk. We derive an MCMC algorithm for fitting the model using Baye...
متن کاملMixtures of Self-Modeling Regressions
A shape invariant model for functions f1, . . . , fn specifies that each individual function fi can be related to a common shape function g through the relation fi(x) = aig(cix + di) + bi. We consider a mixture model that allows multiple shape functions g1, . . . , gK , where each fi is a shape invariant transformation of one of those gk. We derive an MCMC algorithm for fitting the model using ...
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: Computational Statistics & Data Analysis
سال: 2016
ISSN: 0167-9473
DOI: 10.1016/j.csda.2014.04.014