Nonparametric quantile regression for twice censored data | Zendy

Stanislav Volgushev | Zendy; Holger Dette | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Nonparametric quantile regression for twice censored data

Author(s) -

Stanislav Volgushev,

Holger Dette

Publication year - 2013

Publication title -

bernoulli

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.814

H-Index - 72

eISSN - 1573-9759

pISSN - 1350-7265

DOI - 10.3150/12-bej462

Subject(s) - mathematics , quantile , quantile regression , estimator , nonparametric statistics , consistency (knowledge bases) , monotone polygon , weak convergence , statistics , asymptotic distribution , quantile function , nonparametric regression , strong consistency , conditional probability distribution , kaplan–meier estimator , econometrics , cumulative distribution function , discrete mathematics , probability density function , computer science , geometry , computer security , asset (computer security)

We consider the problem of nonparametric quantile regression for twice censored data. Two new estimates are presented, which are constructed by applying concepts of monotone rearrangements to estimates of the conditional distribution function. The proposed methods avoid the problem of crossing quantile curves. Weak uniform consistency and weak convergence is established for both estimates and their nite sample properties are investigated by means of a simulation study. As a by-product, we obtain a new result regarding the weak convergence of the Beran estimator for right censored data on the maximal possible domain, which is of its own interest.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research