English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

In many semiparametric models that are parameterized by two types ofparameters---a Euclidean parameter of interest and an infinite-dimensionalnuisance parameter---the two parameters are bundled together, that is, thenuisance parameter is an unknown function that contains the parameter ofinterest as part of its argument. For example, in a linear regression model forcensored survival data, the unspecified error distribution function involvesthe regression coefficients. Motivated by developing an efficient estimatingmethod for the regression parameters, we propose a general sieve M-theorem forbundled parameters and apply the theorem to deriving the asymptotic theory forthe sieve maximum likelihood estimation in the linear regression model forcensored survival data. The numerical implementation of the proposed estimatingmethod can be achieved through the conventional gradient-based searchalgorithms such as the Newton--Raphson algorithm. We show that the proposedestimator is consistent and asymptotically normal and achieves thesemiparametric efficiency bound. Simulation studies demonstrate that theproposed method performs well in practical settings and yields more efficientestimates than existing estimating equation based methods. Illustration with areal data example is also provided.

A sieve M-theorem for bundled parameters in semiparametric models, with application to the efficient estimation in a linear model for censored data