Characterization of  c ‐,  L ‐ and  ϕ   k  ‐optimal designs for a class of non‐linear multiple‐regression models | Zendy

Schmidt Dennis | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Premium

Characterization of c ‐, L ‐ and ϕ k ‐optimal designs for a class of non‐linear multiple‐regression models

Author(s) -

Schmidt Dennis

Publication year - 2019

Publication title -

journal of the royal statistical society: series b (statistical methodology)

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 6.523

H-Index - 137

eISSN - 1467-9868

pISSN - 1369-7412

DOI - 10.1111/rssb.12292

Subject(s) - covariate , mathematics , censoring (clinical trials) , poisson regression , negative binomial distribution , generalized linear model , optimal design , poisson distribution , regression analysis , linear model , linear regression , class (philosophy) , statistics , characterization (materials science) , proper linear model , polynomial regression , computer science , population , artificial intelligence , materials science , nanotechnology , demography , sociology

Summary Optimal designs for multiple‐regression models are determined. We consider a general class of non‐linear models including proportional hazards models with different censoring schemes, the Poisson and the negative binomial model. For these models we provide a complete characterization of c ‐optimal designs for all vectors c in the case of a single covariate. For multiple regression with an arbitrary number of covariates, c ‐optimal designs for certain vectors c are derived analytically. Using some general results on the structure of optimal designs for multiple regression, we determine L ‐ and ϕ k ‐optimal designs for models with an arbitrary number of covariates.

This content is not available in your region!

Continue researching here.

Having issues? You can contact us here

Accelerating Research