Open Access
Open access
Taylor & Francis
Statistical Theory and Related Fields
,  pages 1-14

Locally R-optimal designs for a class of nonlinear multiple regression models

Publication type: Journal Article
Publication date: 2022-12-12
Taylor & Francis
scimago Q3
wos Q2
SJR: 0.319
CiteScore: 1.2
Impact factor: 1.3
ISSN: 24754269, 24754277
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He L., Yue R. Locally R-optimal designs for a class of nonlinear multiple regression models // Statistical Theory and Related Fields. 2022. pp. 1-14.
GOST all authors (up to 50) Copy
He L., Yue R. Locally R-optimal designs for a class of nonlinear multiple regression models // Statistical Theory and Related Fields. 2022. pp. 1-14.
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RIS Copy
TY - JOUR
DO - 10.1080/24754269.2022.2153540
UR - https://doi.org/10.1080/24754269.2022.2153540
TI - Locally R-optimal designs for a class of nonlinear multiple regression models
T2 - Statistical Theory and Related Fields
AU - He, Lei
AU - Yue, Rong-Xian
PY - 2022
DA - 2022/12/12
PB - Taylor & Francis
SP - 1-14
SN - 2475-4269
SN - 2475-4277
ER -
BibTex
Cite this
BibTex (up to 50 authors) Copy
@article{2022_He,
author = {Lei He and Rong-Xian Yue},
title = {Locally R-optimal designs for a class of nonlinear multiple regression models},
journal = {Statistical Theory and Related Fields},
year = {2022},
publisher = {Taylor & Francis},
month = {dec},
url = {https://doi.org/10.1080/24754269.2022.2153540},
pages = {1--14},
doi = {10.1080/24754269.2022.2153540}
}