✦ LIBER ✦

A note on D-optimal designs for a logistic regression model

✍ Scribed by Paola Sebastiani; Raffaella Settimi

Publisher: Elsevier Science
Year: 1997
Tongue: English
Weight: 613 KB
Volume: 59
Category: Article
ISSN: 0378-3758
DOI: 10.1016/s0378-3758(96)00111-5

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

081036 (M31) A note on D-optimal designs

081036 (M31) A note on D-optimal designs for a logistic regression model: Sebastiani P., Settimi R., Department of Actuarial Science and Statistics, City University, London, City University, Statistical Research Paper No. 4, 1996

📂 Article 📅 1996 🏛 Elsevier Science 🌐 English ⚖ 88 KB

D-Optimal designs for a multivariate reg

D-Optimal designs for a multivariate regression model

✍ Olaf Krafft; Martin Schaefer 📂 Article 📅 1992 🏛 Elsevier Science 🌐 English ⚖ 413 KB

Exact D-optimal designs for weighted pol

Exact D-optimal designs for weighted polynomial regression model

✍ Ray-Bing Chen; Mong-Na Lo Huang 📂 Article 📅 2000 🏛 Elsevier Science 🌐 English ⚖ 137 KB

In this work, the exact D-optimal designs for weighted polynomial regression are investigated. In Ga ke (1987, J. Statist. Planning Inference 15, 189 -204) a su cient condition has been given that Salaeveski Ä i's type of result about the exact D-optimal designs holds when sample size n is large eno

A note on robust designs for polynomial

A note on robust designs for polynomial regression

✍ Holger Dette 📂 Article 📅 1991 🏛 Elsevier Science 🌐 English ⚖ 557 KB

An algorithm for A-optimal regression de

An algorithm for A-optimal regression designs

✍ Kim-Hung Li; N.N. Chan 📂 Article 📅 1989 🏛 Elsevier Science 🌐 English ⚖ 466 KB

Bayesian D-optimal designs for exponenti

Bayesian D-optimal designs for exponential regression models

✍ Holger Dette; H.-M. Neugebauer 📂 Article 📅 1997 🏛 Elsevier Science 🌐 English ⚖ 875 KB

We consider the Bayesian D-optimal design problem for exponential growth models with one. two or three parameters. For the one-parameter model conditions on the shape of the density of the prior distribution and on the range of its support are given guaranteeing that a one-point design is also Bayes