# A note on locally optimal designs for generalized linear models with restricted support

## TL;DR

This paper explores methods to derive locally optimal experimental designs for generalized linear models, especially when prior parameter knowledge is limited, by relating models with and without intercepts.

## Contribution

It introduces assumptions that connect optimal designs between models with and without intercepts, facilitating design derivation without full prior knowledge.

## Key findings

- Derived locally optimal designs for models with and without intercepts.
- Applied methods to Poisson and logistic models.
- Extended approaches to nonlinear models.

## Abstract

Optimal designs for generalized linear models require a prior knowledge of the regression parameters. At certain values of the parameters we propose particular assumptions which allow to derive a locally optimal design for a model without intercept from a locally optimal design for the corresponding model with intercept and vice versa. Applications to Poisson and logistic models and Extensions to nonlinear models are provided.