arXiv subjects

Luzia A. Trinca

Publications and source records attributed to Luzia A. Trinca.

4 recordsLinked to original sources

arXiv2025

Pseudo-Bayesian Optimal Designs for Fitting Fractional Polynomial Response Surface Models

Fractional polynomial models are potentially useful for response surfaces investigations. With the availability of routines for fitting nonlinear models in statistical packages they are increasingly being used. However, as in all experiments the design should be chosen such that the model parameters are estimated as efficiently as possible. The design choice for such models involves the known nonlinear models' design difficulties but \cite{gilmour_trinca_2012b} proposed a methodology capable of producing exact designs that makes use of the computing facilities available today. In this paper, we use this methodology to find Bayesian optimal exact designs for several fractional polynomial models. The optimum designs are compared to various standard designs in response surface problems.

stat.ME↗

arXiv2024

MOODE: An R Package for Multi-Objective Optimal Design of Experiments

We describe the R package MOODE and demonstrate its use to find multi-objective optimal experimental designs. Multi-Objective Optimal Design of Experiments (MOODE) targets the experimental objectives directly, ensuring that the full set of research questions is answered as economically as possible. In particular, individual criteria aimed at optimizing inference are combined with lack-of-fit and MSE-based components in compound optimality criteria to target multiple and competing objectives reflecting the priorities and aims of the experimentation. The package implements either a point exchange or coordinate exchange algorithm as appropriate to find nearly optimal designs. We demonstrate the functionality of MOODE through the application of the methodology to two case studies of varying complexity.

stat.CO↗

arXiv2024

Response Surface Designs for Crossed and Nested Multi-Stratum Structures

Response surface designs are usually described as being run under complete randomization of the treatment combinations to the experimental units. In practice, however, it is often necessary or beneficial to run them under some kind of restriction to the randomization, leading to multi-stratum designs. In particular, some factors are often hard to set, so they cannot have their levels reset for each experimental unit. This paper presents a general solution to designing response surface experiments in any multi-stratum structure made up of crossing and/or nesting of unit factors. A stratum-by-stratum approach to constructing designs using compound optimal design criteria is used and illustrated. It is shown that good designs can be found even for large experiments in complex structures.

stat.ME↗

arXiv2019

Prediction properties of optimum response surface designs

Prediction capability is considered an important issue in response surface methodology. Following the line of argument that a design should have several desirable properties we have extended an existing compound design criterion to include prediction properties. Prediction of responses and of differences in response are considered. Point and interval predictions are allowed for. Extensions of existing graphical tools for inspecting prediction performances of the designs in the whole region of experimentation are also introduced. The methods are illustrated with two examples.

stat.ME↗