ENBIS-18 in Nancy

2 – 25 September 2018; Ecoles des Mines, Nancy (France) Abstract submission: 20 December 2017 – 4 June 2018

Optimal Bayesian Design via MCMC Simulations for a Soldering Reliability Study

5 September 2018, 10:00 – 10:30

Abstract

Submitted by
Rossella Berni
Authors
Rossella Berni (Department of Statistics, Informatics, Applications -University of Florence)
Abstract
Optimal design criteria has recently received growing attention,both at theoretical and computational levels, in part following the increase of computational power. Since the 70s, there is a long history of seminal papers in literature on D and T-optimality, both to estimate model parameters and also to discriminate among models. Furthermore, the building of optimal designs has been improved in a Bayesian framework, by introducing prior distributions on models and parameters and by selecting the optimal design according to the definition of an utility function and its maximization, also by considering a decision analysis framework.

Notwithstanding the generality achieved, in actual applications further flexibility is often needed, for example by defining a utility function in which the cost of each observation depends on the value taken by the independent variable. Moreover, the relevance for costs may be also evaluated by specific weights, which take environmental conditions and technological information into account.

In this talk, we consider the improving of building optimal designs in the technological field by applying Markov Chain Monte Carlo simulations, and by evaluating: i) an hierarchical structure of the observed data; ii) an utility function including costs and weights; iii) modelling discrimination.

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