Proposal Description

Organizer: Jesus Lopez-Fidalgo (Instituto de Ciencia de los Datos e Inteligencia Artificial, Universidad de Navarra, Spain)
Chair: Jialiang Li (Department of Statistics and Data Science, National University of Singapore)

1. Presentation Title: An innovative proposal for a joint multi-response Kriging modelling and optimization with an application to freight trains
Authors: Nedka Dechkova Nikiforova1, Rossella Berni1, Luciano Cantone2
1Department of Statistics Computer Science Applications “G. Parenti”, University of Florence, Italy
2Department of Engineering for Enterprise “Mario Lucertini”, University of Rome “Tor Vergata”, Rome, Italy

Abstract: In this talk, we deal with a proposal for a joint multi-response Kriging modelling and optimization, by specifically considering Universal Kriging models involving a non-constant trend and anisotropic covariance functions. More specifically, we propose a joint Kriging modelling for a multiple response situation, aiming also to study the association among the responses.

2. Presentation Title: A design optimality criterion based on the AUC for classification
Authors: Carlos de la Calle, Jesus Lopez-Fidalgo, Pablo Urruchi
Speaker: Jesus Lopez-Fidalgo (Instituto de Ciencia de los Datos e Inteligencia Artificial, Universidad de Navarra, Spain)

Abstract: An appropriate estimation method for classification and then an optimality criterion for searching for an optimal subsampling procedure are proposed. An example for classifying tweets coming either from humans or from bots illustrates the results

3. Nature-inspired metaheuristics for designing innovative drug studies
Speaker: Weng Kee Wong, PhD (Department of Biostatistics, UCLA)

Abstract: Review of some exemplary nature-inspired metaheuristic algorithms: (i) how they can be applied to develop adaptive designs that extend Simon Two-Stage Adaptive Designs and (ii) how to design optimal strategies for optimally recruiting in a global clinical trial so that the target enrollment is met in the specified time with high probability at minimum cost.

4. Presentation Title: Optimal Exact Designs for Small Studies in Toxicology with Applications to Hormesis via a Metaheuristic Algorithm
Speaker: Ray-Bing Chen, PhD (Department of Statistics & Institute of Data Science, National Cheng Kung University, Tainan, Taiwan)

Abstract: There are theory-based methods for constructing model-based optimal designs when the sample size is large. The problem becomes challenging when the sample size is small. The theory may no longer apply and even if it did, the optimal design
may not be implementable. We provide examples and also show that a simple
rounding procedure of the weights from an optimal approximate design to an
optimal exact design can produce the wrong optimal exact design. To solve this
longstanding, serious and practical problem, we propose a state-of-the-art nature-
inspired metaheuristic algorithm to find efficient designs for an experiment with a
small sample size. As an application, we use the algorithm to find an optimal design
for a toxicology experiment to detect existence of hormesis in a dose response
study, and an optimal design to estimate the hormesis threshold. Being a
metaheuristic algorithm, it can be used to find different types of optimal designs for
various statistical models. We demonstrate its flexibility by finding locally D-optimal
designs for estimating model parameters in logistic models for small experiments,
along with user-friendly codes to produce all designs in the paper.