ENBIS-23 Valencia Conference

Session

INVITED South American

12 Sept 2023, 11:05

2.11

Conveners

INVITED South American

• Geoff Vining (Virginia Tech Statistics Department)

132. A Non-Linear Mixed Model Approach for Detecting Outlying Profiles

Valeria Quevedo (Universidad de Piura)

12/09/2023, 11:05

Quality

In parametric non-linear profile modeling, it is crucial to map the impact of model parameters to a single metric. According to the profile monitoring literature, using multivariate $T^2$ statistic to monitor the stability of the parameters simultaneously is a common approach. However, this approach only focuses on the estimated parameters of the non-linear model and treats them as separate...

27. Design of Experiments (DoE)-Based Approach to Better Capture Uncertainty in Future Climate Projections

Carla Vivacqua (Universidade Federal do Rio Grande do Norte)

12/09/2023, 11:35

Other/special session/invited session

We are living in the big data era. The amount of data created is enormous and we are still planning to generate even more data. We should stop and ask ourselves: Are we extracting all the information from the available data? Which data do we really need? The next frontier of climate modelling is not in producing more data, but in producing more information. The objective of this talk is to...

69. Statistical Model for Wildfires and the Effect of the Climate Change

András Zempléni (Eötvös Loránd University, Budapest), Kristóf Halász (Eötvös Loránd University)

12/09/2023, 12:05

Environment

We have created a wildfire-probability estimating system, based on publicly available data (historic wildfires, satellite images, weather data, maps). The mathematical model is rather simple: kriging, logistic regression and the bootstrap are its main tools, but the computational complexity is substantial, and the data analysis is challenging.

It has a wide range of applications. Here we...

2. The Best Model May Not Be Good Enough

Prof. André Luis Santos de pinho (Universidade Federal do Rio Grande do Norte)

Other/special session/invited session

It is common to use model performance measures, such as AIC and BIC, to evaluate how well the model fits the data. This work illustrates that we need to go beyond these measures to assess a model's capability to represent the data. There are several ways to achieve that. Here we focus on a graphical approach using the probability integral transform (PIT) histogram. We present a situation in...