Abstracts

Estimating the number of kinds using proxy sampling effort data

Simon Wilson

Data in a number of kinds problem are usually modelled as one of 2 types:

1. Complete sampling data, where the number of individuals sampled and their kind are observed;
2. Temporal data that describe when new kinds were observed but lack information on numbers sampled.

Very different models and estimation methods apply to these types.

Inconveniently, one of the most important applications of this problem, estimation of the number of species, falls into neither type; complete sampling information is lacking, but there is some proxy information on it, typically some measure of effort like estimates of numbers of individuals that can be sampled. In this talk we propose a hybrid model that allows such proxy information to be incorporated. The advantage of this approach are that it produces a framework around which the uncertainties in the number of species can be modelled and quantified, something that is certainly needed for a question where estimates vary by at least an order of magnitude and estimates of uncertainty are often lacking.

Inference is implemented via ABC and applied to 2 large databases: Catalogue of Life and World Register of Marine Species. Prior sensitivity and approaches to speeding up the implementation are discussed.

Spatio-temporal Dirichlet regression models 

Mario Figueira, David Conesa and Antonio López-Quílez

Compositional Data Analysis (CoDa) has experienced a surge in popularity in recent years due to its applicability in various fields, e.g. land use management, demography, or the analysis of microbiome composition. This analytical approach deals with data comprising values from distinct categories that collectively sum up to a constant. Fitting multivariate models within the CoDa framework presents challenges, particularly when incorporating structured random effects like temporal or spatial variations.

In this work we present two different approaches for analysing compositional data, along with the extension implemented in the dirinla package. Both approaches allow us to construct highly versatile models for analysing spatial and spatiotemporal structure. Thanks to its implementation in INLA, we can obtain results at a low computational cost, enabling us to carry out step-wise regression processes. Thus, we can select the model that best fits a given set of explanatory variables under different basic structures for the spatial and temporal components. Furthermore, downscaling models can be implemented to avoid effects stemming from highly irregular area structures.As an example of the use of this methodology, we present an analysis of data provided in an European project of Land Use Management for Sustainability. Data have been categorised into five distinct types: cropland, grassland, forest, urban, and other natural lands. The spatial structure of the data is at the NUTS3 level, with yearly data available from 2008 to 2017.

Approaching Factorial Designs under a Model uncertainty perspective

G. García-Donato and V. Peña

Factorial experiments are designed to explain the variability of a variable of interest with the levels of p factors and their interactions. We focus on the case of two-level factors. The statistical models involved in these experiments become highly parameterised as p increases, resulting in the need to collect a large sample size n if we want to estimate all the parameters. An ingenious, cheaper alternative is fractional factorial experiments, which are designed to provide comparable properties with a reduced number of observations. 

In this talk, we consider factorial models from the perspective of Bayesian model uncertainty. Our argument is that the existence of a real contribution of the factors to the response is unknown and should therefore the added uncertainty should be explictily treated. A major challenge we face is the construction of a prior distribution over the considered models (combination of active factors and/or interactions). We propose solutions aimed at controlling the effect of multiplicity and preserving the constraints associated with the different rank of the hierarchy between main effects and interactions. We show that this new perspective leads to innovative solutions for the analysis of factorial experiments.

Heterogeneous influence of banking risk on cost efficiency: a hierarchical bayesian analysis

Pilar Gargallo, Jordi Moreno and Manuel Salvador

During the past two decades, European banking sectors have undergone a profound process of integration and financial globalization. In this highly competitive context, and following the global financial crisis (2007-2009), concerns have arisen regarding the risk assumed by banking systems and its impact on their performance. Despite the regulations implemented, such as those proposed in Basel III aimed at enhancing the stability and security of the global banking system, recent studies have revealed that these measures have not achieved their objectives for European banks.

Understanding the influence of risk on banking outcomes is essential, particularly given the high-competition environment that incentivizes risky behaviours. While the literature addressing this issue is abundant, it remains unclear to what extent and how different types of banking risk affect their performance. Moreover, previous studies examining this relationship have often considered a homogeneous effect of risk on banking efficiency, without accounting for the possibility that this risk assumed by banks could have heterogeneous effects depending on the bank's own characteristics and the conditions of the operating environment.

To shed light on this matter, we analysed a sample of commercial banks operating in European Union countries between 2004 and 2020. For the analysis, we adopted an approach based on the modified value-added method, where deposits are simultaneously treated as inputs and outputs since they involve value creation. We proposed a Bayesian stochastic frontier model applied to an unbalanced panel of data to estimate the evolution of cost efficiency for each bank. This efficiency is assumed to depend on the bank's size and the levels of risk it assumes. The model is hierarchical and supposes, in a second stage, that the influence exerted by these characteristics depends, in turn, on the environment (country, industry) in which the bank operates. Using Bayesian tools, we compared various hypotheses regarding the degree of homogeneity of this influence concerning the country and industry. The economic and financial data were obtained from Orbis Bank Focus (2004-2016) and Orbis (2017-2020), while data on industry and country were collected from the World Bank's World Development Indicators database.

A Bayesian approach to modeling imperfect diagnostic tests

R. Susi, C.M. Rodríguez-Leal, E. Dacal, J. Amador, C. Nieto.

In this work, a Bayesian approach is presented to estimate the validity,measured by sensitivity and specificity, of one or more imperfect diagnostic tests, initially considering dichotomous tests. Additionally, when working in the absence of a gold standard, Bayesian inference proves useful for determining illness prevalence. The estimation of diagnostic test validity and illness prevalence is considered in various scenarios, including situations with at least two imperfect diagnostic tests. These tests can be either independent or correlated, given the illness status, across one or more populations.

Furthermore, beyond Bayesian inference for dichotomous imperfect diagnostic tests, we delve into a more complex scenario involving a continuous variable or biomarker used for diagnosis. Specifically, we explore the receiver operating characteristic (ROC) curve to evaluate the discriminatory ability of the biomarker. Bayesian estimation of the ROC curve for two diagnostic tests is then presented, considering both the binormal and bigamma models.

The theoretical concepts discussed in this presentation are illustrated using two real-world cases:

(1) Estimation of the validity of two uncorrelated dichotomous imperfect diagnostic tests and the prevalence of strongyloidiasis.

(2) Estimation of the ROC curve for two biomarkers, assuming gamma-distributed data, in the context of pulmonary thromboembolism disease.