A probabilistic estimation and prediction technique for dynamic continuous social science models: The evolution of the attitude of the Basque Country population towards ETA as a case study

Abstract

a b s t r a c tIn this paper, a computational technique to deal with uncertainty in dynamic continuous models in Social Sciences is presented. Considering data from surveys, the method consists of determining the probability distribution of the survey output and this allows to sample data and fit the model to the sampled data using a goodness-of-fit criterion based on the χ 2 -test. Taking the fitted parameters that were not rejected by the χ 2 -test, substituting them into the model and computing their outputs, 95% confidence intervals in each time instant capturing the uncertainty of the survey data (probabilistic estimation) is built. Using the same set of obtained model parameters, a prediction over the next few years with 95% confidence intervals (probabilistic prediction) is also provided. This technique is applied to a dynamic social model describing the evolution of the attitude of the Basque Country population towards the revolutionary organisation ETA.

Probabilistic prediction of outbreaks of meningococcus W-135 infections over the next few years in Spain

“…This technique, introduced in [15], consists of using information from surveys to assign probability distributions to the data. Then, we sample data values from these probability distributions and fit the model to the sampled data.…”

“…The increasing number of cases in countries such as Chile and UK and the high risk of mortality associated with this strain implies that the statistical modelling of this pandemic is a hot topic in epidemiology. For these reasons we will estimate the probability for an outbreak of meningococcal W-135 disease [15] in Spain using a probabilistic fitting technique for the genogroup competition SCS model discussed above. Our approach allows us to predict the maximum increase of the number of carriers expected from the seroepidemiological studies of 2011 and 2012 at the Reference Laboratory for Meningococci of the Carlos III Institute of Health in Spain [4].…”

Modeling the Dynamics of the Frequent Users of Electronic Commerce in Spain Using Optimization Techniques for Inverse Problems with Uncertainty

“…In both approaches, strong assumptions about model parameters are implicitly assumed. In [8], it is proposed a complete computational approach of the problem to obtain the probability distribution of the model parameters, the solution stochastic process and also to capture the data uncertainty via punctual (mean) and probabilistic (confidence intervals) information. To do that, the probability distributions of these data uncertainties need to be previously assigned.…”

Capturing the Data Uncertainty Change in the Cocaine Consumption in Spain Using an Epidemiologically Based Model

“…With this aim, we use the probabilistic fitting technique recently developed in [3,4] by some of the authors. With this technique, we expect to capture the data uncertainty (data coming from surveys), despite the change of trend, and provide an estimation to the probability distribution of the model parameters.…”

“…Also, the model is scaled in order to match data and model magnitudes. In Section 4, we recall and adapt the probabilistic fitting technique introduced in [3]. This technique is applied to the model in order to obtain both data estimation with uncertainty and an estimation of the probability distribution of the model parameters.…”