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The purpose of the book is to put these new techniques at the disposal of researchers coming from different horizons, to assess the state of the art, to identify the capability of these new algorithms, to evidence the contribution of these methods to Economics and Management Sciences. It is a privileged place to expose the know-how and to discuss new developments and problems encountered in the researches. The contributions in this book bring new confirmations of the interest of connectionist approaches for researchers in Economics and Management Sciences. The first part is dedicated to theoretical advances; the second part presents a wide range of applications. All papers contain interesting results on each subject, which would have been very difficult to show with classical techniques but which has been proven by using these connectionist non linear methods. They reflect the great diversity of connectionist approaches of which we know the reader will benefit for his(her) own research. If this study enlarges the range of analysis tools for researchers in Economics and Management we will have reached our goal of sharing our interest in these new and fascinating connectionist methods

International audience ; Bounded time series and time series of continuous proportions are often encountered in statistical modeling. Usually, they are addressed either by a logistic transformation of the data, or by specific probability distributions, such as Beta distribution. Nevertheless, these approaches may become quite tricky when the data show an over-dispersion in 0 and/or 1. In these cases, the zero-and/or-one Beta-inflated distributions, ZOIB, are preferred. This manuscript combines ZOIB distributions with hidden-Markov models and proposes a flexible model, able to capture several regimes controlling the behavior of a time series of continuous proportions. For illustrating the practical interest of the proposed model, several examples on simulated data are given, as well as a case study on historical data, involving the military logistics of the Duchy of Savoy during the XVIth and the XVIIth centuries.

International audience ; Bounded time series and time series of continuous proportions are often encountered in statistical modeling. Usually, they are addressed either by a logistic transformation of the data, or by specific probability distributions, such as Beta distribution. Nevertheless, these approaches may become quite tricky when the data show an over-dispersion in 0 and/or 1. In these cases, the zero-and/or-one Beta-inflated distributions, ZOIB, are preferred. This manuscript combines ZOIB distributions with hidden-Markov models and proposes a flexible model, able to capture several regimes controlling the behavior of a time series of continuous proportions. For illustrating the practical interest of the proposed model, several examples on simulated data are given, as well as a case study on historical data, involving the military logistics of the Duchy of Savoy during the XVIth and the XVIIth centuries.