The clustering of their letter shapes is performed based on the pairwise distances between their topological signatures.The article presents a new probability distribution, created by compounding the Poisson distribution with the weighted exponential distribution. Important mathematical and...

We study long-term incentives for polluting and regulated firms to invest in advanced abatement technologies, when some new technology is available but an even better technology will be expected in the future. Firms can invest only once. We find that depending on the adoption fixed costs all...

We propose a dynamic factor model for the analysis of multivariate time series count data. Our model allows for idiosyncratic as well as common serially correlated latent factors in order to account for potentially complex dynamic interdependence between series of counts. The model is estimated...

Multivariate random sums appear in many scientific fields, most notably in actuarial science, where they model both the number of claims and their sizes. Unfortunately, they pose severe inferential problems. For example, their density function is analytically intractable, in the general case,...

We develop a new class of distributions, namely, the odd power generalized Weibull-G powerseries (OPGW-GPS) class of distributions. We present some special classes of the proposeddistribution. Structural properties, have also been derived. We conducted a simulation studyto evaluate the...

In this paper, a new Poisson area-biased Ailamujia distribution has been formulated to analyse count data. It was created by combining two distributions: the Poisson and area-biased Ailamujia distributions, using the compounding technique. Several distributional properties of the formulated...

We identify structural vector autoregressions using narrative sign restrictions. Narrative sign restrictions constrain the structural shocks and the historical decomposition around key historical events, ensuring that they agree with the established narrative account of these episodes. Using...

This work presents a Bayesian approach for inferring the capacity factor of an offshore wind farm. The application of the proposed methodology is illustrated through a case study of a wind power offshore project located on the northern coast of the Rio de Janeiro State.

In this work, we consider modeling the past volatilities through an asymmetric generalised autoregressive conditional heteroskedasticity (Garch) model with heavy tailed sampling distributions. In particular, we consider the Student-t model with unknown degrees of freedom and indicate how it may...