Maximum likelihood estimation of higher-order integer-valued autoregressive processes

In this article, we extend the earlier work of Freeland and McCabe [Journal of time Series Analysis (2004) Vol. 25, pp. 701–722] and develop a general framework for maximum likelihood (ML) analysis of higher-order integer-valued autoregressive processes. Our exposition includes the case where the innovation sequence has a Poisson distribution and the thinning is binomial. A recursive representation of the transition probability of the model is proposed. Based on this transition probability, we derive expressions for the score function and the Fisher information matrix, which form the basis for ML estimation and inference. Similar to the results in Freeland and McCabe (2004), we show that the score function and the Fisher information matrix can be neatly represented as conditional expectations. Using the INAR(2) speci?cation with binomial thinning and Poisson innovations, we examine both the asymptotic e?ciency and ?nite sample properties of the ML estimator in relation to the widely used conditional least
squares (CLS) and Yule–Walker (YW) estimators. We conclude that, if the Poisson assumption can be justi?ed, there are substantial gains to be had from using ML especially when the thinning parameters are large.

Inference in Credal Networks Through Integer Programming

A credal network associates a directed acyclic graph with a collection of sets of probability measures; it offers a compact representation for sets of multivariate distributions. In this paper we present a new algorithm for inference in credal networks based on an integer programming reformulation. We are concerned with computation of lower/upper probabilities for a variable in a given credal network. Experiments reported in this paper indicate that this new algorithm has better performance than existing ones for some important classes of networks.

'A Job in Politics is not for Woman': Analysing Barriers to Women's Political Representation in CEE

This article discusses women’s political representation in Central and Eastern Europe in the fifteen years after the fall of the Berlin Wall and the adoption of liberal democratic political systems in the region. It highlights the deepseated gender stereotypes that define women primarily as wives and mothers, with electoral politics seen as an appropriate activity for men, but less so for women. The article explores the ways in which conservative attitudes on gender roles hinders the supply of, and demand for, women in the politics of Central and Eastern Europe. It also discusses the manner in which the internalisation of traditional gender norms affects women’s parliamentary behaviour, as few champion women’s rights in the legislatures of the region. The article also finds that links between women MPs and women’s organisations are weak and fragmented, making coalition-building around agendas for women’s rights problematic.

Quantum teleportation via mixed two-mode squeezed states in the coherent-state representation

Quantum teleportation for continuous variables is generally described in phase space by using the Wigner functions. We study quantum teleportation via a mixed two-mode squeezed state in Hilbert-Schmidt space by using the coherent-state representation and operators. This shows directly how the teleported state is related to the original state.

Time operators and shift representation of dynamical systems

We introduce and characterise time operators for unilateral shifts and exact endomorphisms. The associated shift representation of evolution is related to the spectral representation by a generalized Fourier transform. We illustrate the results for a simple exact system, namely the Renyi map.

A novel continuous forward algorithm for RBF neural modelling

A continuous forward algorithm (CFA) is proposed for nonlinear modelling and identification using radial basis function (RBF) neural networks. The problem considered here is simultaneous network construction and parameter optimization, well-known to be a mixed integer hard one. The proposed algorithm performs these two tasks within an integrated analytic framework, and offers two important advantages. First, the model performance can be significantly improved through continuous parameter optimization. Secondly, the neural representation can be built without generating and storing all candidate regressors, leading to significantly reduced memory usage and computational complexity. Computational complexity analysis and simulation results confirm the effectiveness.

Prospects for women's legislative representation in postsocialist Europe: the views of female politicians

Research on women’s political representation in post-socialist Europe has highlighted the role of cultural and political factors in obstructing women’s access to legislative power, such as the prevalence of traditional gender stereotypes, electoral systems, and the absence of a feminist movement. Yet the role of women political elites in enhancing or hindering women’s access to political power in the region has so far remained uncharted. This article seeks to fill some of the existing gaps in this literature by examining the views of women politicians with regard to women’s political underrepresentation and their assessments of strategies for redressing this imbalance. Findings from the analysis reveal that although women politicians recognize gender inequalities in representation as a problem requiring intervention, how the problem is perceived, and the preferred measures to deal with it, is largely shaped by the social and cultural context in which these actors are embedded.