Books on probability ideas are reviewed

In this paper, semiparametric methods are applied to estimate multivariate volatility functions, using a residual approach as in [J. Fan and Q. Yao, Efficient estimation of conditional variance functions in stochastic regression, Biometrika 85 (1998), pp. 645–660; F.A. Ziegelmann, Nonparametric estimation of volatility functions: The local exponential estimator, Econometric Theory 18 (2002), pp. 985–991; F.A. Ziegelmann, A local linear least-absolute-deviations estimator of volatility, Comm. Statist. Simulation Comput. 37 (2008), pp. 1543–1564], among others. Our main goal here is two-fold: (1) describe and implement a number of semiparametric models, such as additive, single-index and (adaptive) functional-coefficient, in volatility estimation, all motivated as alternatives to deal with the curse of dimensionality present in fully nonparametric models; and (2) propose the use of a variation of the traditional cross-validation method to deal with model choice in the class of adaptive functional-coefficient models, choosing simultaneously the bandwidth, the number of covariates in the model and also the single-index smoothing variable. The modified cross-validation algorithm is able to tackle the computational burden caused by the model complexity, providing an important tool in semiparametric volatility estimation. We briefly discuss model identifiability when estimating volatility as well as nonnegativity of the resulting estimators. Furthermore, Monte Carlo simulations for several underlying generating models are implemented and applications to real data are provided.     

Complete moment convergence and Lr convergence for asymptotically almost negatively associated random variables

In this paper, we investigate the complete moment convergence and L_r convergence for maximal partial sums of asymptotically almost negatively associated random variables under some general conditions. The results obtained in the paper generalize some corresponding ones for negatively associated random variables.     

The almost sure convergence rate of the estimator of optimized certainty equivalent risk measure under α-mixing sequences

Conditional value-at-risk (CVaR) model is a kind of financial risk measure that is extensively supported and accepted by international financial community. Its optimized form can be regarded as an optimized certainty equivalent (OCE) risk measurement. In this paper, we mainly discuss and analyze the strong laws of large numbers and the convergence rate of OCE's estimator under α-mixing sequences. The result shows that the almost sure convergence rate of CVaR estimator is given by the results of OCE estimator. Its convergence rate is inversely proportional to the square root of the sample size under certain conditions. Its effectiveness is verified by simulation experiments for two classical α-mixing sequences.     

THE RELEVANCE OF THE CONCEPT OF DISSOCIATION IN CHILD PROTECTION

This paper explores one aspect of unresolved trauma, namely dissociation. Its relevance to child protection is discussed with reference to two aspects: emotional neglect and choice of partner. There is an exploration of the implications for both assessment and treatment. The central place of shame in relation to dissociation is also explored. It is argued that dissociation is an important concept in child protection assessments and that social work assessments should be alert to this.     

Empirical Bayes estimation in continuous one-parameter exponential families under associated samples

In this paper, we study the empirical Bayes (EB) estimation in continuous one-parameter exponential families under negatively associated (NA) samples and positively associated (PA) samples. Under certain regularity conditions, it is shown that the convergence rates of proposed EB estimators under NA or PA samples are the same as those of EB estimators under independent observations, which significantly improve the existing results in EB estimation under associated samples.     

Complete convergence and the strong laws of large numbers for pairwise NQD random variables

The authors study the strong convergence for sequences of pairwise negatively quadrant dependent (NQD) random variables under some wide conditions, and present some new theorems on the complete convergence and the strong laws of large numbers. The obtained results extend and improve some theorems in existing literature.     

Strong law of large numbers and complete convergence for non-identically distributed WOD random variables

In this paper, we establish the strong law of large numbers and complete convergence for non-identically distributed WOD random variables. We derive some new inequalities of Fuk–Nagaev type for the sums of non-identically distributed WD random variables. All these results further extend and refine previous ones.     

20 years of Bologna - a story of success,a story of failure

The Bologna Process is a unique harmonisation process taking place outside the policy-making framework of the European Union. It aims at enhancing the comparability and compatibility of higher education structures and degrees across Europe, as well as to institutionalise quality assurance mechanisms. The aim of this article is to provide a condensed, up-to-date overview of the Bologna Process with regard to structural characteristics, before embedding it into a discussion on processes on voluntary policy convergence and to which extent we should be able to find this kind of policy harmonisation in the realm of the Bologna Process. Related to this are questions why this, in principle, completely voluntary process of policy harmonisation, has appealed to so many countries and why they might or might not feel committed to the implementation of its policies and tools.     

吉伯德-萨特思韦特防策略不可能性定理的逻辑初探

自20世纪70年代吉伯德-萨特思韦特防策略不可能性定理确立后，引起了数学、经济学、计算机科学和哲学等诸多领域中学者的广泛关注。然而，在国内外文献中，对吉伯德-萨特思韦特防策略不可能性定理的逻辑研究还比较少。基于此，借助公理化的方法探讨吉伯德-萨特思韦特防策略不可能性定理，并形式化地给出定理的内容和证明，力图把精细的逻辑推理应用于复杂的过程分析中，使防策略投票这一过程形式化、清晰化，以期能指导人们进行合理、有效的决策活动。