Conditional entropy,entropy density,and strong law of large numbers for generalized controlled tree-indexed Markov chains

In this paper, we first introduces a tree model without degree boundedness restriction namely generalized controlled tree T, which is an extension of some known tree models, such as homogeneous tree model, uniformly bounded degree tree model, controlled tree model, etc. Then some limit properties including strong law of large numbers for generalized controlled tree-indexed non homogeneous Markov chain are obtained. Finally, we establish some entropy density properties, monotonicity of conditional entropy, and entropy properties for generalized controlled tree-indexed Markov chains.     

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.     

Missing value imputation method for disaster decision-making using K nearest neighbor

Due to destructiveness of natural disasters, restriction of disaster scenarios and some human causes, missing data usually occur in disaster decision-making problems. In order to estimate missing values of alternatives, this paper focuses on imputing heterogeneous attribute values of disaster based on an improved K nearest neighbor imputation (KNNI) method. Firstly, some definitions of trapezoidal fuzzy numbers (TFNs) are introduced and three types of attributes (i.e. linguistic term sets, intervals and real numbers) are converted to TFNs. Then the correlated degree model is utilized to extract related attributes to form instances that will be used in K nearest neighbor algorithm, and a novel KNNI method merging with correlated degree model is presented. Finally, an illustrative example is given to verify the proposed method and to demonstrate its feasibility and effectiveness.     

Optimal cut-off for rare events and unbalanced misclassification costs

This paper develops a method for handling two-class classification problems with highly unbalanced class sizes and misclassification costs. When the class sizes are highly unbalanced and the minority class represents a rare event, conventional classification methods tend to strongly favour the majority class, resulting in very low detection of the minority class. A method is proposed to determine the optimal cut-off for asymmetric misclassification costs and for unbalanced class sizes. Monte Carlo simulations show that this proposal performs better than the method based on the notion of classification accuracy. Finally, the proposed method is applied to empirical data on Italian small and medium enterprises to classify them into default and non-default groups.     

Trading numbers vs. rights? Accounting for liberal and restrictive dynamics in the evolution of asylum and refugee policies

ABSTRACT

The recent evolution of asylum and refugee policies in developed countries has been characterised by two apparently contradictory dynamics. Efforts to limit the number of asylum applicants have coincided with the strengthening of rights for asylum seekers and refugees inside existing protection systems. The ‘numbers vs. rights’ model seeks to explain such counter-veiling trends as a trade-off, as the result of attempts to manage costs within given budget constraints. The model suggests that high numbers of migrants will tend to go hand in hand with attempts to restrict their rights, while low numbers will typically be associated with more rights. This paper provides a critical analysis of the model when applied to asylum and refugee policies and examines its explanatory purchase through the analysis of longitudinal data on visa and asylum statistics. We argue that while the model provides an interesting framework through which to analyse executive decisions in this field, it underestimates the opportunities and constraints provided by the institutional context in which policy choices are made. We argue that ‘over-time’ variation in the influence of non-majoritarian institutions (in Europe, increasingly those operating at the EU level) provide a more compelling account of the dynamics of asylum and refugee policies over time than the political economy predictions of a ‘number vs. rights’ trade-off.     

A strong law of large numbers for independent random variables under non-additive probabilities

Abstract

Under non‐additive probabilities, cluster points of the empirical average have been proved to quasi-surely fall into the interval constructed by either the lower and upper expectations or the lower and upper Choquet expectations. In this paper, based on the initiated notion of independence, we obtain a different Marcinkiewicz-Zygmund type strong law of large numbers. Then the Kolmogorov type strong law of large numbers can be derived from it directly, stating that the closed interval between the lower and upper expectations is the smallest one that covers cluster points of the empirical average quasi-surely.     

The laws of large numbers for Pareto-type random variables with infinite means

In this paper, we consider the laws of large numbers for NSD random variables satisfying Pareto-type distributions with infinite means. Based on the Pareto-Zipf distributions, some weak laws of large numbers for weighted sums of NSD random variables are obtained. Meanwhile, we show that a weak law for Pareto-Zipf distributions cannot be extended to a strong law. Furthermore, based on the two tailed Pareto distribution, a strong law of large numbers for weighed NSD random variables is presented. Our results extend the corresponding earlier ones.     

英语新闻标题信息有效性解析

标题被视为新闻报道的窗口,标题信息的有效性直接影响读者获取信息的数量和质量。对标题信息有效性的研究有助于新闻标题的设计,也能够为读者浏览新闻提供一些指导意见。笔者从用词数量,语法特点和词汇选用3个方面对英语新闻标题信息的有效性进行了对比分析。数据统计分析结果表明具有较高信息有效性的标题通常由7±2个词组成,采用主动语态的简单句,精选简短的小词和生动活泼的习语,运用恰到好处的修辞手法。