Deletions in random binary search trees: A story of errors

The usual assumptions for the average case analysis of binary search trees (BSTs) are random insertions and random deletions. If a BST is built by n random insertions the expected number of key comparisons necessary to access a node is 2 ln n+O(1). This well-known result is already contained in the first papers on such ‘random’ BSTs. However, if random insertions are intermixed with random deletions the analysis of the resulting BST seems to become more intricate. At least this is the impression one gets from the related publications since 1962, and it is quite appropriate to speak of a story of errors in this context, as will be seen in the present survey paper, giving an overview on this story.     

The concept of risk unbiasedness in statistical prediction

This paper extends the concept of risk unbiasedness for applying to statistical prediction and nonstandard inference problems, by formalizing the idea that a risk unbiased predictor should be at least as close to the “true” predictant as to any “wrong” predictant, on the average. A novel aspect of our approach is measuring closeness between a predicted value and the predictant by a regret function, derived suitably from the given loss function. The general concept is more relevant than mean unbiasedness, especially for asymmetric loss functions. For squared error loss, we present a method for deriving best (minimum risk) risk unbiased predictors when the regression function is linear in a function of the parameters. We derive a Rao–Blackwell type result for a class of loss functions that includes squared error and LINEX losses as special cases. For location-scale families, we prove that if a unique best risk unbiased predictor exists, then it is equivariant. The concepts and results are illustrated with several examples. One interesting finding is that in some problems a best unbiased predictor does not exist, but a best risk unbiased predictor can be obtained. Thus, risk unbiasedness can be a useful tool for selecting a predictor.     

Subsistence,risk-taking,and reciprocity among the Tanchangya in the Chittagong Hill Tracts,Bangladesh

This paper examines livelihood strategies of Tanchangya culantro cultivators of the Chittagong Hill Tracts, Bangladesh, in relation to their subsistence, risk-taking, and reciprocity practices, who have been embroiled in compulsive market participation due to paternalist state policies. It puts forward two propositions. First, the objectives of protecting subsistence and of improving familial situation drive Tanchangya peasants to employ flexible strategies in relation to risk management and income generation. Second, it proposes that reciprocity practices provide minimum security to village households in times of crises and exigencies, and work as a safeguard against the exploitation of capitalist Bengali traders. It concludes that subsistence struggles lead peasant families to choose most suitable crops and farming methods and remain open to diverse income sources. Village reciprocity practices, either as dynamic and evolving relationships between two actors or involving the larger community, having different forms, supplement this struggle of peasants for survival.     

多源高维数据的多分类纵向整合分析及应用

多分类数据分析在实证研究中具有重要意义。然而,由于高维数、小样本及低信噪比等原因,现有的多分类方法仍面临信息量不足而导致的效果不佳问题。为此,学者们通过收集更多信息源 数据以更全面地刻画实际问题。不同于收集相同自变量的不同源样本,目前较为流行的多源数据收集了相同样本的不同源自变量,它们的独立性和相关性为统计建模带来了新的挑战。本文提出基于典型变量回归的多分类纵向整合分析方法,其中利用惩罚技术实现变量选择,并独特地考虑不同源数据间的关联结构,提出高效的ADMM算法进行模型优化。数值模拟结果表明,该方法在变量选择和分类预测 上均具有优越性。基于我国上证50的多源股票数据,利用该方法对2019年股票日收益率的影响因素进行了实证探究。研究表明,本文提出的多分类整合分析在筛选出具有解释意义变量的同时具有更好的预测效果。     

基于变系数模型的高维数据异同性识别方法研究

随着信息技术的发展,高维数据日益丰富。现实中,很多高维数据由多个主体各异的数据集融合而成。如何准确识别出高维数据集间的异同性成为大数据分析的目标之一。本文提出了变系数模型下的高维数据整合分析方法。该方法可以同时对多个数据集进行变量选择和系数估计,并且能 够自动识别出变量系数在数据集间的异同性。模拟结果表明本文方法在异同性识别、变量选择、系数估 计和预测等方面明显优于对比方法。在肺癌致病基因识别的应用研究中,本文方法能够识别出具有生物解释的致病基因并发现了两种亚型之间的异同性。     

Satisfaction and preferences in a legality social dilemma: Does corporate social responsibility impact consumers’ behaviour?

We investigate players’ preferences in a multiplayer prisoner's dilemma by comparing results from satisfaction-based and a choice-based approach by means of a laboratory experiment. The experimental design tests the effects of the legality rating frame on consumers’ choice between products from producers labeled by the legality rating and products by unlabeled producers. Both approaches provide strong evidence of preference heterogeneity, with players who cooperate above median being less affected in their choice by monetary payoffs vis-à-vis the public good component. The empirical findings support the hypothesis of our theoretical model that (part of the) players have, in addition to the standard self-interest component, an other-regarding preference argument that is further satisfied in the legality frame plus conformity design.A policy suggestion stemming from our experiment is based on the fact that corporate social responsibility legality frames and culture have a significant effect on an important portion of consumers. These consumers reveal that the often-declared willingness to pay for socially, environmentally and legally responsible features of products is confirmed by actual purchases of more expensive responsible products. Governments should therefore promote the creation of legality rating schemes with well-defined pre-established rules such as in the Italian case.     

分形插值在储层参数预测中的应用

岩心分析和测井分析表明,沉积岩的物性分布常常具有分形特征;文中用改进实验变差函数分析某油田X探区测井和取心的孔渗数据,得到在某些井中孔渗参数具有分形特征,并采用fGn分形插值技术,得到井间参数的空间分布。     

报表分析中的利润剖析

在实际工作中,部分企业为了一些目的而对报表进行调控,本文通过对调控利润的方式进行了分类、分析,并提出合理的利润结构应有的特征。旨在给一些读者以启示,在分析企业报表时,保持一份应有的清醒,不被其高额的利润所迷惑。     

Generalized Quantification of Risk Associated with Extreme Events1

In the partitioned multiobjective risk method (PMRM) the probability axis is typically partitioned into three regimes: high-exceedance low-consequence, intermediate-exceedance intermediate-consequence, and low-exceedance high-consequence (LE/HC). For each regime, the PMRM generates a conditional expected risk-function given that the damage lies within the regime. The theme of this paper is the conditional expected-risk function for the LE/HC regime. This function, denoted by f₄(.), captures the behavior of the “extreme events” of an underlying decision-making problem. The PMRM offers two advantages: (a) it isolates LE/HC events, allowing the decision-maker(s) to focus on the impacts of catastrophies; and (b) it generates more valuable information than that obtained from the common unconditional expected-risk function. Theoretical problems may arise from uncertainty about the behavior of the tail of the risk curve describing the underlying frequency of damages. When the number of physical observations in small (e.g., in flood frequency analysis), the analyst is forced to make assumptions about the density of damages. Each succeeding distributional assumption will generate a different value of f₄(.). An added dimension of difficulty is also created by the sensitivity of f₄(.) to the choice of the boundary of the LE/HC regime. This paper has two overall objectives: (a) to present distribution-free results concerning the magnitude of f₄(.); and (b) to use those results to obtain a distribution-free estimate of the sensitivity of f₄(.) to the choice of the boundary of the LE/HC regime. The above objectives are realized by extending, and further developing, existing inequalities for continuously distributed random variables.