Some results on the compound Markov binomial model

Abstract

Some years ago, in the course of an analysis of upper and lower limits for incomplete moments of statistical distributions I established an elementary summation formula¹ which proved rather useful for the purpose I had in view. Subsequently the formula was generalized by professor Steffensen, who showed² that the formula in question could be looked upon as giving the first term of an expansion in a certain type of series. Professor Steffensen established recurrence formulae for the coefficients of the series and computed the second, third and fourth term and the corresponding remainders¹, but did not arrive at a general, explicite expression for the coefficient of the n-th term and the corresponding remainder. A year later I found these expressions accidentally while I was working on some other problem. I also discovered the real nature of the procedure in question which proved to be a certain kind of least square fitted polynomial approximation. I did not, however, at the time publish the result. Taking the question up again later I found that the whole problem could be considerably generalized. The type of generalization in question is analogous to the generalization from polynomials to arbitrary functions.     

Does player specialization predict player actions? Evidence from penalty kicks at FIFA World Cup and UEFA Euro Cup

Penalty kicks are analysed in the literature as ‘real life experiments’ for assessing the use of rational mixed strategies by professional players. However, each penalty kick cannot be considered a repetition of the same event because of the varying background conditions, in particular the heterogeneous ability of different players. Consequently, aggregate statistics over data sets composed of a large number of penalty kicks mediate the behaviour of the players in different games, and the properties of optimal mixed strategies cannot be tested directly because of aggregation bias. In this article, we model the heterogeneous ability of players. We then test the hypothesis that differently talented players randomize over different actions. To achieve this aim, we study a data set that collects penalties kicked during shoot-out series in the last editions of FIFA World Cup and UEFA Euro Cup (1994–2012) where kickers are categorized as specialists and non-specialists. The results support our theoretical predictions.     

基于政府行为的农村信用体系建设分析——以甘肃省定西市为例

政府能在农村信用体系建设中发挥其组织领导、宣传教育、信用评价和政策激励等优势。甘肃省定西市政府通过强化农村信用体系建设,促进了农村经济发展,提升了农户贷款获取能力,增强了金融机构经营实力。为进一步推动农村信用体系建设,政府应加强法制建设,完善信用奖惩制度,加大政策扶持力度,创新征信宣传内容和宣传方式。     

轻刑化简论

轻刑化是客观存在的历史规律,轻刑化思想源远流长,了解轻刑化的动因,掌握轻刑化的规律,领悟轻刑化的思想内涵,对于我国的轻刑化建设必定产生深远影响。通过对轻刑化思想演进及轻刑化动因分析,旨在探讨我国刑法中的轻刑化建设。     

保险诈骗罪司法认定若干问题探析

我国刑法关于保险诈骗罪的规定采用的是结果犯模式,只有行为人实施了法定的保险诈骗行为,并取得或控制了数额较大的保险金,才构成本罪的既遂。鉴定人、证明人、财产评估人的行为为保险诈骗罪的实行犯创造条件的,可构成共犯中的帮助犯。保险公司的工作人员内外勾结骗取保险金的行为,实质上只是各行为人共同利用保险公司工作人员职务上的便利而实施的骗取保险公司财产的职务侵占或者贪污行为,只可能构成贪污或者职务侵占罪。     

财务报告舞弊行政处罚与可感知审计质量

以2001年至2008年间非金融类、非外资类正常交易A股公司为研究样本,考察财务报告舞弊行政处罚是否传递了可感知审计质量信息,实证研究结果表明:投资者总体上并不认可财务报告舞弊行政处罚传递了可感知审计质量信息,仅当投资者观察到反映坏消息的审计意见(如审计师出具非标审计意见、审计意见未改善等)和反映审计师声誉信息(如审计师受到行政处罚)的情况下,投资者才认为财务报告舞弊行政处罚传递了可感知审计质量信息。     

基于博弈视角的银企信贷关系分析

银行和企业之间的信贷关系是影响我国经济增长的重要因素。由于信息不对称,银企信贷关系存在着很大的不确定性。本文通过对银企信贷行为的博弈分析,找出信贷风险的存在原因。通过增大企业贷款前的伪装成本,减少企业的逆向选择行为;增大企业贷款后的惩罚成本,减少企业的道德风险行为,从根本上改变银企之间的信息不对称状况,有利于银企关系的良好发展。     

Granger causality detection in high-dimensional systems using feedforward neural networks

This paper proposes a novel methodology to detect Granger causality on average in vector autoregressive settings using feedforward neural networks. The approach accommodates unknown dependence structures between elements of high-dimensional multivariate time series with weak and strong persistence. To do this, we propose a two-stage procedure: first, we maximize the transfer of information between input and output variables in the network in order to obtain an optimal number of nodes in the intermediate hidden layers. Second, we apply a novel sparse double group lasso penalty function in order to identify the variables that have the predictive ability and, hence, indicate that Granger causality is present in the others. The penalty function inducing sparsity is applied to the weights that characterize the nodes of the neural network. We show the correct identification of these weights so as to increase sample sizes. We apply this method to the recently created Tobalaba network of renewable energy companies and show the increase in connectivity between companies after the creation of the network using Granger causality measures to map the connections.     

The suspension of borrowing: an implicit penalty for loan default under imperfect information

A credit seeker may be suspended from borrowing for a period of time due to a previous default. Such suspension is widely used in bank lending through credit check. Our work analyses the effects of suspension on the investment choice of borrowers under uncertainty and on the lending policy of banks facing asymmetric information. We show that suspension should be tightened at low loan rates, but loosened otherwise, to improve the repayment performance of borrowers. We also show that although credit rationing may not be completely removed due to imperfect information, the excess demand for credit or transitive waiting in the market can actually be attenuated by such efficient use of suspension. Our theoretical predictions are consistent with observed cyclical patterns of changes in lendingrates and suspension severity.     

基于Logistic回归惩罚函数的企业财务危机预警

随着大数据时代的到来，企业的财务数据越来越复杂，在高维的财务数据中选出重要的影响因素，可以帮助企业人员对企业发生财务危机的可能性进行分析预测，降低企业的风险。研究企业财务危机的传统方法大多数是基于低维数据，而目前的财务数据变量众多。惩罚函数（penalty function）不仅能在高维数据的情况下进行稳定的变量选择，还能进行参数估计。采用基于Logistic回归惩罚函数模型研究企业财务危机预警问题，通过选取48个财务指标来进行建模和分析，找出影响企业财务危机的主要因素，并从预测精度、风险区分能力、变量选择这3个方面与传统方法进行对比，最终得到基于Logistic回归的惩罚函数方法更具有优越性。这对企业进行财务危机分析具有预警作用。