基于累积前景理论的多资产投资组合优化与实证研究

基于CPT视角的多风险资产投资组合模型,探讨投资者面对不同投资风险时的心理变化以及心理变化对其投资策略的影响。通过选取不同超额收益率及波动率水平的股票,测试投资策略对于风险态度指标的敏感度。研究表明,投资者面对不同风险具有明显地心理变化,并且其心理变化对投资策略具有显著的影响。具体表现在几个方面：投资者面对不确定收益时,表现出风险厌恶,面对不确定损失时,表现出风险偏好;投资者将无风险资产的投资收益作为心理参考点,所做的投资决策与相对于此参考点的相对财富水平的变化有关,而不是与传统理论中的绝对财富变化量相关。     

企业用户云服务技术选择行为

如何选择最佳云服务方案,是企业决定采纳云服务之后所面临的一个重要决策。为了解决云服务技术选择问题,从业务流程的视角出发,以模块化系统理论为基础,提出一个流程模块化、IT可拆解性及安全风险如何影响云服务技术选择的研究模型。采用问卷调查和计量经济学的普通最小二乘法OLS进行实证分析。研究结果表明:流程模块化和IT可拆解性对云服务技术选择的抽象程度有显著的正向影响,即流程模块化程度IT可拆解性越高的企业,越倾向于选择抽象程度高的云服务,如软件即服务SaaS;而流程模块化程度和IT可拆解性越低的企业,越倾向于选择抽象程度低的云服务,如基础设施即服务IaaS。同时,安全风险会正向调节流程模块化和IT可拆解性对云服务技术选择的抽象程度的影响。     

高新技术创新的文化路径及政策选择

相对一般的技术创新，高新技术创新有其独有的一些特点，这些特点决定了文化对高新技术创新具有重要作用。高新技术创新的文化路径包括观念文化路径、制度文化路径和行动文化路径三个方面。基于文化视角优化高新技术创新的政策选择，完善我国科技政策，有助于推动高新技术的进一步创新。     

A likelihood ratio test to discriminate exponential–Poisson and gamma distributions

The exponential–Poisson (EP) distribution with scale and shape parameters β>0 and λ∈?, respectively, is a lifetime distribution obtained by mixing exponential and zero-truncated Poisson models. The EP distribution has been a good alternative to the gamma distribution for modelling lifetime, reliability and time intervals of successive natural disasters. Both EP and gamma distributions have some similarities and properties in common, for example, their densities may be strictly decreasing or unimodal, and their hazard rate functions may be decreasing, increasing or constant depending on their shape parameters. On the other hand, the EP distribution has several interesting applications based on stochastic representations involving maximum and minimum of iid exponential variables (with random sample size) which make it of distinguishable scientific importance from the gamma distribution. Given the similarities and different scientific relevance between these models, one question of interest is how to discriminate them. With this in mind, we propose a likelihood ratio test based on Cox's statistic to discriminate the EP and gamma distributions. The asymptotic distribution of the normalized logarithm of the ratio of the maximized likelihoods under two null hypotheses – data come from EP or gamma distributions – is provided. With this, we obtain the probabilities of correct selection. Hence, we propose to choose the model that maximizes the probability of correct selection (PCS). We also determinate the minimum sample size required to discriminate the EP and gamma distributions when the PCS and a given tolerance level based on some distance are before stated. A simulation study to evaluate the accuracy of the asymptotic probabilities of correct selection is also presented. The paper is motivated by two applications to real data sets.     

Limit theorems for dependent Bernoulli variables with statistical inference

We consider a class of dependent Bernoulli variables where the conditional success probability is a linear combination of the last few trials and the original success probability. We obtain its limit theorems including the strong law of large numbers, weak invariance principle, and law of the iterated logarithm. We also derive some statistical inference results which make the model applicable. Simulation results are exhibited as well to show that with small sample size the convergence rate is satisfying and the proposed estimators behave well.     

Quadratic Approximation via the SCAD Penalty with a Diverging Number of Parameters

The high-dimensional data arises in diverse fields of sciences, engineering and humanities. Variable selection plays an important role in dealing with high dimensional statistical modelling. In this article, we study the variable selection of quadratic approximation via the smoothly clipped absolute deviation (SCAD) penalty with a diverging number of parameters. We provide a unified method to select variables and estimate parameters for various of high dimensional models. Under appropriate conditions and with a proper regularization parameter, we show that the estimator has consistency and sparsity, and the estimators of nonzero coefficients enjoy the asymptotic normality as they would have if the zero coefficients were known in advance. In addition, under some mild conditions, we can obtain the global solution of the penalized objective function with the SCAD penalty. Numerical studies and a real data analysis are carried out to confirm the performance of the proposed method.     

A Method to Handle Zero Counts in the Multinomial Model

In the context of an objective Bayesian approach to the multinomial model, Dirichlet(a, …, a) priors with a < 1 have previously been shown to be inadequate in the presence of zero counts, suggesting that the uniform prior (a = 1) is the preferred candidate. In the presence of many zero counts, however, this prior may not be satisfactory either. A model selection approach is proposed, allowing for the possibility of zero parameters corresponding to zero count categories. This approach results in a posterior mixture of Dirichlet distributions and marginal mixtures of beta distributions, which seem to avoid the problems that potentially result from the various proposed Dirichlet priors, in particular in the context of extreme data with zero counts.     

Transductive versions of the LASSO and the Dantzig Selector

Transductive methods are useful in prediction problems when the training dataset is composed of a large number of unlabeled observations and a smaller number of labeled observations. In this paper, we propose an approach for developing transductive prediction procedures that are able to take advantage of the sparsity in the high dimensional linear regression. More precisely, we define transductive versions of the LASSO (Tibshirani, 1996) and the Dantzig Selector (Candès and Tao, 2007). These procedures combine labeled and unlabeled observations of the training dataset to produce a prediction for the unlabeled observations. We propose an experimental study of the transductive estimators that shows that they improve the LASSO and Dantzig Selector in many situations, and particularly in high dimensional problems when the predictors are correlated. We then provide non-asymptotic theoretical guarantees for these estimation methods. Interestingly, our theoretical results show that the Transductive LASSO and Dantzig Selector satisfy sparsity inequalities under weaker assumptions than those required for the “original” LASSO.