Model selection in high-dimensional noisy data: a simulation study

In many practical applications, high-dimensional regression analyses have to take into account measurement error in the covariates. It is thus necessary to extend regularization methods, that can handle the situation where the number of covariates p largely exceed the sample size n, to the case in which covariates are also mismeasured. A variety of methods are available in this context, but many of them rely on knowledge about the measurement error and the structure of its covariance matrix. In this paper, we set the goal to compare some of these methods, focusing on situations relevant for practical applications. In particular, we will evaluate these methods in setups in which the measurement error distribution and dependence structure are not known and have to be estimated from data. Our focus is on variable selection, and the evaluation is based on extensive simulations.     

Instrumental variable based variable selection for generalized linear models with endogenous covariates

We study the variable selection problem for a class of generalized linear models with endogenous covariates. Based on the instrumental variable adjustment technology and the smooth-threshold estimating equation (SEE) method, we propose an instrumental variable based variable selection procedure. The proposed variable selection method can attenuate the effect of endogeneity in covariates, and is easy for application in practice. Some theoretical results are also derived such as the consistency of the proposed variable selection procedure and the convergence rate of the resulting estimator. Further, some simulation studies and a real data analysis are conducted to evaluate the performance of the proposed method, and simulation results show that the proposed method is workable.     

Fast and approximate exhaustive variable selection for generalised linear models with APES

We present APproximated Exhaustive Search (APES), which enables fast and approximated exhaustive variable selection in Generalised Linear Models (GLMs). While exhaustive variable selection remains as the gold standard in many model selection contexts, traditional exhaustive variable selection suffers from computational feasibility issues. More precisely, there is often a high cost associated with computing maximum likelihood estimates (MLE) for all subsets of GLMs. Efficient algorithms for exhaustive searches exist for linear models, most notably the leaps‐and‐bound algorithm and, more recently, the mixed integer optimisation (MIO) algorithm. The APES method learns from observational weights in a generalised linear regression super‐model and reformulates the GLM problem as a linear regression problem. In this way, APES can approximate a true exhaustive search in the original GLM space. Where exhaustive variable selection is not computationally feasible, we propose a best‐subset search, which also closely approximates a true exhaustive search. APES is made available in both as a standalone R package as well as part of the already existing mplot package.     

高频数据瞬时波动率核估计的窗宽选择及算法研究

在瞬时波动率的各种估计量中,非参数估计量因其能准确地度量瞬时波动率,一直是学者们的研究热点。然而,这类估计量在实际应用中都面临着最优窗宽的确定问题。由于最优窗宽中往往携带一些难以估计的未知参数,使得在实际应用过程中确定最优窗宽的具体数值存在困难。本文以瞬时波动率的核估计量为例,借鉴非参数回归分析中窗宽选择的思想,构建了一种能从数据中准确计算出最优窗宽具体值的算法。理论的分析和数值上的验证表明：文中所构建的算法具有良好的稳定性、适应性和收敛速度。算法的提出为瞬时波动率的后续应用研究铺平道路。     

基于股价预测的泛证券投资组合策略

本文基于期望效用最大化和L₁-中位数估计研究了在线投资组合选择问题。与EG（Exponential Gradient）策略仅利用单期价格信息估计价格趋势不同,本文将利用多期价格信息估计价格趋势,以提高在线策略的性能。首先,基于多期价格数据,利用L₁-中位数估计得到预期价格趋势。然后,通过期望效用最大化,提出一个新的具有线型时间复杂度的在线策略,EGLM（Exponential Gradient via L₁-Median）。并通过相对熵函数定义资产权重向量的距离,进而证明了EGLM策略具有泛证券投资组合性质。最后,利用国内外6个证券市场的历史数据进行实证分析,结果表明相较于UP（Universal Portfolio）策略和EG策略,EGLM策略有更好的竞争性能。     

异质品双渠道供应链下制造商的渠道决策研究

研究由两个制造商、两个零售商以及消费者组成的双渠道供应链,制造商的最优产品分销渠道策略问题,通过构建Stackelberg主从动态博弈模型,分析制造商在不同渠道选择下的产品质量水平及定价决策,消费者三种不同渠道偏好下渠道竞争和品牌竞争激烈程度对制造商渠道选择均衡结果及帕累托最优选择的影响,以及对产品质量水平、产品价格、制造商及零售商利润的影响.研究表明：品牌和渠道双重竞争下,两种竞争之间存在相互作用的关系,并共同影响制造商最终渠道选择,制造商通过权衡价格决策的后动优势与消费者渠道偏好对其利润的贡献选择是否开辟双渠道,产品价格与产品质量水平正相关,与产品的替代率负相关,产品质量水平对渠道价格的影响程度等于消费者对该渠道的偏好程度.当消费者偏好某一渠道时,制造商可在该渠道上的产品采取优质高价策略,但渠道上产品性价比降低,当品牌竞争越激烈,产品价格越低,对制造商和零售商的利润挤压越严重.     

多渠道医疗门诊挂号的时间策略研究

现阶段多种门诊挂号方式并存时,根据患者特性制定不同的等待时间策略尤为重要。本文考虑门诊挂号的三种渠道：直接排队挂号渠道、电话预约挂号渠道与O2O预约挂号渠道。在价格外生前提下,综合考虑患者的渠道偏好程度、时间敏感性与提前支付敏感性,基于效用理论构建患者的渠道选择模型。根据该模型推导得出不同市场条件下医院的需求,医院再据此制定患者的等待时间策略以达到利润最大化的目的。研究发现：当医院仅开通单一挂号渠道时,若患者的时间敏感度越大、渠道偏好程度越小,等待时间均越小;开通某两种挂号渠道时,还需考虑患者对两种渠道的偏好比例,偏好某种渠道的患者比例增加,选择该渠道的患者等待时间增加,选择另一渠道的患者等待时间减小;同时开通三种渠道时,最偏好某渠道的患者比例越大,选择该渠道的患者的等待时间也越大;在所有情况下,患者对提前支付的敏感度均不影响最优等待时间的制定。     

Women Rule: Gendered Leadership and State Supreme Court Chief Justice Selection

ABSTRACT

While women are underrepresented in many political institutions and leadership positions, nearly half of state supreme court chief justices are women. Is there something about the role of state supreme court justice that facilitates the recruitment of women to this important political position? We examine whether the selection of a woman chief is driven by the court’s institutional need for women’s leadership style or simply the supply of qualified justices. We find that ideological diversity drives demand for a woman chief. A supply of experienced women justices also has a significant impact on likelihood of selection.     

单窃听双跳协作网络的中继选择方案及其性能分析

研究了安全通信意义下,单向译码转发（decode-and-forward,DF）协作无线网络的中继选择问题。针对窃听者既能获得信源发出的信号,又能窃取中继节点转发数据的通信系统,提出了3种中继选择方案来对抗窃听者,增强系统物理层安全性。其中,方案一选择到窃听者信噪比（signal-to-noise ratio,SNR）最小的中继节点;方案二为最大最小（max-min）选择方案,即选择信源到中继节点和中继节点到信宿的较差信噪比中最大值所对应的中继节点;方案三根据窃听信道和主信道的瞬时信道状态信息（channel state information,CSI）选择使得窃听网络有最大保密容量的中继节点。在对各方案的性能分析过程中,得到了各中继选择方案拦截概率的闭式表示,进一步对拦截概率作渐近分析,获得了各中继选择方案的分集阶数。具体地,方案一的分集阶数为1,另外2个中继选择方案的分集阶数均为中继节点个数M。数值结果验证了理论分析得到的结论。