产品质量差异与需求不确定双因素下的双渠道供应链分销策略选择

在考虑产品质量差异与需求不确定双因素影响的基础上,通过构建垂直Nash定价与库存联合博弈模型,研究当线上与线下双渠道销售特定异质产品时,供应链成员如何根据不同的产品类型选择最优分销策略以实现双渠道供应链的共赢。研究结果表明:对于质量差异化低且需求不确定性低的产品类型,供应链成员的共赢分销策略是在线下渠道销售低质量产品并在线上渠道销售高质量产品;对于质量差异化低且需求不确定性高的产品类型,共赢分销策略是在线下销售高质量产品并在线上销售低质量产品;当制造商采用“做坏品”质量差异化策略时,产品类型和分销策略之间的最佳匹配依然保持稳健。     

收费公路运营效率测度及时空分异研究

收费公路行业内部债务治理与外部减税降费均要求收费公路运营降本增效。为了探析收费公路运营效率水平及其空间分布格局,使用SBM模型测度了2014—2020年29个省份收费公路运营效率,进而采用泰尔指数和Moran's I指数分析了其时空分异特征。研究认为:收费公路运营存在着效率较低且在时间维度上没有明显增长的趋势,在空间上存在发展不平衡问题,收费公路运营效率的区域差异主要来源于区域内部差异,区域内差异的贡献率平均达到81.578%; 收费公路运营效率具有空间正相关性,呈现出显著的“高-高”集聚和“低-低”集聚的空间特征,运营效率较高的省份对相邻省份具有正向溢出效应。研究表明,各省针对相邻省份或最近省份的运营效率现状,结合自身的比较优势探索产业升级或者物流发展模式,充分发挥相邻省份的经济发展和产业集聚对本省收费公路运营的辐射带动作用,推动建立起收费公路跨区域合作运营机制。     

新能源汽车产业商业模式创新多要素联动机制研究 ———基于创新生态系统视角

基于创新生态系统理论和种群关系理论,将我国新能源汽车产业上市公司划分为不同生态位 企业种群。 运用模糊集定性比较分析法,从资源基础、企业家精神、技术创新、激励水平和环境动态性 5 个维度出发,探究不同生态位新能源汽车企业种群商业模式创新的联动机制。 研究结果表明:低生态位 企业种群存在“技术创新-激励水平”“技术创新-环境动态性”两种高水平商业模式创新联动机制;中生 态位企业种群主要通过内部资源基础和企业家精神驱动来实现商业模式创新发展;高生态位企业种群存 在“企业家精神-环境动态性”“企业家精神”“激励水平-环境动态性”三种商业模式要素联动机制。     

多元正态分布广义方差区间估计的改进

讨论了多元正态分布广义方差的区间估计问题,给出了在覆盖率及长度上均优于最优仿射同变区间估计的改进估计．     

A Population Approach to Analysis of Variance Models

Several authors have contributed to what can now be considered a rather complete theory for analysis of variance in cases with orthogonal factors. By using this theory on an assumed basic reference population, the orthogonality concept gives a natural definition of independence between factors in the population. By looking upon the treated units in designed experiments as a formal sample from a future population about which we want to make inference, a natural parametrization of expectations and variances connected to such experiments arises. This approach seems to throw light upon several controversial questions in the theory of mixed models. Also, it gives a framework for discussing the choice of conditioning in models     

A Multivariate Model for Repeated Failure Time Measurements

A parametric multivariate failure time distribution is derived from a frailty-type model with a particular frailty distribution. It covers as special cases certain distributions which have been used for multivariate survival data in recent years. Some properties of the distribution are derived: its marginal and conditional distributions lie within the parametric family, and association between the component variates can be positive or, to a limited extent, negative. The simple closed form of the survivor function is useful for right-censored data, as occur commonly in survival analysis, and for calculating uniform residuals. Also featured is the distribution of ratios of paired failure times. The model is applied to data from the literature     

A Unified Approach to the Approximation of Multivariate Densities

Approximation of a density by another density is considered in the case of different dimensionalities of the distributions. The results have been derived by inverting expansions of characteristic functions with the help of matrix techniques. The approximations obtained are all functions of cumulant differences and derivatives of the approximating density. The multivariate Edgeworth expansion follows from the results as a special case. Furthermore, the density functions of the trace and eigenvalues of the sample covariance matrix are approximated by the multivariate normal density and a numerical example is given     

A Class of Efficient Non-parametric K-sample Tests for Multivariate Counting Processes with Frailties

A K -sample testing problem is studied for multivariate counting processes with time-dependent frailty. Asymptotic distributions and efficiency of a class of non-parametric test statistics are established for certain local alternatives. The concept of efficiency is to show that for every non-parametric test in this class, there is a parametric submodel for which the optimal test has the same asymptotic power as the non-parametric one. The theory is applied to analyse a diabetic retinopathy study data set. A simulation study is also presented to illustrate the theory     

Efficient Estimation of a Distribution Function under Quadrant Dependence

LetX₁,X₂, ..., be real-valued random variables forming a strictly stationary sequence, and satisfying the basic requirement of being either pairwise positively quadrant dependent or pairwise negatively quadrant dependent. LetF^ be the marginal distribution function of theX_ips, which is estimated by the empirical distribution functionF_n and also by a smooth kernel-type estimateF_n, by means of the segmentX₁, ...,X_n. These estimates are compared on the basis of their mean squared errors (MSE). The main results of this paper are the following. Under certain regularity conditions, the optimal bandwidth (in the MSE sense) is determined, and is found to be the same as that in the independent identically distributed case. It is also shown thatn MSE(F_n(t)) andnMSE (F^_n(t)) tend to the same constant, asn→∞ so that one can not discriminate be tween the two estimates on the basis of the MSE. Next, ifi(n) = min {k∈{1, 2, ...}; MSE (F_k(t)) ≤ MSE (F_n(t))}, then it is proved thati(n)/n tends to 1, asn→∞. Thus, once again, one can not choose one estimate over the other in terms of their asymptotic relative efficiency. If, however, the squared bias ofF^_n(t) tends to 0 sufficiently fast, or equivalently, the bandwidthh_n satisfies the requirement thatnh³_n→ 0, asn→∞, it is shown that, for a suitable choice of the kernel, (i(n) ?n)/(nh_n) tends to a positive number, asn→∞ It follows that the deficiency ofF_n(t) with respect toF^_n(t),i(n) ?n, is substantial, and, actually, tends to ∞, asn→∞. In terms of deficiency, the smooth estimateF^_n(t) is preferable to the empirical distribution functionF_n(t)     

The calibration of P-values,posterior Bayes factors and the AIC from the posterior distribution of the likelihood

The posterior distribution of the likelihood is used to interpret the evidential meaning of P-values, posterior Bayes factors and Akaike's information criterion when comparing point null hypotheses with composite alternatives. Asymptotic arguments lead to simple re-calibrations of these criteria in terms of posterior tail probabilities of the likelihood ratio. (Prior) Bayes factors cannot be calibrated in this way as they are model-specific.