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。     

CO-OFDM系统中基于线性预处理的新相位噪声抑制算法

针对相干光正交频分复用（coherent optical orthogonal frequency division multiplexing,CO-OFDM）系统中相位噪声引起的载波间干扰（inter-carrier interference,ICI）问题,提出了一种基于线性预处理的新判决反馈相位噪声抑制算法。该新算法改进了线性预处理部分,利用循环前缀与OFDM符号固有的相关性,在时域进行简单的线性组合运算,充分利用了OFDM符号中冗余信息。仿真分析表明,在激光器线宽为200 kHz且误码率（bit error rate, BER）为10－4时,与判决反馈相位噪声抑制算法和一次迭代的判决反馈相位噪声抑制算法相比,该新算法BER曲线的信噪比（signal to noise ratio,SNR）分别改善了3 dB和1 dB,有效地降低由ICI引起的错误平层。     

线性可调恒流大功率LED驱动电源设计

为满足大功率LED驱动的性能要求,如驱动能力强、效率高、稳定可靠、可线性调节等,本研究设计一种线性可调恒流驱动电源方案.该电路由DC-DC转换模块、恒流驱动模块、PWM数模转换组成.其中,DC-DC转换模块以TPS51113为核心,采用大功率MOSFET同步BUCK模式;恒流驱动模块采用运放加MOSFET典型电路,实现...     

零售商双渠道供应链定价及协调研究

研究了零售商双渠道供应链的定价策略和协调问题,建立制造商和零售商两方博弈模型,分析了集中决策和分散决策两种情境下的定价决策均衡解,并设计收入共享契约来解决零售商双渠道存在的双重边际化效应。结果表明,零售商需要提供合理的收入分配比例才能实现供应链各成员利润的Pareto改进,并最终协调整个双渠道供应链。在双渠道竞争环境下,线上线下不能采取完全同价策略,企业应针对不同渠道的具体情况制定不同的销售价格。     

A comparison study on modeling of clustered and overdispersed count data for multiple comparisons

Data collected in various scientific fields are count data. One way to analyze such data is to compare the individual levels of the factor treatment using multiple comparisons. However, the measured individuals are often clustered – e.g. according to litter or rearing. This must be considered when estimating the parameters by a repeated measurement model. In addition, ignoring the overdispersion to which count data is prone leads to an increase of the type one error rate. We carry out simulation studies using several different data settings and compare different multiple contrast tests with parameter estimates from generalized estimation equations and generalized linear mixed models in order to observe coverage and rejection probabilities. We generate overdispersed, clustered count data in small samples as can be observed in many biological settings. We have found that the generalized estimation equations outperform generalized linear mixed models if the variance-sandwich estimator is correctly specified. Furthermore, generalized linear mixed models show problems with the convergence rate under certain data settings, but there are model implementations with lower implications exists. Finally, we use an example of genetic data to demonstrate the application of the multiple contrast test and the problems of ignoring strong overdispersion.     

Variance estimation based on blocked 3×2 cross-validation in high-dimensional linear regression

In high-dimensional linear regression, the dimension of variables is always greater than the sample size. In this situation, the traditional variance estimation technique based on ordinary least squares constantly exhibits a high bias even under sparsity assumption. One of the major reasons is the high spurious correlation between unobserved realized noise and several predictors. To alleviate this problem, a refitted cross-validation (RCV) method has been proposed in the literature. However, for a complicated model, the RCV exhibits a lower probability that the selected model includes the true model in case of finite samples. This phenomenon may easily result in a large bias of variance estimation. Thus, a model selection method based on the ranks of the frequency of occurrences in six votes from a blocked 3×2 cross-validation is proposed in this study. The proposed method has a considerably larger probability of including the true model in practice than the RCV method. The variance estimation obtained using the model selected by the proposed method also shows a lower bias and a smaller variance. Furthermore, theoretical analysis proves the asymptotic normality property of the proposed variance estimation.     

“走廊”与“通道”:中国西南区域研究的人类学再构思

中国西南的人类学研究有着深厚的区域研究传统。改革开放之后,西南研究陆续出现了＂区域模式＂、＂族群模式＂和＂跨境模式＂三种研究范式。其中＂区域模式＂更贴近于社会历史现实,具有强烈的学科问题意识。至二十一世纪,基于历史上民族的源流考察与走势判断,兴起了诸如西南丝绸之路、茶马古道、滇黔驿道、进藏公路等＂通道＂研究。这种基于＂走廊＂学说和＂通道＂研究而展开的＂区域模式＂,将交互性和流动性作为研究之重点,来深入地阐释形塑西南中国社会人文特质的关系结构,将有利于中国人类学获得更符合中国历史文化经验的世界图式,也有助于具体研究者获得真正的文化自觉和认知主体性。人类学的中国西南研究只有基于＂区域性＂的大视野来展开,在一种关系格局中来寻找西南研究的问题意识,才能摆脱＂民族性＂的现代叙事,并对＂民族认同＂这样的问题有所超越,最终回归到当地的社会历史现实和人文形态特质来深化对中国西南的认识把握,从而在学理上展开一些更为深入的对话。     

Testing for bioequivalence of highly variable drugs from TR‐RT crossover designs with heterogeneous residual variances

Traditional bioavailability studies assess average bioequivalence (ABE) between the test (T) and reference (R) products under the crossover design with TR and RT sequences. With highly variable (HV) drugs whose intrasubject coefficient of variation in pharmacokinetic measures is 30% or greater, assertion of ABE becomes difficult due to the large sample sizes needed to achieve adequate power. In 2011, the FDA adopted a more relaxed, yet complex, ABE criterion and supplied a procedure to assess this criterion exclusively under TRR‐RTR‐RRT and TRTR‐RTRT designs. However, designs with more than 2 periods are not always feasible. This present work investigates how to evaluate HV drugs under TR‐RT designs. A mixed model with heterogeneous residual variances is used to fit data from TR‐RT designs. Under the assumption of zero subject‐by‐formulation interaction, this basic model is comparable to the FDA‐recommended model for TRR‐RTR‐RRT and TRTR‐RTRT designs, suggesting the conceptual plausibility of our approach. To overcome the distributional dependency among summary statistics of model parameters, we develop statistical tests via the generalized pivotal quantity (GPQ). A real‐world data example is given to illustrate the utility of the resulting procedures. Our simulation study identifies a GPQ‐based testing procedure that evaluates HV drugs under practical TR‐RT designs with desirable type I error rate and reasonable power. In comparison to the FDA's approach, this GPQ‐based procedure gives similar performance when the product's intersubject standard deviation is low (≤0.4) and is most useful when practical considerations restrict the crossover design to 2 periods.     

Forecast mean squared error reductionin the VAR(1) process

When VAR models are used to predict future outcomes, the forecast error can be substantial. Through imposition of restrictions on the off-diagonal elements of the parameter matrix, however, the information in the process may be condensed to the marginal processes. In particular, if the cross-autocorrelations in the system are small and only a small sample is available, then such a restriction may reduce the forecast mean squared error considerably.

In this paper, we propose three different techniques to decide whether to use the restricted or unrestricted model, i.e. the full VAR(1) model or only marginal AR(1) models. In a Monte Carlo simulation study, all three proposed tests have been found to behave quite differently depending on the parameter setting. One of the proposed tests stands out, however, as the preferred one and is shown to outperform other estimators for a wide range of parameter settings.