浅析基带信号发生器的设计研究

基带信号发生器的设计与实现可以有多种方法,本文重点介绍了基带信号发生器的几种主要方法,如CDMA2000基带信号发生器的设计方法,基于FPGA的基带信号发生器的设计方法等,对其设计原理,设计方案等进行了分析.在此基础上本文提出一种自己的设计方案对基带信号发生器进行设计,并详细介绍了该设计方案的工作原理等内容.     

顾客体验之于新产品供应链协调的影响

新产品引入市场时,其内在价值很难判断,顾客体验能够降低其接受产品的风险.研究了外生体验与内生体验对供应链协调的影响.考虑了销售补偿合同与回购合同,其中,销售补偿合同是供应商对销售商已销售的产品给予补偿,而回购合同是对未销售的产品进行补偿.结论表明,提升外生体验,即顾客收到的噪声信号质量较高,销售商能制定更高的销售价格,...     

Bain: a program for Bayesian testing of order constrained hypotheses in structural equation models

This paper presents a new statistical method and accompanying software for the evaluation of order constrained hypotheses in structural equation models (SEM). The method is based on a large sample approximation of the Bayes factor using a prior with a data-based correlational structure. An efficient algorithm is written into an R package to ensure fast computation. The package, referred to as Bain, is easy to use for applied researchers. Two classical examples from the SEM literature are used to illustrate the methodology and software.     

竞争市场上保修期与价格的联合质量信号传递作用

虽然顾客重视产品的质量（可靠性），但其在做出购买决策时往往不能完全掌握产品的质量信息。不同质量的产品可能具有不同生产和保修成本，因此保修期和销售价格可以成为传递产品质量的有效信号。考虑市场上存在相互竞争的两个生产商：在位者和新进者，顾客了解在位者的产品质量信息但不了解新进者的质量状况。研究并比较了仅保修期作为单质量信号、保修期和价格同时作为质量信号时，市场实现分离均衡的条件，以及新进者的保修期设计与定价决策。研究表明：在保修期作为单质量信号时，只要高质量新进者的单位时间保修成本小于低质量新进者，即可实现分离均衡；在保修期和价格同时作为质量信号时，当满足某些条件时也可实现分离均衡。相对于保修期作单质量信号的情形，当保修期和价格同作质量信号时，高质量新进者实现分离均衡所需提供的保修期更短、价格更低（信息租金更少），且能获得更高的市场份额和利润。最后，数值分析表明保修期和价格同作质量信号时所需满足的条件是比较宽松的，比保修期单独作质量信号时更容易实现分离均衡。     

基于前景理论的PPP项目投资者逆向选择问题研究

PPP项目中投资者逆向选择问题时有发生，严重制约我国PPP模式的可持续发展。为减少投资者逆向选择，在采购阶段对投资者合理筛选机制的设置至关重要。考虑到政府部门和投资者均为"有限理性"，本文基于前景理论，构建了PPP项目采购阶段双方之间的信号博弈模型。研究结果表明，低能力投资者的伪装成本是PPP市场实现分离均衡的决定性因素。为节约政府部门的检查成本，并使PPP市场更容易出现分离均衡，本文引入政府部门抽检模型，即对投资者报价的合理性进行抽检，进而得到最优抽检比例，可完全杜绝投资者逆向选择。通过数值模拟显示，政府部门最优抽检比例随着高能力投资者可获得合理利润率的增加而增加，但随着伪装成本、政府惩罚力度和高能力投资者折现率报价的增加而降低；且相比于收益，投资者对损失更为敏感，但随着收益和损失的增加，其敏感程度降低，符合前景理论。前景理论的引入，考虑了博弈过程中决策者的心理因素，使结果更接近真实情况。最后，为有效抑制投资者逆向选择，本文针对性提出了相关政策建议，为政府部门和投资者的决策提供参考和依据，助力完善PPP项目治理体系。     

Multi-attribute unit and group runs control chart to identify the process deterioration

Gadre and Rattihalli [Gadre, M.P. and Rattihalli, R.N., 2005a, A unit and group runs based chart to identify increases in fraction nonconforming. Journal of Quality Technology, 37, 199–209.] proposed a control chart called the unit and group runs (UGR) control chart to identify increases in fraction non-conforming. In this article, the concept of UGR chart is extended to the multi-attribute case to detect the process deterioration. It is illustrated that in multi-attribute cases also, the UGR chart gives a remarkable reduction in out-of-control average time to signal when compared with the multi-attribute np chart, the multi-attribute synthetic chart and the multi-attribute group runs chart recently developed by Gadre and Rattihalli [Gadre, M.P. and Rattihalli, R.N., 2005b, Some group inspection based multi-attribute control charts. Economic Quality Control, 20, 191–204.]. The steady state performance of the multi-attribute UGR chart is also excellent. The procedure of identifying the attributes causing signal is also described and illustrated.     

On the sampling distribution of clement's capability index

Most of the research effort concerning the development and statistical study of capability indices has been devoted to normal processes. In this paper a statistical study of a capability index for non-normal processes proposed by Clements (1989) is developed. An approximate distribution for the natural estimator of the index is obtained from a distribution free point of view and a simulation study is used to compare it with its empirical distribution. An approximate conservative lower confidence limit for the index is also constructed.     

Performance of Variable Selection Methods in Regression Using Variations of the Bayesian Information Criterion

The Bayesian information criterion (BIC) is widely used for variable selection. We focus on the regression setting for which variations of the BIC have been proposed. A version that includes the Fisher Information matrix of the predictor variables performed best in one published study. In this article, we extend the evaluation, introduce a performance measure involving how closely posterior probabilities are approximated, and conclude that the version that includes the Fisher Information often favors regression models having more predictors, depending on the scale and correlation structure of the predictor matrix. In the image analysis application that we describe, we therefore prefer the standard BIC approximation because of its relative simplicity and competitive performance at approximating the true posterior probabilities.     

A Modular CDF Approach for the Approximation of Percentiles

This article describes a method for computing approximate statistics for large data sets, when exact computations may not be feasible. Such situations arise in applications such as climatology, data mining, and information retrieval (search engines). The key to our approach is a modular approximation to the cumulative distribution function (cdf) of the data. Approximate percentiles (as well as many other statistics) can be computed from this approximate cdf. This enables the reduction of a potentially overwhelming computational exercise into smaller, manageable modules. We illustrate the properties of this algorithm using a simulated data set. We also examine the approximation characteristics of the approximate percentiles, using a von Mises functional type approach. In particular, it is shown that the maximum error between the approximate cdf and the actual cdf of the data is never more than 1% (or any other preset level). We also show that under assumptions of underlying smoothness of the cdf, the approximation error is much lower in an expected sense. Finally, we derive bounds for the approximation error of the percentiles themselves. Simulation experiments show that these bounds can be quite tight in certain circumstances.     

Approximate Small-Sample Tests of Fixed Effects in Nonlinear Mixed Models

Nonlinear mixed effect models have been studied extensively over several decades, particularly in pharmacokinetic and pharmacodynamic applications. Here, we focus on investigating the performance of commonly applied tests of linear hypotheses about the fixed effect parameters under different approximations to the likelihood function and to the estimated covariance matrix of the estimators. Included are the first-order approximation (FIRO), first-order conditional approximation (FOCE), and Gaussian quadrature approximation (AGQ) estimation methods. There is no straightforward way to mimic the approximations and adjustments taken in linear mixed models, such as the Kackar–Harville–Jeske–Kenward–Roger approach. By simulations, we illustrate the accuracy of p-values for the tests considered here. The observed results indicate that FOCE and AGQ estimation methods outperform FIRO. The test with an adjustment coefficient that takes into consideration the number of sampling units and the number of fixed effect parameters (Gallant-type) seems to perform closest to desirable even for small-sample sizes.