Robust Priors in Nonlinear Panel Data Models

Many approaches to estimation of panel models are based on an average or integrated likelihood that assigns weights to different values of the individual effects. Fixed effects, random effects, and Bayesian approaches all fall into this category. We provide a characterization of the class of weights (or priors) that produce estimators that are first‐order unbiased. We show that such bias‐reducing weights will depend on the data in general unless an orthogonal reparameterization or an essentially equivalent condition is available. Two intuitively appealing weighting schemes are discussed. We argue that asymptotically valid confidence intervals can be read from the posterior distribution of the common parameters when N and T grow at the same rate. Next, we show that random effects estimators are not bias reducing in general and we discuss important exceptions. Moreover, the bias depends on the Kullback–Leibler distance between the population distribution of the effects and its best approximation in the random effects family. Finally, we show that, in general, standard random effects estimation of marginal effects is inconsistent for large T, whereas the posterior mean of the marginal effect is large‐T consistent, and we provide conditions for bias reduction. Some examples and Monte Carlo experiments illustrate the results.     

Some aspects of the reparametrization of statistical models

Definitions are given for orthogonal parameters in the context of Bayesian inference and likelihood inference. The exact orthogonalizing transformations are derived for both cases, and the connection between the two settings is made precise. These parametrizations simplify the interpretation of likelihood functions and posterior distributions. Further, they make numerical maximization and integration procedures easier to apply. Several applications are studied.     

单模光纤的极化模式色散及其测量

本文分析了实用单模光纤的极化模式色散及其测量方法。在实用单模光纤中,由于双折射现象导致两个正交极化模式在传榆过程中改变极化方向并产生时延差即色散,从而使光波脉冲展宽,产生误码,因此限制了光纤的通道容量和传输距离。     

Ng10二通插装阀结构形状及参数的试验研究

本文提出了测量Ng10二通插装阀静,动态性能的试验方法,测试了阀套开口形状、各种尾部结构的阀芯、阀芯行程、阀芯锥度、控制盖板阻尼孔尺寸等结构形状参数对插装阀静、动态性能的影响,并对试验结果进行了对比分析.在此基础上.确定了本文述及条件下的最佳结构形状和参数.     

On the asymptotic power and relative efficiency of the frequency X2 test

The asymptotic (Pitman) power of the X² test is investigated for particular classes of alternatives. A simple rule is introduced to identify ‘orthogonal alternatives’, for which the noncentrality parameter can be computed in a very simple way. In the sequel, restricted alternatives are considered and the ARE of the unrestricted test w.r.t. the restricted one is shown to depend only on the numbers of degrees of freedom. The concluding section discusses ‘undetectable alternatives’, i.e. alternatives for which the noncentrality vanishes.     

Jackknifing in Categorical Data Analysis

Estimation of nonlinear functions of a multinomial parameter vector is necessary in many categorical data problems. The first and second order jackknife are explored for the purpose of reduction of bias. The second order jackknife of a function g(.) of a multinomial parameter is shown to be asymptotically normal if all second order partials ?²g( p )?dp_i?p_j obey a Hölder condition with exponent α>1/2. Numerical results for the estimation of the log odds ratio in a 2times2 table demonstrate the efficiency of the jackknife method for reduction of mean-square-error and the construction of approximate confidence intervals.     

不确定需求下二级供应链中的鲁棒定价问题

对由一个制造商和一个零售商组成的单一产品二级供应链,在市场潜在需求和价格敏感系数不确定的情况下,分析制造商和零售商的鲁棒定价决策。首先考虑了零售商掌握完全信息而制造商仅已知各参数区间的不对称信息情形,建立Stackelberg鲁棒博弈模型并对模型的均衡解进行探讨,进一步通过对解的分析和对比讨论了不对称信息下零售商的决策优势。然后研究了制造商和零售商都仅已知各参数区间的不完全信息情形,给出Stackelberg鲁棒博弈模型,求得其均衡解并与完全信息下的情形以及不对称信息下的情形进行比较,分别得到制造商和零售商在不完全信息下所获得的利润比在完全信息下所获得利润高的条件,并证明了制造商在不完全信息下所获得的利润比在不对称信息下获得的利润高,而零售商则刚好相反。最后给出算例分析,对所得到的解和结论做一些补充。     

基于随机Petri网的生产提前期牛鞭效应测度研究

利用随机Petri网结合三角模糊参数,对产品生产的三工艺阶段生产周期的波动进行测定,得出前工艺阶段生产周期的波动造成后工艺阶段生产周期更大的波动,且波动逐级传递。证实了生产提前期也具有类似供应链中的牛鞭效应的现象,对不确定因素导致生产周期波动的测度提供了一种新的思路和方法。     

带通滤波器的计算新思路

用一种新的思路和方法 ,先计算低通、再计算高通滤波器的有关参数 ,然后组合成带通滤波器     

Measuring the complexity of generalized linear hierarchical models

Measuring a statistical model's complexity is important for model criticism and comparison. However, it is unclear how to do this for hierarchical models due to uncertainty about how to count the random effects. The authors develop a complexity measure for generalized linear hierarchical models based on linear model theory. They demonstrate the new measure for binomial and Poisson observables modeled using various hierarchical structures, including a longitudinal model and an areal‐data model having both spatial clustering and pure heterogeneity random effects. They compare their new measure to a Bayesian index of model complexity, the effective number p_D of parameters (Spiegelhalter, Best, Carlin & van der Linde 2002); the comparisons are made in the binomial and Poisson cases via simulation and two real data examples. The two measures are usually close, but differ markedly in some instances where p_D is arguably inappropriate. Finally, the authors show how the new measure can be used to approach the difficult task of specifying prior distributions for variance components, and in the process cast further doubt on the commonly‐used vague inverse gamma prior.