Generalized Weibull Linear Models

For the first time, a new class of generalized Weibull linear models is introduced to be competitive to the well-known generalized (gamma and inverse Gaussian) linear models which are adequate for the analysis of positive continuous data. The proposed models have a constant coefficient of variation for all observations similar to the gamma models and may be suitable for a wide range of practical applications in various fields such as biology, medicine, engineering, and economics, among others. We derive a joint iterative algorithm for estimating the mean and dispersion parameters. We obtain closed form expressions in matrix notation for the second-order biases of the maximum likelihood estimates of the model parameters and define bias corrected estimates. The corrected estimates are easily obtained as vectors of regression coefficients in suitable weighted linear regressions. The practical use of the new class of models is illustrated in one application to a lung cancer data set.     

Comments

In this paper we compare Bartlett-corrected, bootstrap, and fast double bootstrap tests on maximum likelihood estimates of cointegration parameters. The key result is that both the bootstrap and the Bartlett-corrected tests must be based on the unrestricted estimates of the cointegrating vectors: procedures based on the restricted estimates have almost no power. The small sample size bias of the asymptotic test appears so severe as to advise strongly against its use with the sample sizes commonly available; the fast double bootstrap test minimizes size bias, while the Bartlett-corrected test is somehow more powerful.     

On shrinkage m-estimators of location parameters

For a general class of continuous ( and marginally symmetric ) inultivariate distributions, based on suitable M-statistics ( involving bounded but possibly discontinuous score generating functions), shrinkage estimators of location are considered. These estimators are based on the James-Stein type rule and incorporates the idea of preliminary test estimation too. The main emphasis is laid on the study of asymptotic tdistributional ) risk properties of these est-innators, and asymptotic tin-) adraissibility results are also studied under fairly general regularity conditions.     

BOOST－PFC电路反馈环节的优化设计

介绍了平均电流控制ＢＯＯＳＴ─ＰＦＣ电路的原理，建立了电流环和电压环的小信号简化模型，讨论了双环反馈环节的设计原则和方法，提出最优设计的思想和数学模型，并以一种ＢＯＯＳＴ─ＰＦＣ电路为例进行优化设计，计算模拟结果显示优化后有关指标好于优化前。     

On Asymptotic Strategy-Proofness of Classical Social Choice Rules

We show that, when the number of participating agents n tends to infinity, all classical social choice rules are asymptotically strategy-proof with the proportion of manipulable profiles being of order O (1/n).     

宏观经济指标与上证综指长短期走势的预测

上证综合指数和宏观经济指标,如广义货币供应量、国内生产总值、出口与进口的比值,以及外商直接投资之间存在长期的均衡关系,可以通过宏观经济指标来预测上证综合指数的长期走势。误差修正模型的估计结果表明,上证综合指数具备经济"晴雨表"的功能。     

人才招聘中信息不对称矫正技术的研究

由于信息不对称到处存在,往往会引起人才招聘中的逆向选择,甚而会造成整个招聘市场的瘫痪;克服信息不对称的“自动亮相法”“可信传递信号法”“堆积岗位对号入座法”等各类矫正技术,使招聘者不仅象伯乐能识“千里马”,而且还更胜一筹,让“千里马”自己跑出来自动显示“私人信息”。即使对“千里马”也要进行必要的考察,特别是有必要进行“组织文化”的认同,使他真正成为“组织的千里马”。     

王利器"范注"订补柬释

范文澜的<文心雕龙注>1929～1931年由北平文化学社分上、中、下3册出版,1936年上海开明书店出版7册线装本,至此"范注"基本定型.1958年经作者又一次核对订正,人民文学出版社分2册重印,这就是现在流行的本子.人们一直以为"范注"由开明书店本到人民文学本的修订工作就是范老本人做的.然而,据王利器回忆,他曾担任范文澜<文心雕龙注>重版的责任编辑,在整理过程中,他订补了五百多条注文.笔者从增补、订正、补充、厘正4个方面,对王利器所做的"订补"进行考辨,以期澄清事实真相.     

On shrinkage least squares estimation in a parallelism problem

In a multi-sample simple regression model, generally, homogeneity of the regression slopes leads to improved estimation of the intercepts. Analogous to the preliminary test estimators, (smooth) shrinkage least squares estimators of Intercepts based on the James-Stein rule on regression slopes are considered. Relative pictures on the (asymptotic) risk of the classical, preliminary test and the shrinkage least squares estimators are also presented. None of the preliminary test and shrinkage least squares estimators may dominate over the other, though each of them fares well relative to the other estimators.     

On asymptotic distribution of the test statistic for the mean of the non-isotropic principal component

In this paper, the authors derived the large sample distribution of the t statistic based upon the observations on the first principal component instead of the original variables. It is shown that the above statistic is distributed asymptotically as Student's t distribution.