泰乐松注射液的毒理学试验研究

为提供泰乐松注射液的安全性毒理学评价资料,本试验采用改进Karber法测定小鼠LD_(50);以剂量递增法测定小鼠蓄积性和耐受性;分别以剂量340、130、30mg／kg为3个试验组．设阴性和阳性对照组进行小鼠骨髓微核试验;分别以剂量500、250、120mg／kg为3个剂量组．设阴性和阳性对照组进行小鼠精于畸形试验。结果表明：其LD_(50)为669．80mg／kg,蓄积系数大于5．3,无明显蓄积毒性和耐受性（P＞0．05）,小鼠骨髓微核试验和精子畸形试验结果均为阴性。     

多载波CDMA在Rician衰落信道的性能研究

在一种同步ＯＦＤＭＣＤＭＡ的系统模型基础上,对频率非选择性Ｒｉｃｉａｎ衰落信道的误码率性能进行了研究,分析和数值结果说明由于多径的存在系统误码率性能迅速下降,而且当直射成分很小时,信道为Ｒａｙｌｅｉｇｈ信道,系统性能变为最差。     

沪市认购权证与其标的股票价格走势的Granger因果检验

运用G ranger因果检验的方法,检验沪市6只认购权证与其标的股票之间的因果关系。分析结果表明:在大样本条件下,认购权证与其标的股票之间存在单向的G ranger因果关系,权证的价格走势影响其标的股票的价格走势,说明从长期来看,认购权证发挥了其潜在的投资杠杆的作用,其价值发现与风险对冲功能在市场中得到实现;在小样本条件下,认购权证与其标的股票之间不存在显著的G ranger因果关系,表明短期内权证与其标的股票价格走势相互独立,市场中有可能存在对权证投机炒作,人为放大权证的投资风险,使权证背离其正常的投资价值。     

Testing Inference in Inflated Beta Regressions under Model Misspecification

We consider testing inference in inflated beta regressions subject to model misspecification. In particular, quasi-z tests based on sandwich covariance matrix estimators are described and their finite sample behavior is investigated via Monte Carlo simulations. The numerical evidence shows that quasi-z testing inference can be considerably more accurate than inference made through the usual z tests, especially when there is model misspecification. Interval estimation is also considered. We also present an empirical application that uses real (not simulated) data.     

大型低速重载径向动压轴承研究

为了开发大型贯流式水轮发电机组径向轴承,在试验台上采用了360的模型轴承对1740可倾6瓦块原型轴承进行模拟试验研究,最高试验比压3.37 MPa、最低线速度3.77 m/s、最高线速度22.62 m/s。试验表明:载荷、进油压力、转速、进油温度和安装间隙比等,对轴承油温升、油流量、功耗、瓦温及油膜厚度等都存在不同程度的影响,其中载荷和转速的影响比较显著。按照模型轴承指导设计的红岩子机组1740轴承已在电站可靠运行,同时开发了径向轴承热弹流计算程序,可以预测径向轴承的油膜压力、油膜温度、油膜厚度、损耗、流量等参数。     

On comparing,classifying and clustering several dependent regression models

Classifying several regression models fitted on a dataset is one of the most problems in data analysis. In other words, scientists are interested in comparing several regression models that can be used for a dataset. In this paper, an approach will be used to compare and classify several dependent regression models. Then the performance of the proposed method is investigated using simulation study and real example.     

Influence Diagnostics on Testing Linear Hypothesis in Linear Models with Correlated Errors

To assess the influence of observations on the parameter estimates, case deletion diagnostics are commonly used in linear regression models. For linear models with correlated errors we study the influence of observations on testing a linear hypothesis using single and multiple case deletions. The change in likelihood ratio test and F test theoretically is derived and it is shown these tests to be completely determined by two proposed generalized externally studentized residuals. An illustrative example of a real data set is also reported.     

Estimation of variance in bivariate normal distribution after the preliminary test of homogeneity

A preliminary test estimator of variance in the bivariate normal distribution is proposed after the Pitman–Morgan test of homogeneity of two variances. The bias and mean square error of the estimator are derived. The relative efficiency (RE) of the preliminary test estimator is studied. Computations and 3D graphs of RE for different parameters are analyzed. In order to get the maximum RE, recommendations of the significance level for the preliminary test are given for various sample sizes by using the max–min criterion.     

An algorithm for maximum likelihood estimation in incomplete block variance component models

In the recovery of interblock information to improve the treatment differences estimates in incomplete block designs, the parameter p is usually unknown. Many authors have worked on the problem of estimating it and of studying its properties together with the properties of the treatment differences estimates. In this paper a numerically efficient algorithm is developed which yields the maximum likelihood estimates (MLE) of all the parameters in the mixed incomplete block design model (treatment effects, ρ and variance)     

Decomposition of the main effects and interaction term by using orthogonal polynomials in multiple non symmetrical correspondence analysis

The multiple non symmetric correspondence analysis (MNSCA) is a useful technique for analyzing a two-way contingency table. In more complex cases, the predictor variables are more than one. In this paper, the MNSCA, along with the decomposition of the Gray–Williams Tau index, in main effects and interaction term, is used to analyze a contingency table with two predictor categorical variables and an ordinal response variable. The Multiple-Tau index is a measure of association that contains both main effects and interaction term. The main effects represent the change in the response variables due to the change in the level/categories of the predictor variables, considering the effects of their addition, while the interaction effect represents the combined effect of predictor categorical variables on the ordinal response variable. Moreover, for ordinal scale variables, we propose a further decomposition in order to check the existence of power components by using Emerson's orthogonal polynomials.