节流流量计测量脉动气流平均流量的研究

在用节流流量计测量周期性脉动气流的平均流量时,需要在脉源与流量计之间添加稳压箱以减弱脉动对流量计的影响。本文以稳压箱为控制体建立基本方程,对其无量纲化后进行数值求解,并用准则方程式予以拟合,数值计算结果表明,按本文方程得到的曲线与ＩＳＯ／ＴＲ３３１３中提供的实验数据的吻合程序优于该技术报告中的理论分析结果与实验数据的吻合程度。     

现金流量表准则的国际比较

《企业会计准则——现金流量表》的颁布与实施 ,是我国会计顺应世界会计发展潮流的又一重大举措。本文试就该准则与美国、英国及国际会计准则委员会等西方会计规范进行比较 ,借以透视我国的现金流量表准则     

高等学校规模定位的经济分析

文章根据“木桶原理”,采用类比方法、边际分析方法和数学模型方法,从成本—收益的角度讨论了学校的合理发展规模问题;提出了一个决策分析模式和一个适度规模的判别准则,并给出了一个案例。      

一种基于最小二乘准则的自适应滤波算法

基于最注二乘准则Ｊ（ｎ）＝　　　　　,利用最徒梯度下降法,得到一种新的梯度型自适应滤波算法．该算法避免了递推最小二乘ＲＬＳ（Ｒｅｃｕｒｓｉｖｅ　Ｌｅａｓｔ　Ｓｑｕａｒｅｓ）算法需递推估计更新自相关矩阵Ｒｘｘ（ｎ）的逆的不足,计算机模拟仿真结果表明该算法有良好的收敛性能,收敛速度快于ＬＭＳ（Ｌｅａｓｔ Ｍｅａｎ　Ｓｑｕａｒｅｓ）算法、ＮＬＭＳ（ＮｏｒｍａｌｉｚｅｄＬｅａｓｔ　Ｓｑｕａｒｅｓ）算法和ＲＬＳ算法．     

独立审计准则法律地位的经济学分析

2006年独立审计准则的颁布,对注册会计师执业提出了新的要求。注册会计师遵循独立审计准则,是否可以作为判断注册会计师应否承担某种不利后果或赔偿民事责任的标准?对这一问题的认识,会计界与法律界存在一定的分歧。从经济学的角度分析注册会计师法律责任的归责原则,可以进一步明确独立审计准则的法律地位。     

A pre-test like estimator dominating the least-squares method

We develop a pre-test type estimator of a deterministic parameter vector β$\mathit{\beta }$ in a linear Gaussian regression model. In contrast to conventional pre-test strategies, that do not dominate the least-squares (LS) method in terms of mean-squared error (MSE), our technique is shown to dominate LS when the effective dimension is greater than or equal to 4. Our estimator is based on a simple and intuitive approach in which we first determine the linear minimum MSE (MMSE) estimate that minimizes the MSE. Since the unknown vector β$\mathit{\beta }$ is deterministic, the MSE, and consequently the MMSE solution, will depend in general on β$\mathit{\beta }$ and therefore cannot be implemented. Instead, we propose applying the linear MMSE strategy with the LS substituted for the true value of β$\mathit{\beta }$ to obtain a new estimate. We then use the current estimate in conjunction with the linear MMSE solution to generate another estimate and continue iterating until convergence. As we show, the limit is a pre-test type method which is zero when the norm of the data is small, and is otherwise a non-linear shrinkage of LS.     

A constrained optimum experimental design problem for model discrimination with a continuously varying factor

We present the theoretical background and the numerical procedure for calculating optimum experimental designs for non-linear model discrimination in the presence of constraints. The design support points consist of two kinds of factors: a continuous function of time and discrete levels of other quantitative factors. That is, some of the experimental conditions are allowed to continually vary during the experimental run. We implement the theory in a chemical kinetic model discrimination problem.     

On Bayesian sample size determination

Three Bayesian methods are considered for the determination of sample sizes for sampling from the Laplace distribution – the distribution of time between rare events – with a normal prior. These methods are applied to the sizing of aircraft mid-air collisions in a navigation system or large flight path deviations of aircraft in air traffic management scenarios. A computer program handles all computations and gives a good insight about the best suggested method.     

Evaluation of realized volatility predictions from models with leptokurtically and asymmetrically distributed forecast errors

Accurate volatility forecasting is a key determinant for portfolio management, risk management and economic policy. The paper provides evidence that the sum of squared standardized forecast errors is a reliable measure for model evaluation when the predicted variable is the intra-day realized volatility. The forecasting evaluation is valid for standardized forecast errors with leptokurtic distribution as well as with leptokurtic and asymmetric distributions. Additionally, the widely applied forecasting evaluation function, the predicted mean-squared error, fails to select the adequate model in the case of models with residuals that are leptokurtically and asymmetrically distributed. Hence, the realized volatility forecasting evaluation should be based on the standardized forecast errors instead of their unstandardized version.     

Shared gamma frailty models based on reversed hazard rate for modeling Australian twin data

In this paper, we consider shared gamma frailty model with the reversed hazard rate (RHR) with two different baseline distributions, namely the generalized inverse Rayleigh and the exponentiated Gumbel distributions. With these two baseline distributions we propose two different shared frailty models. We develop the Bayesian estimation procedure using Markov Chain Monte Carlo technique to estimate the parameters involved in these models. We present a simulation study to compare the true values of the parameters with the estimated values. A search of the literature suggests that currently no work has been done for these two baseline distributions with a shared gamma frailty with the RHR so far. We also apply these two models by using a real life bivariate survival data set of Australian twin data given by Duffy et a1. (1990) and a better model is suggested for the data.