A note on minimum average risk estimators for coefficients in linear models

In this note, we have derived a set of necessary and sufficient conditions for the biased estimators analyzed by Swamy and Mehta (1976) to be better than the generalized least squares estimator of the coefficient vector in a standard linear regression model.     

Estimation for the three-parameter inverse gaussian distribution

The three-parameter inverse Gaussian distribution is defined and moment estimators and maximum likelihood estimators are obtained. The moment estimators are found in closed form and their asymprotic normality is proven. A sufficient condition is provided for the existence of the maximum likelihood estimators.     

A note on the completeness of the normal family with constant coefficient of variation

Suppose that Z₁ …Zⁿ are independent and normally distributed with common mean u and variance σ².When σ² = λμ²with given λ> 0, it is well known that the minimal sufficient statistic is not complete for μ if n ≥ 2. The question of completeness seems to be unresolved when n = 1. In this note, we consider this case and, by using the heat equation, establish the second moment completeness of the minimal sufficient statistic, a property which is weaker than completeness but stronger than bounded completeness. Finally, we apply this result to a linear calibration problem.     

Correcting for non-compliance in randomized trials using structural nested mean models

In a randomized trial designed to study the effect of a treatment of interest on the evolution of the mean of a time-dependent outcome variable, subjects are assigned to a treatment regime, or, equivalently, a treatment protocol. Unfortunately, subjects often fail to comply with their assigned regime. From a public health point of view, the causal parameter of interest will often be a function of the treatment differences that would have been observed hadcontrary to fact, all subjects remained on protocol. This paper considers the identification and estimation of these treatment differences based on a new class of structural models, the multivariate structural nested mean models, when reliable estimates of each subject's actual treatment are available. Estimates of “actual treatment” might, for example, be obtained by measuring the amount of “active drug” in the subject's blood or urine at each follow-up visit or by pill counting techniques. In addition, we discuss a natural extension of our methods to observational studies.     

Asymptotics of the run lengths of two control charts

The CUSUM control chart proposed by Page is a widely used in monitoring the quality of manufacturing processes. The Shiryayev-Roberts (S-R) control chart due to Shiryayev (1963) and Roberts (1988) is one of its competitors, This paper is concerned with the distribution properties of the run lengths of these two control charts. In context of continuous time, we first give the expansions of the higher moments of these run lengths. Then, we show that the asymptotic distributions of these run lengths are either some exponential distributions, or the distribution of the suprema of a standard Brownian motion, or some normal distributions, according to whether the μ<δ/2,μ =δ/2 and μ>δ/2. Here δ is the reference value of the above charts. Some similar results are also obtained in the context of discrete time.     

The beta log-normal distribution

For the first time, we introduce the beta log-normal (LN) distribution for which the LN distribution is a special case. Various properties of the new distribution are discussed. Expansions for the cumulative distribution and density functions that do not involve complicated functions are derived. We obtain expressions for its moments and for the moments of order statistics. The estimation of parameters is approached by the method of maximum likelihood, and the expected information matrix is derived. The new model is quite flexible in analysing positive data as an important alternative to the gamma, Weibull, generalized exponential, beta exponential, and Birnbaum–Saunders distributions. The flexibility of the new distribution is illustrated in an application to a real data set.     

卷取机张力控制中转动惯量测量模型的建立

从卷取机结构出发,根据动力学方程推导了转动惯量的测量模型,利用该模型可以计算出动态补偿力矩,从而使整个卷取过程中张力平稳。并用VB6.0开发了相应的微机控制系统,利用该系统可以方便快捷地完成对转动惯量的实时测量。     

一种单视广义极化K-分布纹理参数的估计方法

提出了通过归一化极化联合矩对SAR（合成孔径雷达）单视广义极化K-分布纹理参数进行估计的方法,阐述了SAR各个极化通道之间的纹理相关性。在此基础上给出了单视广义极化K-分布波形因子、纹理相关系数和相干斑相关系数的算法及偏差性能分析,并进行了蒙特卡洛仿真实验论证,进一步证明了该方法的良好估计性能。     

基于幂律型分布的动态VaR模型及实证研究

针对金融资产回报时间序列的尖峰厚尾性和波动集聚性,提出了基于AR(1)-GARCH(1,1)模型与幂律型分布相结合计算VaR的方法。用GARCH模型对时间序列建模刻画波动集聚性,用基于幂律型分布的扩展形式拟合GARCH模型的残差分布尾部,刻画回报时间序列的厚尾特征,二者结合更好地描述回报时序的动态波动现象。对上证综指进行实证分析,结果表明本文提出的方法比基于正态分布的GARCH模型和静态幂律尾法更精确。     

力矩电机调速系统

介绍一种力矩电机调速系统，阐述了系统原理、构成，以及为提高系统性能所采取的一系列措施，所构成的系统已经投入实际运行。