Selecting Closest to Control

Consider the usual one-way fixed effect analysis of variance model where the populations Π_i ( I = 0, 1, . . . , k ) have independent normal distributions with unknown means and common unknown variance. Let Π₀ be a control population with which the other (treatment) populations are to be compared. The basic problem is to select the treatment that is closest to the control mean. This situation occurs when one of the Π_i must be chosen, regardless of how many are equivalent to the control in the sense of having means sufficiently close. This paper follows the approach of Hsu (1996) and is based on a set of simultaneous confidence intervals. It provides a table of critical values which allows direct implementation of the new inference procedure. The applications given are of the balanced cross-over design type with negligible carry-over effects, for which the results of this paper may be used. One of the applications refers to the selection of a drug, which may not be bioequivalent to a reference formulation but is the closest of those drugs that are readily available to the group of patients considered.     

子矩阵约束下矩阵方程XA=B的对称非负定解

利用矩阵的广义逆及奇异值分解 ,给出了子矩阵约束下线性矩阵方程XA =B有对称非负定解的充分必要条件 ,并在有解时 ,给出了相应解的一般表达式     

广义KDV-MKDV方程的精确解

利用直接积分方法将广义KDV-MKDV方程化为一阶变系数非线性常微分方程组,然后用待定系数法确定相应的常数获得了广义KDV-MKDV方程新的精确解;利用先作假设变换后选取试探函数的方法来直接构造广义KDV-MKDV方程新的精确解.     

Estimators for a simultaneous equation model with lagged endogenous variables and autocorrelated errors

Two full information estimators and a limited information estimator for the simultaneous equation model with autocorrelated errors are studies by the Monte Carlo method.The estimators share features of the two-step Gauss-Newton procedure and of Aitken generalized least squares.One full information method generates the estimated endogenous variables used in the later stages of computation from the unrestricted reduced form while the other uses a restricted reduced form.The observed small sample behavior was close to that suggested by asymptotic theory.     

Bayesian autoregressive filter and squared gain estimation

This article describes a Bayesian small sample approach to making inferences for the operator (or filter) and squared gain of a p — th order Gaussian univariate autoregressive process. Simultaneous pos¬terior probability bands are developed for the real and the imaginary parts of the frequency-response function of an autoregressive operator as well as for the squared gain of an autoregressive process.     

Analysis of spatial frailty models by a weighted estimating equation

Spatially correlated survival data are frequently observed in ecological and epidemiological studies. An assumption in the clustered survival models is inter-cluster independence, which may not be adequate to model the dependence in spatial settings. For survival data, the likelihood function based on a spatial frailty may be complicated. In this paper, we develop a weighted estimating equation for spatially right-censored data. Some large sample properties for the estimate are developed. We also conduct simulations to compare estimation performance with other methods. A data set from a study of forest decline in Wisconsin is used to illustrate the proposed method.     

Estimation of binomial parameters when both , are unknown

We revisit the classic problem of estimation of the binomial parameters when both parameters n,p are unknown. We start with a series of results that illustrate the fundamental difficulties in the problem. Specifically, we establish lack of unbiased estimates for essentially any functions of just n or just p. We also quantify just how badly biased the sample maximum is as an estimator of n. Then, we motivate and present two new estimators of n. One is a new moment estimate and the other is a bias correction of the sample maximum. Both are easy to motivate, compute, and jackknife. The second estimate frequently beats most common estimates of n in the simulations, including the Carroll–Lombard estimate. This estimate is very promising. We end with a family of estimates for p; a specific one from the family is compared to the presently common estimate and the improvements in mean-squared error are often very significant. In all cases, the asymptotics are derived in one domain. Some other possible estimates such as a truncated MLE and empirical Bayes methods are briefly discussed.     

基于SEM方法的PPP项目绩效影响因素分析

在综合考虑PPP项目中的独立监管机构、私营机构、政府部门和社会公众等利益目标的基础上,提出了9个测量PPP项目绩效的关键指标。对现有的PPP项目绩效影响因素进行了分析比较,识别出PPP项目绩效的3大影响因素和18个可测量的子因素,并构建了分析PPP项目绩效影响因素的结构方程模型。结果表明,PPP项目的特征因素与项目绩效的好坏有直接的关系,项目环境和项目参与人因素的影响相对较弱,参与方的满意度、项目的功能和质量对PPP项目绩效最具有解释能力。     

有关椭球面轨迹问题的研究

对有关椭球面的轨迹问题作了研究,得出了两个重要的定理。     

SEMIPARAMETRIC ESTIMATION OF THE ERROR DISTRIBUTION IN MULTIVARIATE REGRESSION USING COPULAS

A semiparametric method is developed to estimate the dependence parameter and the joint distribution of the error term in the multivariate linear regression model. The nonparametric part of the method treats the marginal distributions of the error term as unknown, and estimates them using suitable empirical distribution functions. Then the dependence parameter is estimated by either maximizing a pseudolikelihood or solving an estimating equation. It is shown that this estimator is asymptotically normal, and a consistent estimator of its large sample variance is given. A simulation study shows that the proposed semiparametric method is better than the parametric ones available when the error distribution is unknown, which is almost always the case in practice. It turns out that there is no loss of asymptotic efficiency as a result of the estimation of regression parameters. An empirical example on portfolio management is used to illustrate the method.