Sequential estimation of the sum of sinusoidal model parameters

Estimating the parameters of the sum of a sinusoidal model in presence of additive noise is a classical problem. It is well known to be a difficult problem when the two adjacent frequencies are not well separated or when the number of components is very large. In this paper we propose a simple sequential procedure to estimate the unknown frequencies and amplitudes of the sinusoidal signals. It is observed that if there are p components in the signal then at the k  th (k?p)$(k?p)$ stage our procedure produces strongly consistent estimators of the k   dominant sinusoids. For k>p$k>p$, the amplitude estimators converge to zero almost surely. Asymptotic distribution of the proposed estimators is also established and it is observed that it coincides with the asymptotic distribution of the least squares estimators. Numerical simulations are performed to observe the performance of the proposed estimators for different sample sizes and for different models. One ECG data and one synthesized data are analyzed for illustrative purpose.     

Mixed linear regression with equi-cross-correlated errors

Received: August 12, 1999; revised version: April 3, 2000     

A second look at Krackhardt's graph theoretical dimensions of informal organizations

Krackhardt (1994) proposed four dimensions to describe and measure the amount of hierarchy in networks of informal organizations. We examine these conditions, suggest some relaxations and prove that they are both necessary and sufficient to guarantee an arborescence (or out-tree). In addition we suggest situations some of which are outside of informal organizations in which fewer of the conditions can be used to capture the hierarchical tree structure.     

上海顾客满意度指数模型及其稳健性研究

采用结构方程组方法,建立了上海顾客满意度指数基础测量模型。并通过对一个简化模型的蒙特卡罗模拟分析,证明在应用偏最小二乘估计的情况下,这一模型对于结构变动,以及数据质量缺陷是稳健的。     

Autocorrelation and tolerance limits

Tolerance limits are limits that include a specified proportion of the population at a given confidence level. They are used to make sure that the production will not be outside specifications. Tolerance limits are either designed based on the normality assumption, or nonparametric tolerance limits are established. In either case, no provision for autocorrelated processes is made in the available design tables of tolerance limits. It is shown how to construct tolerance limits to cover a specified proportion of the population when autocorrelation is present in the process. A comparison of four different tolerance limits is provided, and recommendations are given for choosing the "best" estimator of the process variability for the construction of tolerance limits.     

Percentage points of the joint distribution of the extreme roots of the random matrix S1(S1+S2)−1

The numerical treatment of nonlinear model fitting problems often can be simplified by manipulating the model equations. Algebraic manipulations including nonlinear transformations of model parameters, do not change the numerical result of the adjustment and can be a powerful method to improve the performance of solution algorithms. Nonlinear transformations of the observations, on the other hand, do change the numerical results unless the normal equations are transformed accordingly. The latter transformation has been neglected by previous authors and this article provides a complete set of formulas that are needed to implement transformations of observations. The transformations are in general less useful than parameter transformations for the improvement of algorithms but may have other applications in particular situations.     

A relative error-based estimation with an increasing number of parameters

The least product relative error (LPRE) estimator and test statistic to test linear hypotheses of regression parameters in the multiplicative regression model are studied when the number of covariate variables increases with the sample size. Some properties of the LPRE estimator and test statistic are obtained such as consistency, Bahadur presentation, and asymptotic distributions. Furthermore, we extend the LPRE to a more general relative error criterion and provide their statistical properties. Numerical studies including simulations and two real examples show that the proposed estimation performs well.     

农民工参与就业培训的决定因素及对收入影响的实证分析

笔者利用在7个省市的调研数据,利用离散选择模型分析农民工参与就业培训的影响因素;同时,运用平均处理效应估计方法,考察参与就业培训对农民工收入水平的影响。分析结果说明,就业培训有助于提高农民工在就业市场的竞争力,提高农民工的收入水平。但这需要增加资金投入,加大宣传力度,正确引导营利性培训机构的发展。     

Population Marginal Means in the Linear Model: An Alternative to Least Squares Means

The parameter concept in the term least squares mean is defined and given the more meaningful name population marginal mean; and its estimation is discussed.     

A generalized least squares estimation method for VARMA models

In this paper a new generalized least squares procedure for estimating VARMA models is proposed. This method differs from existing ones in explicitly considering the stochastic structure of the approximation error that arises when lagged innovations are replaced with lagged residuals obtained from a long VAR. Simulation results indicate that this method performs better than the Double Regression method and similar to Mauricio's (1995) exact maximum likelihood estimation procedure.