Inverting covariance matrices in unbalanced hierarchical models ∗

Explicit expression for the inverse of the covariance matrix in a linear experiment with unbalanced multiway hierarchical classification of the random effects is obtained.     

中国货币政策的非对称性偏好调控模式及其演变研究

非对称性实现物价稳定和经济增长是央行调控宏观经济的普遍策略，本文基于非对称性福利损失函数，首次通过逆推实现央行时变非对称性偏好调控模式估计。结果表明，央行在调控通胀和通胀波动目标上表现出较强的政策取向，同时，存在厌恶通胀和规避经济收缩的时变非对称性偏好，由此也使得央行具有较强的动机维护实际通胀低于通胀目标以及实际经济增速高于经济增长目标。特别是，央行在厌恶通胀和规避经济衰退上的非对称性偏好呈现出较强的此消彼长关系。此外，近年来央行厌恶通胀的非对称性偏好持续走强，而规避经济收缩的非对称性偏好却逆向变弱，表明我国货币政策具有较强的治理通胀偏好，但维持经济增速高于增速目标的政策取向却有所减弱。     

The particle filter based on random number searching algorithm for parameter estimation

This article addresses the issue of parameter estimation in linear system in the presence of Gaussian noises, under which the random number searching algorithm (LJ (Luus and Jaakola) algorithm) is combined with the Rao-Blackwellised particle filter (RBPF) algorithm. This yields the so-called RBPF algorithm based on LJ (RBPF-LJ). Unlike the mature alternatives of generic particle filter, the parameter particles of RBPF-LJ are set as random numbers that search in the parameter value scope, which is regulated based on the estimation result to track the changes of the unknown parameter. The contrasting simulations show that the proposed RBPF-LJ outperform the RBPF as well as the particle filter based on kernel smoothing contraction algorithm on the estimation of the dynamically linear or nonlinear parameter and it can obtain the similar estimation results on the static parameter if some coefficients are regulated.     

A new estimator of covariance matrix

The problem of estimating a covariance matrix is considered in this paper. Using the so-called partial Iwasawa coordinates of the covariance matrix, a new improved estimator dominating the James-Stein estimator is proposed. The results of a simulation study verifies that the new estimator provides a substantial improvement in risk under Stein's loss.     

Longitudinal data analysis based on generalized linear partially varying-coefficient models

In this article, we consider a generalized linear partially varying-coefficient model for longitudinal data analysis. A local quasi-likelihood method is proposed to estimate the constant-coefficient and varying-coefficient functions simultaneously based on the local polynomial kernel regression. The corresponding standard error estimates are derived. Large sample properties are investigated. The proposed methodologies are demonstrated by extensive simulation studies and a real example.     

Optimal bivariate step-stress accelerated life test for Type-I hybrid censored data

This paper presents a step-stress accelerated life test for two stress variables to obtain optimal hold times under a Type-I hybrid censoring scheme. An exponentially distributed life and a cumulative exposure model are assumed. The maximum-likelihood estimates are given, from which the asymptotic variance and the Fisher information matrix are obtained. The optimal test plan is determined for each combination of stress levels by minimizing the asymptotic variance of reliability estimate at a typical operating condition. Finally, simulation results are discussed to illustrate the proposed criteria. Simulation results show that the proposed optimum plan is robust, and the initial estimates have a small effect on optimal values.     

Statistical analysis of the Lomax–Logarithmic distribution

In this paper we introduce a three-parameter lifetime distribution following the Marshall and Olkin [New method for adding a parameter to a family of distributions with application to the exponential and Weibull families. Biometrika. 1997;84(3):641–652] approach. The proposed distribution is a compound of the Lomax and Logarithmic distributions (LLD). We provide a comprehensive study of the mathematical properties of the LLD. In particular, the density function, the shape of the hazard rate function, a general expansion for moments, the density of the rth order statistics, and the mean and median deviations of the LLD are derived and studied in detail. The maximum likelihood estimators of the three unknown parameters of LLD are obtained. The asymptotic confidence intervals for the parameters are also obtained based on asymptotic variance–covariance matrix. Finally, a real data set is analysed to show the potential of the new proposed distribution.     

基于谐波特征矩阵的负载识别

给出一种基于电流谐波特性矩阵的负载识别方法，并利用二进制数独立不重复的特性，给出了求多个数据随机组合计算值的算法。此方法首先采样计算各单一负载的谐波特性，利用随机组合计算算法得到各种负载组合后的谐波特性矩阵。之后实时地计算当前电网中的电流谐波值，并将此值与谐波特性矩阵中的数据进行比对，与吻合数据对应的负载即为当前工作在电网中的负载。此方法在试验中取得了很好的效果。     

Optimal sample size allocation for accelerated life test under progressive type-II censoring with competing risks

In this article, we study the optimization problem of sample size allocation when the competing risks data are from a progressive type-II censoring in a constant-stress accelerated life test with multiple levels. The failure times of the individual causes are assumed to be statistically independent and exponentially distributed with different parameters. We obtain the estimates of the unknown parameters through a maximum likelihood method, and also derive the Fisher information matrix. We propose three optimization criteria and two search scenarios to obtain the sample size allocation at each stress level. Some numerical results are studied to illustrate the usage of the proposed methods.