Towards Uniformly Efficient Trend Estimation Under Weak/Strong Correlation and Non‐stationary Volatility

In this paper, we consider the deterministic trend model where the error process is allowed to be weakly or strongly correlated and subject to non‐stationary volatility. Extant estimators of the trend coefficient are analysed. We find that under heteroskedasticity, the Cochrane–Orcutt‐type estimator (with some initial condition) could be less efficient than Ordinary Least Squares (OLS) when the process is highly persistent, whereas it is asymptotically equivalent to OLS when the process is less persistent. An efficient non‐parametrically weighted Cochrane–Orcutt‐type estimator is then proposed. The efficiency is uniform over weak or strong serial correlation and non‐stationary volatility of unknown form. The feasible estimator relies on non‐parametric estimation of the volatility function, and the asymptotic theory is provided. We use the data‐dependent smoothing bandwidth that can automatically adjust for the strength of non‐stationarity in volatilities. The implementation does not require pretesting persistence of the process or specification of non‐stationary volatility. Finite‐sample evaluation via simulations and an empirical application demonstrates the good performance of proposed estimators.     

Inference and optimal design of multiple constant-stress testing for generalized half-normal distribution under type-II progressive censoring

The generalized half-normal (GHN) distribution and progressive type-II censoring are considered in this article for studying some statistical inferences of constant-stress accelerated life testing. The EM algorithm is considered to calculate the maximum likelihood estimates. Fisher information matrix is formed depending on the missing information law and it is utilized for structuring the asymptomatic confidence intervals. Further, interval estimation is discussed through bootstrap intervals. The Tierney and Kadane method, importance sampling procedure and Metropolis-Hastings algorithm are utilized to compute Bayesian estimates. Furthermore, predictive estimates for censored data and the related prediction intervals are obtained. We consider three optimality criteria to find out the optimal stress level. A real data set is used to illustrate the importance of GHN distribution as an alternative lifetime model for well-known distributions. Finally, a simulation study is provided with discussion.     

Random effects regression mixtures for analyzing infant habituation

Random effects regression mixture models are a way to classify longitudinal data (or trajectories) having possibly varying lengths. The mixture structure of the traditional random effects regression mixture model arises through the distribution of the random regression coefficients, which is assumed to be a mixture of multivariate normals. An extension of this standard model is presented that accounts for various levels of heterogeneity among the trajectories, depending on their assumed error structure. A standard likelihood ratio test is presented for testing this error structure assumption. Full details of an expectation-conditional maximization algorithm for maximum likelihood estimation are also presented. This model is used to analyze data from an infant habituation experiment, where it is desirable to assess whether infants comprise different populations in terms of their habituation time.     

Who cares? A lack of self‐care for women in the non‐profit/non‐governmental sector

In this article I examine the lack of self‐care regimes for women working in the non‐profit/non‐governmental sector. While I draw on ethnographic research conducted in the Malaysian context of women's organizations, the issue of self‐care for activists and feminist activists is a global one that crosses borders and boundaries. I explore the gendered nature of care and care professions to demonstrate how women are predominantly affected in these working environments. To date, there has been little scholarship on self‐care and care in non‐profit/non‐governmental working environments. Using interviews with women working in the sector, I argue that women's emotional, mental and physical health comes at a cost in these hectic workplaces. This article contributes to the literature on gender, work and care in women's organizations by taking seriously women's concerns working in these spaces, where they experience self‐neglect and institutional barriers in care regimes.     

改革所有制结构 促进山西产业结构调整

公有制经济比例过大 ,非公有制经济比例过小是山西所有制结构存在的主要问题 ,它对山西产业结构的调整有着一定的制约作用。发展非公有制经济 ,适度压缩国有经济比例 ,是促进山西产业结构调整的重要举措之一。     

英语习语中的非言语交际表达

从跨文化交际的角度对英语习语中的非言语交际行为及其特征进行探讨研究,认为英语习语在非言语交际中具有民族性和修辞性的特征,通过身体动作、面部表情、颜色、服装容饰等非言语行为的表达,强调和加强了言语内容,使得习语更加形象、生动、传神。     

从WTO规则析我国农业国内支持制度的完善

WTO相关协议允许各国采用一定的农业国内支持措施，尤其是《农产品协定》的绿箱条款更指明了农业国内支持的方向。我国在入世协议中也对农业国内支持作了承诺。为此，要发展农业，应在遵守上述WTO规则的基础上逐步完善我国现行的农业国内支持制度。     

基于TMS320C40的FFT算法改进及应用

讨论了充分利用C4 0的硬件并行结构进行实数FFT的并行算法 ,并在地震勘探信号处理中得以应用     

非统计专业统计学教育刍议

针对非统计专业统计学教育目前存在的问题,阐述了提高对统计学重要性的认识是保证统计教学效果的前提;统计学教学环节的改革是提高教学效果的重要保证;统计教师的业务素质是提高统计教学效果的关键。     

Non-ignorable missing covariate data in survival analysis: a case-study of an International Breast Cancer Study Group trial

Summary.  Non-ignorable missing data, a serious problem in both clinical trials and observational studies, can lead to biased inferences. Quality-of-life measures have become increasingly popular in clinical trials. However, these measures are often incompletely observed, and investigators may suspect that missing quality-of-life data are likely to be non-ignorable. Although several recent references have addressed missing covariates in survival analysis, they all required the assumption that missingness is at random or that all covariates are discrete. We present a method for estimating the parameters in the Cox proportional hazards model when missing covariates may be non-ignorable and continuous or discrete. Our method is useful in reducing the bias and improving efficiency in the presence of missing data. The methodology clearly specifies assumptions about the missing data mechanism and, through sensitivity analysis, helps investigators to understand the potential effect of missing data on study results.