Estimation of parameters and reliability from generalized life models

Simultaneous estimation of parameters with p (≥ 2) components, where each component has a generalized life distribution, is considered under a sum of squared error loss function. Improved estimators are obtained which dominate the maximum likelihood and the niinimum mean square estimators. Robustness of the improved estimators is shown even when the component distributions are dependent. The result is extended to the estimation of the system reliability when the components are connected in series. Several numerical studies are performed to demonstrate the risk improvement and the Pitman closeness of the new estimators.     

Economic-statistical design of X ¥ charts for non-normal data by considering quality loss

When the X ¥ control chart is used to monitor a process, three parameters should be determined: the sample size, the sampling interval between successive samples, and the control limits of the chart. Duncan presented a cost model to determine the three parameters for an X ¥ chart. Alexander et al. combined Duncan's cost model with the Taguchi loss function to present a loss model for determining the three parameters. In this paper, the Burr distribution is employed to conduct the economic-statistical design of X ¥ charts for non-normal data. Alexander's loss model is used as the objective function, and the cumulative function of the Burr distribution is applied to derive the statistical constraints of the design. An example is presented to illustrate the solution procedure. From the results of the sensitivity analyses, we find that small values of the skewness coefficient have no significant effect on the optimal design; however, a larger value of skewness coefficient leads to a slightly larger sample size and sampling interval, as well as wider control limits. Meanwhile, an increase on the kurtosis coefficient results in an increase on the sample size and wider control limits.     

Sparse additive models

Summary.  We present a new class of methods for high dimensional non-parametric regression and classification called sparse additive models. Our methods combine ideas from sparse linear modelling and additive non-parametric regression. We derive an algorithm for fitting the models that is practical and effective even when the number of covariates is larger than the sample size. Sparse additive models are essentially a functional version of the grouped lasso of Yuan and Lin. They are also closely related to the COSSO model of Lin and Zhang but decouple smoothing and sparsity, enabling the use of arbitrary non-parametric smoothers. We give an analysis of the theoretical properties of sparse additive models and present empirical results on synthetic and real data, showing that they can be effective in fitting sparse non-parametric models in high dimensional data.     

Parametric non-mixture cure models for schedule finding of therapeutic agents

We propose a phase I clinical trial design that seeks to determine the cumulative safety of a series of administrations of a fixed dose of an investigational agent. In contrast with traditional phase I trials that are designed solely to find the maximum tolerated dose of the agent, our design instead identifies a maximum tolerated schedule that includes a maximum tolerated dose as well as a vector of recommended administration times. Our model is based on a non-mixture cure model that constrains the probability of dose limiting toxicity for all patients to increase monotonically with both dose and the number of administrations received. We assume a specific parametric hazard function for each administration and compute the total hazard of dose limiting toxicity for a schedule as a sum of individual administration hazards. Throughout a variety of settings motivated by an actual study in allogeneic bone marrow transplant recipients, we demonstrate that our approach has excellent operating characteristics and performs as well as the only other currently published design for schedule finding studies. We also present arguments for the preference of our non-mixture cure model over the existing model.     

On equality of ordinary least squares estimator,best linear unbiased estimator and best linear unbiased predictor in the general linear model

The equality of ordinary least squares estimator (OLSE), best linear unbiased estimator (BLUE) and best linear unbiased predictor (BLUP) in the general linear model with new observations is investigated through matrix rank method, some new necessary and sufficient conditions are given.     

Bayesian analysis of a general growth curve model with predictions using power transformations and AR(1) autoregressive dependence

In this paper, we consider a Bayesian analysis of the unbalanced (general) growth curve model with AR(1) autoregressive dependence, while applying the Box-Cox power transformations. We propose exact, simple and Markov chain Monte Carlo approximate parameter estimation and prediction of future values. Numerical results are illustrated with real and simulated data.     

专科英语应用能力测试中增加口试的必要性

本文从语言学、教育学、心理学、英语教学法及英语教学实践等的角度 ,多方阐述了专科英语教学中口语训练的重要性 ,并论证了专科英语应用能力测试中增加口试的必要性     

地下空间开发对城市环境地质的影响及保护措施浅谈

城市地下空间作为一种资源在城市发展中扮演着重要的角色。地下空间具有恒温、恒湿、抗震、防爆、绝热、封闭和可开拓性好的特点,所以开发地下空间比建造地面工程有着独特的优势,目前开发和利用地下空间已成为我国大中城市解决人口、资源、环境危机和医治“城市综合症”的重要措施,也是实施城市可持续发展的重要途径。     

我国食品安全监管体制研究——兼评我国《食品安全法》

我国食品安全监管的主要问题表现为行政监管组织法体系不健全、监管主体过多、行政监管权分配不合理以及对监管主体缺乏有力的责任约束机制。2009年2月通过的《食品安全法》并没有改变分段监管的模式，食品安全委员会权限设置不明确，影响监管力度。结合我国国情，要完善安全监管行政组织法体系、推行渐进式统一模式，将各个环节的监管职权逐步整合于食品药品监督管理局，加大食品安全委员会的职权，增强其协调权威性，并不断加强监管机构的监管责任，建立权责一致的行政和司法责任追究制度：     

城市人口密度、国内市场规模与就业增长

我国经济“高增长、低就业”现象的原因在于我国贸易成本过高，国内市场规模较少。解决我国就业问题的关键在于减少行政干预，顺应市场要求，促进人口在空间上的适度集中，提高人口密度，提高消费效率，扩大市场规模。应进一步完善城乡一体的户籍制度，完善整合城乡劳动力市场，提高要素流动能力等。