Statistical Properties of a Modified Accelerated Design for Phase I Cancer Clinical Trials

We consider the semiparametric profile likelihood inference for the distribution function under doubly censored data. For further developments of the statistical inference based on the profile likelihood ratio and alternative tools such as the score or Wald-type inference, we discuss the structures of the profile likelihood estimators and their derivatives included in the score function and the Fisher function of the profile likelihood, establishing the consistencies of their estimators.     

Robust Confidence Intervals for the Bernoulli Parameter

Despite the simplicity of the Bernoulli process, developing good confidence interval procedures for its parameter—the probability of success p—is deceptively difficult. The binary data yield a discrete number of successes from a discrete number of trials, n. This discreteness results in actual coverage probabilities that oscillate with the n for fixed values of p (and with p for fixed n). Moreover, this oscillation necessitates a large sample size to guarantee a good coverage probability when p is close to 0 or 1.

It is well known that the Wilson procedure is superior to many existing procedures because it is less sensitive to p than any other procedures, therefore it is less costly. The procedures proposed in this article work as well as the Wilson procedure when 0.1 ≤p ≤ 0.9, and are even less sensitive (i.e., more robust) than the Wilson procedure when p is close to 0 or 1. Specifically, when the nominal coverage probability is 0.95, the Wilson procedure requires a sample size 1, 021 to guarantee that the coverage probabilities stay above 0.92 for any 0.001 ≤ min {p, 1 ?p} <0.01. By contrast, our procedures guarantee the same coverage probabilities but only need a sample size 177 without increasing either the expected interval width or the standard deviation of the interval width.     

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.     

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.     

在中国共产党的旗帜下前进(代序)

在中国共产党诞辰80周年前夕,我们汇聚一堂,隆重热烈地召开党建和思想政治工作理论研讨会,以此来回顾我们党的光荣历史,讴歌我们党始终代表中国先进生产力的发展要求、中国先进文化的发展方向、中国最广大人民的根本利益的光辉业绩,展示自贡师专全体教职工在党的领导下不断端正教育思想、转变教育观念,全面贯彻执行党的教育方针,实现培养一代又一代具有正确的世界观、人生观、价值观,具有创造精神和实践能力的全面发展人才的目标所取得的成绩,表明我们在党的领导下,努力探索在新形势下高等学校党建和思想政治工作新思路、新特点的…     

鲁迅小说的象征解读

文学的最高成就是这用象征,鲁迅则是使用象征的行家,他的小说中充满了象征构思和象征语象。对这样的经典之作,只有用象征主义的阐释方法即象征解读,才能领会其中的万千气象．本文从意旨符号、人物形象、情节布置、人生解构四个方面时鲁迅小说进行了象征解读,从而认为：象征是语言艺术的升华,是诗的点金术。鲁迅小说是诗．     

“中国模式”对马克思主文中国化的作用

“中国模式”对马克思主义中国化历史发展和理论成果具有重要的作用。具体表现在：“中国模式”内容要素是马克思主义中国化理论成果的重要组成部分;“中国模式”各要素的和谐运行是马克思主义中国化理论成果尤其是中国特色社会主义理论体系发展的重要条件;“中国模式”的成功是马克思主义中国化即马克思理论基本原理与中国实际相结合正确性的强有力的证明。