对网络伦理规矩的几点思考

解决网络伦理问题 ,需要制定网络伦理规矩。网络伦理规矩包括由浅到深三个层次的内容 :网络礼仪、网络规范和网络伦理原则。随着网络的出现和发展 ,确立网络伦理规矩已变成一个非常现实的问题。     

试论会计准则中会计政策的选择空间

目前实行的会计改革，一个明显的趋势就是要求企业根据会计准则规定的原则进行职业判断后再进 行会计处理和会计核算，这对会计人员的专业水平和职业道德都提出了挑战。     

多变量仿射非线性系统的可逆性秩判据

系统的可逆性判别是非线性控制的逆系统方法的关键,为探索可逆性分析的新途径,该文将系统可逆的秩检验法引入到多变量仿射非线性系统中,其实质是将系统的可逆性判定转化为对系统的输出函数及其导数所构成的雅可比矩阵的秩条件分析。文中给出了仿射非线性系统可逆的秩判据定理与证明过程,提出了一种具体的求逆算法,最后,举例对算法进行了验证,通过与微分几何法和逆系统方法的比较说明了秩判据法的有效性。     

Testing circular symmetry

The author addresses the problem of testing circular data for reflective symmetry about an unknown central direction and proposes a simple omnibus test based on the sample second sine moment about an estimation of this direction. Under quite general conditions, for an underlying distribution which is reflectively symmetric, the large‐sample asymptotic distribution of the test statistic is standard normal. Randomization and bootstrap variants of the test are also introduced, and the operating characteristics of different versions of the test are investigated in a Monte Carlo study. The large‐sample and bootstrap versions of the test are applied in the analysis of two illustrative examples drawn from the circular statistics literature.     

营造良好的理论创新的客观环境

理论创新与理论创造主体所处的社会客观环境有着密切联系。探讨影响理论创新的客观要素,自觉营造有利于百花齐放、百家争鸣的理论春天,对于促进我国哲学社会科学的繁荣,指导我国社会主义现代化建设实践有着极为重要的意义。当前,形成鼓励创新的社会评价标准,加强鼓励创新的制度建设,确立鼓励创新的体制、机制,对于良好的理论创新的客观环境的营造尤为紧迫。     

The Statistical Analysis of Preference Data

A new statistic and a new method of analysis are proposed for data where a sample of respondents provides a preference ordering of some treatments. The new preference statistic is compared with the Friedman statistic, particularly for an example where 12 home owners each ranked four grasses. The new analysis provides a more natural and less misleading assessment of where the differences occur than an analysis based on the rank sums of the Friedman statistic. The new analysis is also more robust to deviations from the classical location problem, is not related to election methods known to have undesirable characteristics and adheres to the Condorcet criterion for election methods.     

Bayesian model selection for join point regression with application to age-adjusted cancer rates

Summary.  The method of Bayesian model selection for join point regression models is developed. Given a set of K +1 join point models M ₀,  M ₁, …,  M _K with 0, 1, …,  K join points respec-tively, the posterior distributions of the parameters and competing models M _k are computed by Markov chain Monte Carlo simulations. The Bayes information criterion BIC is used to select the model M _k with the smallest value of BIC as the best model. Another approach based on the Bayes factor selects the model M _k with the largest posterior probability as the best model when the prior distribution of M _k is discrete uniform. Both methods are applied to analyse the observed US cancer incidence rates for some selected cancer sites. The graphs of the join point models fitted to the data are produced by using the methods proposed and compared with the method of Kim and co-workers that is based on a series of permutation tests. The analyses show that the Bayes factor is sensitive to the prior specification of the variance σ ², and that the model which is selected by BIC fits the data as well as the model that is selected by the permutation test and has the advantage of producing the posterior distribution for the join points. The Bayesian join point model and model selection method that are presented here will be integrated in the National Cancer Institute's join point software ( http://www.srab.cancer.gov/joinpoint/ ) and will be available to the public.     

Bayesian sample-size determination for one and two Poisson rate parameters with applications to quality control

We formulate Bayesian approaches to the problems of determining the required sample size for Bayesian interval estimators of a predetermined length for a single Poisson rate, for the difference between two Poisson rates, and for the ratio of two Poisson rates. We demonstrate the efficacy of our Bayesian-based sample-size determination method with two real-data quality-control examples and compare the results to frequentist sample-size determination methods.     

ON THE COVERAGE PROBABILITY OF CONFIDENCE INTERVALS IN REGRESSION AFTER VARIABLE SELECTION

This paper considers a linear regression model with regression parameter vector β. The parameter of interest is θ= a^Tβ where a is specified. When, as a first step, a data‐based variable selection (e.g. minimum Akaike information criterion) is used to select a model, it is common statistical practice to then carry out inference about θ, using the same data, based on the (false) assumption that the selected model had been provided a priori. The paper considers a confidence interval for θ with nominal coverage 1 ‐ α constructed on this (false) assumption, and calls this the naive 1 ‐ α confidence interval. The minimum coverage probability of this confidence interval can be calculated for simple variable selection procedures involving only a single variable. However, the kinds of variable selection procedures used in practice are typically much more complicated. For the real‐life data presented in this paper, there are 20 variables each of which is to be either included or not, leading to 2²⁰ different models. The coverage probability at any given value of the parameters provides an upper bound on the minimum coverage probability of the naive confidence interval. This paper derives a new Monte Carlo simulation estimator of the coverage probability, which uses conditioning for variance reduction. For these real‐life data, the gain in efficiency of this Monte Carlo simulation due to conditioning ranged from 2 to 6. The paper also presents a simple one‐dimensional search strategy for parameter values at which the coverage probability is relatively small. For these real‐life data, this search leads to parameter values for which the coverage probability of the naive 0.95 confidence interval is 0.79 for variable selection using the Akaike information criterion and 0.70 for variable selection using Bayes information criterion, showing that these confidence intervals are completely inadequate.