Non-parametric estimation of the long-range dependence exponent for Gaussian processes

We consider a class of long-range-dependent Gaussian processes defined in a semiparametric framework. We propose a new estimator of the long-range dependence parameter, based on the integration of the periodogram in two windows. We show that it is asymptotically Gaussian and calculate the rate of convergence. We optimise parameters defining the window function for the minimum mean-square-error criterion. In a Monte-Carlo study, we compare the proposed estimator with previously studied estimators.     

论中国近代诗歌

这是一篇关于中国近代诗歌的综论。全篇分三个部分。第一个部分为价值论,主要从近代社会剧变对诗歌的期待和中国古典诗歌走向终端的矛盾中确认近代诗歌的价值。第二部分为流派论,在检讨历来划分近代诗派的基础上,将近代诗歌分为一重前瞻,一重后顾的两大潮流。第三部分为特征论。先从创作思想、审美意象、体裁和语言方向说明近代诗歌与古代诗歌的连结,再从诗风转变、题材拓展、语言增新、绝句见重、诗乐结合五点说明近代诗歌有别于古代诗歌的总体特征。通篇所论均有道他人所未道之见识     

企业伦理气候、组织承诺与组织公民行为的关系——以温州民营企业为例

企业社会责任等方面存在的问题是造成温州民营企业“民工荒”的重要原因。通过问卷调查,发现员工对企业伦理气候中的法规导向最为认可且看法比较一致,对组织承诺中的情感承诺最为认可且对道德承诺的看法相对一致,对组织公民行为中的组织公益最为认可且看法比较一致。员工对组织承诺、组织公民行为的主张是正向积极的,未发现企业伦理气候、组织承诺对组织公民行为有强烈的正向影响。提出温州民营企业摆脱困境的思路：重视企业伦理气候建设,形成正确的企业文化,增强员工的向心力;强化员工的组织承诺;鼓励员工的组织公民行为。     

《解放日报》视域中的延安时期男女婚恋观的嬗变及其影响

1942年4月1日《解放日报》将“文艺”专刊改为综合性副刊。《解放日报》改版前的作品更多地是呈现知识分子对于自由婚恋的憧憬与理想,探讨婚后女性面临家庭与事业难以兼顾的窘境以及个人私欲与革命信仰进行博弈而产生的离婚危机。改版后,综合副刊上刊登的婚恋作品主人公大多是工农兵出身,呈现出政治进步可以成就美满婚姻、夫妻共同参加生产劳动可以促进家庭和谐、男女努力学习知识就可以收获爱的体验的新型婚恋观。文章通过考察改版前后不同时期《解放日报》上发表的涉及婚恋问题的文学作品,发现了延安时期男女婚恋观念的嬗变轨迹,认为《解放日报》对男女婚恋问题的反映与思考,对建国后“十七年文学”有着深刻的影响与启示。     

A parallel analysis of individual and ecological data on residential radon and lung cancer in south-west England

Parallel individual and ecological analyses of data on residential radon have been performed using information on cases of lung cancer and population controls from a recent study in south-west England. For the individual analysis the overall results indicated that the relative risk of lung cancer at 100 Bq m⁻³ compared with at 0 Bq m⁻³ was 1.12 (95% confidence interval (0.99, 1.27)) after adjusting for age, sex, smoking, county of residence and social class. In the ecological analysis substantial bias in the estimated effect of radon was present for one of the two counties involved unless an additional variable, urban–rural status, was included in the model, although this variable was not an important confounder in the individual level analysis. Most of the methods that have been recommended for overcoming the limitations of ecological studies would not in practice have proved useful in identifying this variable as an appreciable source of bias.     

Parameterization and inference for nonparametric regression problems

We consider local likelihood or local estimating equations, in which a multivariate function () is estimated but a derived function () of () is of interest. In many applications, when most naturally formulated the derived function is a non-linear function of (). In trying to understand whether the derived non-linear function is constant or linear, a problem arises with this approach: when the function is actually constant or linear, the expectation of the function estimate need not be constant or linear, at least to second order. In such circumstances, the simplest standard methods in nonparametric regression for testing whether a function is constant or linear cannot be applied. We develop a simple general solution which is applicable to nonparametric regression, varying-coefficient models, nonparametric generalized linear models, etc. We show that, in local linear kernel regression, inference about the derived function () is facilitated without a loss of power by reparameterization so that () is itself a component of (). Our approach is in contrast with the standard practice of choosing () for convenience and allowing ()> to be a non-linear function of (). The methods are applied to an important data set in nutritional epidemiology.     

Using the Standardized Difference to Compare the Prevalence of a Binary Variable Between Two Groups in Observational Research

Researchers are increasingly using the standardized difference to compare the distribution of baseline covariates between treatment groups in observational studies. Standardized differences were initially developed in the context of comparing the mean of continuous variables between two groups. However, in medical research, many baseline covariates are dichotomous. In this article, we explore the utility and interpretation of the standardized difference for comparing the prevalence of dichotomous variables between two groups. We examined the relationship between the standardized difference, and the maximal difference in the prevalence of the binary variable between two groups, the relative risk relating the prevalence of the binary variable in one group compared to the prevalence in the other group, and the phi coefficient for measuring correlation between the treatment group and the binary variable. We found that a standardized difference of 10% (or 0.1) is equivalent to having a phi coefficient of 0.05 (indicating negligible correlation) for the correlation between treatment group and the binary variable.     

Transformed Logit Confidence Intervals for Small Populations in Single Capture–Recapture Estimation

The good performance of logit confidence intervals for the odds ratio with small samples is well known. This is true unless the actual odds ratio is very large. In single capture–recapture estimation the odds ratio is equal to 1 because of the assumption of independence of the samples. Consequently, a transformation of the logit confidence intervals for the odds ratio is proposed in order to estimate the size of a closed population under single capture–recapture estimation. It is found that the transformed logit interval, after adding .5 to each observed count before computation, has actual coverage probabilities near to the nominal level even for small populations and even for capture probabilities near to 0 or 1, which is not guaranteed for the other capture–recapture confidence intervals proposed in statistical literature. Thus, given that the .5 transformed logit interval is very simple to compute and has a good performance, it is appropriate to be implemented by most users of the single capture–recapture method.     

Comparing a New Gini Test with Other Symmetry Tests When Median Is Unknown

Although there exists an ample literature on the tests of univariate symmetry, each article provides comparison of few selected competitors only. We are comparing the performance of 15 tests recommended in the literature and two new methods introduced by Auda (2006 Auda (Ouda), H. 2006. New Tests of Univariate Symmetry Based on the Gini Mean Difference, Kalamazoo, MI: Western Michigan University. Ph.D. thesis [Google Scholar]). One of them, rank-based test RS, compares favorably with several existing procedures in controlling the Type I error as well as in power as shown in our comprehensive simulation study. An important novelty in the article are Figs. 1– 3 enabling comparison of Type I error probabilities and power of the 16 tests for 17 null and 19 alternative distributions.     

Sample Size Calculation and Blinded Sample Size Recalculation in Clinical Trials Where the Treatment Effect is Measured by the Relative Risk

In clinical trials with binary endpoints, the required sample size does not depend only on the specified type I error rate, the desired power and the treatment effect but also on the overall event rate which, however, is usually uncertain. The internal pilot study design has been proposed to overcome this difficulty. Here, nuisance parameters required for sample size calculation are re-estimated during the ongoing trial and the sample size is recalculated accordingly. We performed extensive simulation studies to investigate the characteristics of the internal pilot study design for two-group superiority trials where the treatment effect is captured by the relative risk. As the performance of the sample size recalculation procedure crucially depends on the accuracy of the applied sample size formula, we firstly explored the precision of three approximate sample size formulae proposed in the literature for this situation. It turned out that the unequal variance asymptotic normal formula outperforms the other two, especially in case of unbalanced sample size allocation. Using this formula for sample size recalculation in the internal pilot study design assures that the desired power is achieved even if the overall rate is mis-specified in the planning phase. The maximum inflation of the type I error rate observed for the internal pilot study design is small and lies below the maximum excess that occurred for the fixed sample size design.