Empirical likelihood ratio-based goodness-of-fit test for the logistic distribution

The logistic distribution has been used to model growth curves in survival analysis and biological studies. In this article, we propose a goodness-of-fit test for the logistic distribution based on the empirical likelihood ratio. The test is constructed based on the methodology introduced by Vexler and Gurevich [17 A. Vexler and G. Gurevich, Empirical likelihood ratios applied to goodness-of-fit tests based on sample entropy, Comput. Stat. Data Anal. 54 (2010), pp. 531–545. doi: 10.1016/j.csda.2009.09.025[Crossref], [Web of Science ®] , [Google Scholar]]. In order to compute the test statistic, parameters of the distribution are estimated by the method of maximum likelihood. Power comparisons of the proposed test with some known competing tests are carried out via simulations. Finally, an illustrative example is presented and analyzed.     

A Correlation Test for Normality Based on the Lévy Characterization

A powerful test of fit for normal distributions is proposed. Based on the Lévy characterization, the test statistic is the sample correlation coefficient of normal quantiles and sums of pairs of observations from a random sample. Since the test statistic is location-scale invariant, critical values can be obtained by simulation without estimating any parameters. It is proved that this test is consistent. A power comparison study including some directed tests shows that the proposed test is competitive, it is more powerful than the well-known Jarque–Bera test, and it is comparable to Shapiro–Wilk test against a number of alternatives.     

Inference for the Birnbaum–Saunders Lifetime Regression Model with Applications

The Birnbaum–Saunders distribution is a positively skewed distribution that is frequently used for analyzing lifetime data. Regression analysis is widely used in this context when some covariates are involved in the life-test. In this article, we discuss the maximum likelihood estimation of the model parameters and associated inference. We discuss the likelihood-ratio tests for some hypotheses of interest as well as some interval estimation methods. A Monte Carlo simulation study is then carried out to examine the performance of the proposed estimators and the interval estimation methods. Finally, some numerical data analyses are done for illustrating all the inferential methods developed here.     

How to test hypotheses if you must

Drug development is not the only industrial‐scientific enterprise subject to government regulations. In some fields of ecology and environmental sciences, the application of statistical methods is also regulated by ordinance. Over the past 20years, ecologists and environmental scientists have argued against an unthinking application of null hypothesis significance tests. More recently, Canadian ecologists have suggested a new approach to significance testing, taking account of the costs of both type I and type II errors. In this paper, we investigate the implications of this for testing in drug development and demonstrate that its adoption leads directly to the likelihood principle and Bayesian approaches. Copyright © 2015 John Wiley & Sons, Ltd.     

Efficient Tests Under a Weak Convergence Assumption

The asymptotic validity of tests is usually established by making appropriate primitive assumptions, which imply the weak convergence of a specific function of the data, and an appeal to the continuous mapping theorem. This paper, instead, takes the weak convergence of some function of the data to a limiting random element as the starting point and studies efficiency in the class of tests that remain asymptotically valid for all models that induce the same weak limit. It is found that efficient tests in this class are simply given by efficient tests in the limiting problem—that is, with the limiting random element assumed observed—evaluated at sample analogues. Efficient tests in the limiting problem are usually straightforward to derive, even in nonstandard testing problems. What is more, their evaluation at sample analogues typically yields tests that coincide with suitably robustified versions of optimal tests in canonical parametric versions of the model. This paper thus establishes an alternative and broader sense of asymptotic efficiency for many previously derived tests in econometrics, such as tests for unit roots, parameter stability tests, and tests about regression coefficients under weak instruments.     

《韩诗外传》中的享乐考验主题及其文本形态特征

《韩诗外传》中以享乐考验为主题的故事,从正反两方面以叙事形态体现了儒家学说对于享乐的警惕意识。由于《韩诗外传》本身讲经提纲或听课记录的文本性质,其享乐考验故事的文本形态相对简略,限制和削弱了它的文学性。讲经的文学价值、艺术技巧只能在既定的文本形态中显示,有时可以得到充分展现,有时则难免受到限制。     

世界原油价格风险度量——基于EGARCH-EVT-t Copula模型

从原油现货市场收益率的特征分析入手,为了更好地描述原油现货市场收益率的尖峰厚尾、偏态和波动集聚等特征,运用EGARCH 对条件波动率进行建模,进而运用极值理论对标准残差序列的尾部分布进行建模,刻画原油现货市场极值风险,同时结合Copula函数和Monte Carlo模拟技术来度量不同持有期相应的VaR值.实证结果表明:原油市场随着置信度的提高和持有期的延长,VaR的绝对值在增大.同时,回测检验结果表明基于EGARCH-EVT-t Copula的模型能够精确有效地度量原油现货市场极端风险.     

基于制造业从业者职业特征开展科学素养测评的探析

根据目前科学素养理论研究和测评现状,以职业从业人员作为评测的主体,采用具有权重的科学素养五维度测评体系,根据不同职业的特点进行科学素养水平的测评。文章主要对广东省制造业从业者的科学素养开展了抽样测评,从年龄、文化程度、专业技术职务等因素对制造业从业者的科学素养水平进行分析,从而验证了职业人群测评的可行性。     

智能组卷中的试题难度评价建模

主要研究了网络考试系统设计中利用遗传算法进行智能组卷的问题．首先根据用户对组卷的要求,利用遗传算法对组卷所需的约束条件设置各项指标,利用矩阵理论建立有利于遗传算子进行组合交叉和变异的数学模型;再针对组卷问题中的一个重要约束条件——试卷的难度指标,采用模糊数学方法和项目反应理论对试题库中每一小题进行综合评价试题难度的数学建模,以准确确定每道小题的试题难度系数,最终为实现遗传算法全局寻优和智能搜索奠定基础．     

中国农业出口贸易与环境污染关系的实证研究

在估算中国农业环境污染排放量的基础上,运用Johansen协整分析、Granger因果关系检验和脉冲响应分析对1995-2010年中国农业出口贸易总额和农业环境污染之间的长期变化关系进行实证分析.结果表明:从长期来看,中国农业贸易出口额和农业环境污染之问是正向的关系,且农业出口贸易规模的变化是导致农业环境污染排放量变化的原因.脉冲响应分析则进一步从动态的角度验证了农业出口贸易规模对农业环境污染的正向影响程度及滞后趋势.并在实证分析的基础上提出了优化农业出口贸易结构,健全、完善与农业贸易有关的环境规制政策以及逐步推进农业环境成本内部化等建议.