明末清初善书风行现象解析

明末清初善书风行于世,从表面上看,它是受到了帝王及士人的倡导而风行起来的。而从更深的层面看,它既与当时的社会变动、社会矛盾密切相关,又与当时商品经济所造成的道德失控有关,同时它还是三教融合趋势的具体体现。这一时期的善书现世功利性加强,适应了几乎所有民众的功利需求而成为人们内心精神世界的寄托。另一方面,它是统治者进行劝善教化的一种手段,对传统伦理秩序的极力维护是善书无法摆脱的封建政治情结。     

On the influence of misclassified data on results of goodness of fit testing

In this paper we focus on the chi-square test of goodness of fit, which compares an observed discrete distribution to an expected known one. We show that the results of this test, using the common Pearson statistic, are very sensitive to misclassified observations between two or more categories. We also propose a general rule of thumb for analysing data set stability with respect to such classification errors. Practical analysis of a real example illustrates our purpose.     

Empirical-distribution-function goodness-of-fit tests for discrete models

We present a simple framework for studying empirical-distribution-function goodness-of-fit tests for discrete models. A key tool is a weak-convergence result for an estimated discrete empirical process, regarded as a random element in some suitable sequence space. Special emphasis is given to the problem of testing for a Poisson model and for the geometric distribution. Simulations show that parametric bootstrap versions of the tests maintain a nominal level of significance very closely even for small samples where reliance upon asymptotic critical values is doubtful.     

An extensive power evaluation of some tests for the inverse Gaussian distribution

Many applications of the Inverse Gaussian distribution, including numerous reliability and life testing results are presented in statistical literature. The paper studies the problem of using entropy tests to examine the goodness of fit of an Inverse Gaussian distribution with unknown parameters. Some entropy tests based on different entropy estimates are proposed. Critical values of the test statistics for various sample sizes are obtained by Monte Carlo simulations. Type I error of the tests is investigated and then power values of the tests are compared with the competing tests against various alternatives. Finally, recommendations for the application of the tests in practice are presented.     

Visualizing Count Data Regressions Using Rootograms

The rootogram is a graphical tool associated with the work of J. W. Tukey that was originally used for assessing goodness of fit of univariate distributions. Here, we extend the rootogram to regression models and show that this is particularly useful for diagnosing and treating issues such as overdispersion and/or excess zeros in count data models. We also introduce a weighted version of the rootogram that can be applied out of sample or to (weighted) subsets of the data, for example, in finite mixture models. An empirical illustration revisiting a well-known dataset from ethology is included, for which a negative binomial hurdle model is employed. Supplementary materials providing two further illustrations are available online: the first, using data from public health, employs a two-component finite mixture of negative binomial models; the second, using data from finance, involves underdispersion. An R implementation of our tools is available in the R package countreg. It also contains the data and replication code.     

A Discrete Distribution Arising as a Solution of a Linear Difference Equation: Extension of the Non Central Negative Binomial Distribution

This article considers a discrete distribution that arises as the dominant solution of a linear difference equation. Basic properties and various chance mechanisms that lead to this distribution are given. In particular, its formulation as a weighted distribution and a mixed Poisson process are proposed. Parameter estimation by (a) using a combination of observed frequencies and moments and (b) maximum likelihood are examined. An example of goodness of fit is considered.     

A tool for systematically comparing the power of tests for normality

In this article, we describe a new approach to compare the power of different tests for normality. This approach provides the researcher with a practical tool for evaluating which test at their disposal is the most appropriate for their sampling problem. Using the Johnson systems of distribution, we estimate the power of a test for normality for any mean, variance, skewness, and kurtosis. Using this characterization and an innovative graphical representation, we validate our method by comparing three well-known tests for normality: the Pearson χ² test, the Kolmogorov–Smirnov test, and the D'Agostino–Pearson K ² test. We obtain such comparison for a broad range of skewness, kurtosis, and sample sizes. We demonstrate that the D'Agostino–Pearson test gives greater power than the others against most of the alternative distributions and at most sample sizes. We also find that the Pearson χ² test gives greater power than Kolmogorov–Smirnov against most of the alternative distributions for sample sizes between 18 and 330.     

Testing the validity of the logistic model based on the empirical distribution function

The logistic distribution is one of the fundamental distribution and is widely used for describing model growth curves in survival analysis and biological studies. Applications of this distribution are presented in statistical literature. In this article, goodness of fit tests for the logistic distribution based on the empirical distribution function (EDF) are considered. In order to compute the test statistics, because the MLEs cannot be obtained explicitly, we use the approximate maximum likelihood estimates (AMLEs) suggested by Balakrishnan and Cohen (1990 Balakrishnan, N., Cohen, A. C. (1990). Order Statistics and Inference: Estimation Methods. Boston: Academic Press. [Google Scholar]), which are simple explicit estimators. Power comparisons of the considered tests are carried out via simulations. Finally, two illustrative examples are presented and analyzed.     

善概念的普遍定义如何可行--从人的意识的先天内在结构出发演绎善概念

一种彻底的伦理理性不能回避善的定义。但伦理学史中所进行的定义善的努力都因存在重大缺陷而屡遭挫败。它们的共同缺点在于没有充分认识到善是最高的伦理范畴,在逻辑上应该超越伦理论域来寻求定义方法。本文根据善的主观认识本性而把它的根源回溯到意识存在的普遍形式,然后用精心设计的纯粹普遍思维路径尝试打破摩尔制造的善概念不可定义的神话。     

A comparison of suicide patterns in Australia and Hong Kong

We analyse the patterns of 6564 suicides in Hong Kong and 23671 suicides in Australia for the period 1981–1993. Within the unifying framework of logistic regression we investigate how suicide rates vary with marital status and age and how these patterns vary over time and between the two cultures. The main significant differences between the two cultures are that rates are higher in Australia, rates for males are much higher than for females in Australia but only slightly higher in Hong Kong, in Hong Kong the oldest age group has the highest suicide rate unlike in Australia and the protective effects of marriage are larger in Australia.