走向双性化的性别角色教育

性别角色教育的缺失和单性化的性别角色教育是当前性别角色教育中存在的两种误区.贝姆的性别角色发展模式理论和性别角色与心理适应的实证研究为实施双性化的性别角色教育提供了理论依据,通过家庭教育、学校教育以及社会教育的共同努力进行双性化的性别角色教育是造就适应社会发展、推动社会进步的优秀人才的一种有意义的尝试.     

Heteroscedastic Regression Models and Applications to Off-line Quality Control

We discuss in the present paper the analysis of heteroscedastic regression models and their applications to off-line quality control problems. It is well known that the method of pseudo-likelihood is usually preferred to full maximum likelihood since estimators of the parameters in the regression function obtained are more robust to misspecification of the variance function. Despite its popularity, however, existing theoretical results are difficult to apply and are of limited use in many applications. Using more recent results in estimating equations, we obtain an efficient algorithm for computing the pseudo-likelihood estimator with desirable convergence properties and also derive simple, explicit and easy to apply asymptotic results. These results are used to look in detail at variance minimization in off-line quality control, yielding techniques of inferences for the optimized design parameter. In application of some existing approaches to off-line quality control, such as the dual response methodology, rigorous statistical inference techniques are scarce and difficult to obtain. An example of off-line quality control is presented to discuss the practical aspects involved in the application of the results obtained and to address issues such as data transformation, model building and the optimization of design parameters. The analysis shows very encouraging results, and is seen to be able to unveil some important information not found in previous analyses.     

Statistical inference for start-up demonstration tests with rejection of units upon observing d failures

In this paper, we consider the statistical inference for the success probability in the case of start-up demonstration tests in which rejection of units is possible when a pre-fixed number of failures is observed before the required number of consecutive successes are achieved for acceptance of the unit. Since the expected value of the stopping time is not a monotone function of the unknown parameter, the method of moments is not useful in this situation. Therefore, we discuss two estimation methods for the success probability: (1) the maximum likelihood estimation (MLE) via the expectation-maximization (EM) algorithm and (2) Bayesian estimation with a beta prior. We examine the small-sample properties of the MLE and Bayesian estimator. Finally, we present an example to illustrate the method of inference discussed here.     

Understanding the social and cultural bases of Brexit*

We use data from a large scale and nationally representative survey to evaluate two narratives about the social bases of Brexit. The first narrative sees Brexit as a revolt of the economically left-behinds. The second narrative attributes Brexit to the resurgence of an English nationalism. There is some, albeit not always consistent, evidence that people in relative poverty or those living in areas that have seen greater Chinese import penetration are slightly more pro-Leave. People living in economically deprived neighborhoods are not more pro-Brexit. Using the Weberian class–status distinction, it is social status, not social class, which stratifies Brexit support. Individuals for whom being British is important are more pro-Leave. But those who see themselves as British rather than English, and those reporting omnivorous cultural consumption are less supportive of Brexit. Overall, there is empirical support for both narratives. But the weight of the evidence suggests a strong cultural dimension in Brexit support.     

SPECTRAL ANALYSIS IN BUSINESS FORECASTING*

Although spectral analysis has previously been discussed in a number of business journals, the discussion has not been detailed enough for non-mathematicians. The objective of this paper is to review in detail the concepts and to go over the computations of spectral analysis as they pertain to forecasting. To gain insight into the model building technique of spectral analysis, a passing comparison with a familiar model–regression–is made. Regression analysis attempts to find a set of independent variables that shed some light on the dependent variable to be forecasted. In other words, if the independent variables have some functional relationship with the dependent variable, a reliable forecast of the dependent variable can then be made. Forecasting using spectral analysis, on the other hand, is based on the assumption that the variation of a time series can be explained by some mixture of sine and cosine waves. Model parameters can then be estimated for these waves and forecasts be made. These parameters have the same property of least squares as in ordinary regression analysis. A transformation of these parameters gives the spectra of the time series. The spectra are related to the explained variation present in regression analysis. An extension of the spectra gives a set of coefficients of an autoregressive forecasting model. This latter model is referred to as the Wiener-Kolmogorov forecasting model.