Tests for homogeneity of risk differences in stratified design with correlated bilateral data

ABSTRACT

Correlated bilateral data arise from stratified studies involving paired body organs in a subject. When it is desirable to conduct inference on the scale of risk difference, one needs first to assess the assumption of homogeneity in risk differences across strata. For testing homogeneity of risk differences, we herein propose eight methods derived respectively from weighted-least-squares (WLS), the Mantel-Haenszel (MH) estimator, the WLS method in combination with inverse hyperbolic tangent transformation, and the test statistics based on their log-transformation, the modified Score test statistic and Likelihood ratio test statistic. Simulation results showed that four of the tests perform well in general, with the tests based on the WLS method and inverse hyperbolic tangent transformation always performing satisfactorily even under small sample size designs. The methods are illustrated with a dataset.     

Understanding public perceptions of genetically modified organisms in China: The role that heuristics play during digital media exposure

This study explicates the role that heuristics play in influencing Chinese public perceptions of genetically modified organisms (GMO) when they are exposed to digital media. According to the cognitive–affective continuum of heuristics, trust in scientists and negative emotions about GMOs are a cognition-oriented heuristic and an affect heuristic, respectively. The statistical results of an online survey (N?=?414) demonstrated that trust in scientists fully mediated digital media exposure and perceptions of the risks versus benefits of GMOs. In addition, negative emotions moderated the full mediation effect, showing that the indirect effect was stronger in individuals with low levels of negative emotions than in those with high levels of negative emotions. The mediating role of trust in scientists and the moderating role of negative emotions, the two heuristics applied in this study, indicated that when negative emotions were weak, laypeople were more likely to rely on scientists to judge the consequences of GMOs than systematically process the associated information by themselves. The findings have implications for reconsidering the importance of heuristic processing in building reasonable public perceptions of risk-laden technologies through the digital environment.     

Nonparametric beta kernel estimator for long and short memory time series

In this article we introduce a nonparametric estimator of the spectral density by smoothing the periodogram using beta kernel density. The estimator is proved to be bounded for short memory data and diverges at the origin for long memory data. The convergence in probability of the relative error and Monte Carlo simulations show that the proposed estimator automatically adapts to the long- and the short-range dependency of the process. A cross-validation procedure is studied in order to select the nuisance parameter of the estimator. Illustrations on historical as well as most recent returns and absolute returns of the S&P500 index show the performance of the beta kernel estimator. The Canadian Journal of Statistics 48: 582–595; 2020 © 2020 Statistical Society of Canada     

两类含两个变态贝塞尔函数积的积分公式

从变态贝塞尔方程出发,构造了它的一个变换形式,利用微积分学中的定理,巧妙地求得了含两个变态贝塞尔函数和的积的一个普遍的积分公式。进而利用变态贝塞尔函数的性质,可简洁地推导出电磁场领域中两类非常重要的积分的解析表达式,在处理圆柱结构下有关电磁场能量或电磁波传输功率的计算问题时,具有普遍的应用价值。     

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.     

Inference in Censored Models with Endogenous Regressors

This paper analyzes the linear regression model y = xβ+ε with a conditional median assumption med (ε| z) = 0, where z is a vector of exogenous instrument random variables. We study inference on the parameter β when y is censored and x is endogenous. We treat the censored model as a model with interval observation on an outcome, thus obtaining an incomplete model with inequality restrictions on conditional median regressions. We analyze the identified features of the model and provide sufficient conditions for point identification of the parameter β. We use a minimum distance estimator to consistently estimate the identified features of the model. We show that under point identification conditions and additional regularity conditions, the estimator based on inequality restrictions is normal and we derive its asymptotic variance. One can use our setup to treat the identification and estimation of endogenous linear median regression models with no censoring. A Monte Carlo analysis illustrates our estimator in the censored and the uncensored case.     

A Bias–Reduced Log–Periodogram Regression Estimator for the Long–Memory Parameter

In this paper, we propose a simple bias–reduced log–periodogram regression estimator, ^d_r, of the long–memory parameter, d, that eliminates the first– and higher–order biases of the Geweke and Porter–Hudak (1983) (GPH) estimator. The bias–reduced estimator is the same as the GPH estimator except that one includes frequencies to the power 2k for k=1,…,r, for some positive integer r, as additional regressors in the pseudo–regression model that yields the GPH estimator. The reduction in bias is obtained using assumptions on the spectrum only in a neighborhood of the zero frequency. Following the work of Robinson (1995b) and Hurvich, Deo, and Brodsky (1998), we establish the asymptotic bias, variance, and mean–squared error (MSE) of ^d_r, determine the asymptotic MSE optimal choice of the number of frequencies, m, to include in the regression, and establish the asymptotic normality of ^d_r. These results show that the bias of ^d_r goes to zero at a faster rate than that of the GPH estimator when the normalized spectrum at zero is sufficiently smooth, but that its variance only is increased by a multiplicative constant. We show that the bias–reduced estimator ^d_r attains the optimal rate of convergence for a class of spectral densities that includes those that are smooth of order s≥1 at zero when r≥(s−2)/2 and m is chosen appropriately. For s>2, the GPH estimator does not attain this rate. The proof uses results of Giraitis, Robinson, and Samarov (1997). We specify a data–dependent plug–in method for selecting the number of frequencies m to minimize asymptotic MSE for a given value of r. Some Monte Carlo simulation results for stationary Gaussian ARFIMA (1, d, 1) and (2, d, 0) models show that the bias–reduced estimators perform well relative to the standard log–periodogram regression estimator.     

Aggregation of isotropic autoregressive fields

This note constitutes a corrigendum to the article of Azomahou [2009, Memory properties and aggregation of spatial autoregressive models. J. Statist. Plann. Inference, 139, 2581-2597]. The aggregation of isotropic four nearest neighbors autoregressive models on the lattice Z², with random coefficient, is investigated. The spectral density of the resulting random field is studied in details for a large class of law of the AR coefficient. Depending on this law, the aggregated field may exhibit short memory or isotropic long memory.     

我国刑事法律援助制度的几个问题及完善——以国际刑事司法准则为视角

法律援助制度是法律程序公正的体现,通过实证考察法律援助的实施状况,从立法方面提出了法律援助范围过窄、可能判处死刑案件的援助阶段缺失以及实践中法律援助的责任主体不适当三个问题,继而研究国际刑事司法准则在这三个问题上设立的国际标准,通过比较提出对我国刑事法律援助制度改善的一些建议。