Modified likelihood ratio tests in heteroskedastic multivariate regression models with measurement error

In this paper, we develop modified versions of the likelihood ratio test for multivariate heteroskedastic errors-in-variables regression models. The error terms are allowed to follow a multivariate distribution in the elliptical class of distributions, which has the normal distribution as a special case. We derive the Skovgaard-adjusted likelihood ratio statistics, which follow a chi-squared distribution with a high degree of accuracy. We conduct a simulation study and show that the proposed tests display superior finite sample behaviour as compared to the standard likelihood ratio test. We illustrate the usefulness of our results in applied settings using a data set from the WHO MONICA Project on cardiovascular disease.     

Bivariate deconvolution with SIMEX: an application to mapping Alaska earthquake density

Constructing spatial density maps of seismic events, such as earthquake hypocentres, is complicated by the fact that events are not located precisely. In this paper, we present a method for estimating density maps from event locations that are measured with error. The estimator is based on the simulation–extrapolation method of estimation and is appropriate for location errors that are either homoscedastic or heteroscedastic. A simulation study shows that the estimator outperforms the standard estimator of density that ignores location errors in the data, even when location errors are spatially dependent. We apply our method to construct an estimated density map of earthquake hypocenters using data from the Alaska earthquake catalogue.     

Error analysis of stochastic flight trajectory prediction models

This paper focuses on the analysis of errors between a flight trajectory prediction model and flight data. A novel stochastic prediction flight model is compared with the popular fly-by and fly-over turn models. The propagated error is measured using either spatial coordinates or angles. Depending on the case, the distribution of error is estimated and confidence bounds for the linear and directional mean are provided for all three stochastic flight models.     

Estimating the error distribution function in semiparametric additive regression models

We consider semiparametric additive regression models with a linear parametric part and a nonparametric part, both involving multivariate covariates. For the nonparametric part we assume two models. In the first, the regression function is unspecified and smooth; in the second, the regression function is additive with smooth components. Depending on the model, the regression curve is estimated by suitable least squares methods. The resulting residual-based empirical distribution function is shown to differ from the error-based empirical distribution function by an additive expression, up to a uniformly negligible remainder term. This result implies a functional central limit theorem for the residual-based empirical distribution function. It is used to test for normal errors.     

公司年度会计稳健性计量方法适应性研究

准确计量公司年度会计稳健性是可靠检验会计稳健性经济后果的关键,以2006-2009年的中国上市公司数据采用不同方法相互校验的方式,实证检验了负应计和K&W(2009)会计稳健性指数这两种国内外最主要的公司年度会计稳健性计量方法在中国的适应性。实证研究发现,K&W(2009)会计稳健性指数在不同方法之间存在高度的一致性,而负应计在Basu模型法下具有一致性但不稳定,在盈余反转模型法下不一致,而在应计现金流模型法下一致但不显著。因此,在中国的现实制度背景下,在会计稳健性经济后果研究中应该采用K&W(2009)会计稳健性指数来度量公司的会计稳健性。     

A bias analysis of Weibull models under heaped data

Retrospectively collected duration data are often reported incorrectly. An important type of such an error is heaping—respondents tend to round-off or round-up the data according to some rule of thumb. For two special cases of the Weibull model we study the behaviour of the ‘naive estimators’, which simply ignore the measurement error due to heaping, and derive closed expressions for the asymptotic bias. These results give a formal justification of empirical evidence and simulation-based findings reported in the literature. Additionally, situations where a remarkable bias has to be expected can be identified, and an exact bias correction can be performed.     

全球、区域系统性风险测量与中国海外投资

随着全球经济一体化的深入 ,任何一个投资目的地的风险中必然包含了全球的系统性风险因素。同时 ,地理或经济上联系紧密的国家(地区)还共同受区域系统性风险的影响。中国海外投资目的地的总体风险可分解为全球、区域系统性风险与非系统性风险。研究发现 :总体风险方面 ,发达国家平均总风险显著低于其他地区 ;全球系统性风险方面 ,欧洲国家显著高于其他地区 ,开放度高的小型经济体国家受全球系统性风险影响较大 ;作为我国企业海外投资最主要目的地的亚洲和欧洲大部分国家受区域系统性风险影响较大 ,可适当增加对与亚洲发展中国家经济发展程度类似的南美国家的投资 ,分散区域系统性风险 ;非洲国家非系统性风险占比最高 ,而欧洲国家的非系统性风险较小。     

我国航空运输LTO阶段和巡航阶段排放量测算与预测

利用国际民航组织的发动机排放数据库和我国航空运输的生产数据,测算2011—2016年我国航空运输在LTO阶段和巡航阶段各种污染物的排放量。同时,针对典型机型、典型机场和典型航线进行相关污染物排放量的测算。研究方法和研究结论对于测算和治理我国航空运输排放产生的环境外部成本具有重要意义。     

网上银行顾客感知服务质量影响因素研究

通过文献研究和深度访谈,提炼出可能影响网上银行顾客感知服务质量的19个因素,通过探索性因子分析,将这19个因素归纳为经济性、易用性、履行性、安全性和响应性。通过相关分析和多元回归分析发现,有4类因素与网上银行顾客感知服务质量呈显著正相关,依次是经济性、易用性、履行性和安全性。通过对我国主要网上银行服务质量测评发现,当前我国网上银行服务质量不高,且各家网上银行服务质量存在明显差异。     

公示语翻译错误之分析——以金华地区广告、标识和招牌英语使用错误为例

本文从金华市公示语,包括广告、标识和招牌等英语使用调查入手,分析了公示语翻译错误的类型,以及产生这些翻译错误的原因。最后从实际出发提出了两点结论和四条如何改进这些翻译错误的建议。