Identification of significant threats and errors affecting aviation safety in Taiwan using the analytical hierarchy process

This study ranked the significant threats and human errors affecting aviation safety. Two-phase questionnaires were used to elicit experts' views on four categories of human errors and threats comprising twenty-three criteria. The analytical hierarchy process was used to calculate the weight for each criteria which were then ranked in order of importance. The study indicated that flight crew errors are the most important threats.     

To Sue or Not to Sue: An Experimental Study of Factors Affecting Hong Kong Liquidators Audit Litigation Decisions

We report an experiment examining the effect of three factors on professional Hong Kong liquidators' decisions to bring legal action in negligence against auditors. Factors were (a) the strength (merit) of the supporting evidence (arguable vs. overwhelming), (b) the type of alleged audit failure (failure to report financial statement errors vs. management fraud) and (c) audit firm type (Big 6 vs. non-Big 6). We find evidence that liquidators' litigation decisions are influenced by case merit. We also find that liquidators were marginally more likely to institute legal action against a Big 6 than against a non-Big 6 auditor. However, we find no evidence that the type of alleged audit failure influences litigation decisions.     

Approximate inference in multivariate nonlinear functional relationships

An estimation procedure based on estimating equations is presented for the parameters in a multivariate functional relationship model, where all observations are subject to error. The covariance matrix of the observational errors may be parametrized and is allowed to be different for different sets of observations. Estimators are defined for the unknown relation parameters and error parameters.
For linear models (i.e. where the model function is linear in the incidental parameters) the estimators are consistent and asymptotically normal. A consistent expression for the covariance matrix of the estimators is derived. The results are valid for general error distributions.
For nonlinear models the estimators are based on locally linear approximations to the model function. The afore mentioned properties of the estimators are now only approximately valid. The adequacy of the approximate inference, based on asymptotic theory for the linearized model, needs at least informal check. Some examples are given to illustrate the estimation procedure.     

Decision making and planning under low levels of predictability

This special section aims to demonstrate the limited predictability and high level of uncertainty in practically all important areas of our lives, and the implications of this. It summarizes the huge body of solid empirical evidence accumulated over the past several decades that proves the disastrous consequences of inaccurate forecasts in areas ranging from the economy and business to floods and medicine. The big problem is, however, that the great majority of people, decision and policy makers alike, still believe not only that accurate forecasting is possible, but also that uncertainty can be reliably assessed. Reality, however, shows otherwise, as this special section proves. This paper discusses forecasting accuracy and uncertainty, and distinguishes three distinct types of predictions: those relying on patterns for forecasting, those utilizing relationships as their basis, and those for which human judgment is the major determinant of the forecast. In addition, the major problems and challenges facing forecasters and the reasons why uncertainty cannot be assessed reliably are discussed using four large data sets. There is also a summary of the eleven papers included in this special section, as well as some concluding remarks emphasizing the need to be rational and realistic about our expectations and avoid the common delusions related to forecasting.     

Validity of climate change forecasting for public policy decision making

Policymakers need to know whether prediction is possible and, if so, whether any proposed forecasting method will provide forecasts that are substantially more accurate than those from the relevant benchmark method. An inspection of global temperature data suggests that temperature is subject to irregular variations on all relevant time scales, and that variations during the late 1900s were not unusual. In such a situation, a “no change” extrapolation is an appropriate benchmark forecasting method. We used the UK Met Office Hadley Centre’s annual average thermometer data from 1850 through 2007 to examine the performance of the benchmark method. The accuracy of forecasts from the benchmark is such that even perfect forecasts would be unlikely to help policymakers. For example, mean absolute errors for the 20- and 50-year horizons were 0.18  C and 0.24  C respectively. We nevertheless demonstrate the use of benchmarking with the example of the Intergovernmental Panel on Climate Change’s 1992 linear projection of long-term warming at a rate of 0.03  C per year. The small sample of errors from ex ante projections at 0.03  C per year for 1992 through 2008 was practically indistinguishable from the benchmark errors. Validation for long-term forecasting, however, requires a much longer horizon. Again using the IPCC warming rate for our demonstration, we projected the rate successively over a period analogous to that envisaged in their scenario of exponential CO₂ growth—the years 1851 to 1975. The errors from the projections were more than seven times greater than the errors from the benchmark method. Relative errors were larger for longer forecast horizons. Our validation exercise illustrates the importance of determining whether it is possible to obtain forecasts that are more useful than those from a simple benchmark before making expensive policy decisions.     

零件设计中形位公差项目的选择

在进行零件设计时,正确选择形位公差项目及其公差值的确定是一项很慎重的工作,既要根据零件的使用性能和互换性要求,又要考虑生产率和成本的高低。本文仅对形位公差项目的选择进行初步的探讨。     

Quasi Score is more Efficient than Corrected Score in a Polynomial Measurement Error Model

We consider a polynomial regression model, where the covariate is measured with Gaussian errors. The measurement error variance is supposed to be known. The covariate is normally distributed with known mean and variance. Quasi score (QS) and corrected score (CS) are two consistent estimation methods, where the first makes use of the distribution of the covariate (structural method), while the latter does not (functional method). It may therefore be surmised that the former method is (asymptotically) more efficient than the latter one. This can, indeed, be proved for the regression parameters. We do this by introducing a third, so-called simple score (SS), estimator, the efficiency of which turns out to be intermediate between QS and CS. When one includes structural and functional estimators for the variance of the error in the equation, SS is still more efficient than CS. When the mean and variance of the covariate are not known and have to be estimated as well, one can still maintain that QS is more efficient than SS for the regression parameters.     

Further thoughts on simplicity and complexity in population projection models

"This article is a review of--and response to--a special issue of Mathematical Population Studies that focused on the relative performance of simpler vs. more complex population projection models. I do not attempt to summarize or comment on each of the articles in the special issue, but rather present an additional perspective on several points: definitions of simplicity and complexity, empirical evidence regarding population forecast accuracy, the costs and benefits of disaggregation, the potential benefits of combining forecasts, criteria for evaluating projection models, and issues of economic efficiency in the production of population projections."     

The smoothing dichotomy in nonparametric regression under long-memory errors

The phenomenon of smoothing dichotomy in random-design nonparametric regression is exposed in nontechnical terms from two recent papers published jointly with Jan Mielniczuk. This concerns the asymptotic distribution of kernel estimators when the errors exhibit long-range dependence, being instantaneous functions either of Gaussian sequences or of infinite-order moving averages, depending on the amount of smoothing.     

我国A股市场流动性有效度量的实证研究——基于LOT模型与高频交易数据的比较分析

首先以1997年1月1日至2010年12月31日的高频交易数据为基础,计算了我国A股市场反映交易成本维度的流动性基准(报价价差基准)和反映价格影响的流动性基准(报价价差价格影响基准)。然后利用低频日度交易数据,根据LOT族模型计算出流动性的交易成本维度和价格影响维度的代理变量值。最后利用平均横截面相关性、投资组合时间序列相关性和均方预测误差三个"业绩"评价标准评价了流动性代理变量的有效性。结果发现,在反映流动性的交易成本维度方面,LOT_Y代理变量表现得最好;在反映流动性的价格影响方面,LOT_S表现得相对较好;从均方预测误差角度来看,LOT_Y表现得相对较好。