Identifying traffic safety best practice: an application of DEA and Malmquist indices

DEA is used in this paper to investigate target achievements of the operational units of the Norwegian Public Roads Administration (NPRA) charged with traffic safety services. The DEA framework applied corresponds to a BCC model with a unique constant input, or equivalently, with no inputs. This framework is further extended to a DEA-based Malmquist index to measure productivity growth in target achievements. Finally, we use a bootstrapping method to ascertain confidence intervals for efficiency scores derived and to test hypotheses on the extent of productivity growth or regress. The mean efficiency scores by which targets are achieved across the sample years are in the range 0.81–0.93 and significant at the 5% level. Total productivity in target achievements shows progress with significance, on average at 7%. Much of the progress is attributed to technological progress. The results illustrate the usefulness of using a decomposable index for productivity measurement, and the use of bootstrapping for sensitivity tests.     

Benefits and limitations of panel data

Observations for a number of cross-sectional units over time have become increasingly available. The new data sources enable econometricians to construct and test more complicated behavioral models than a single cross sectional or time series data set would allow. The availability of new data sources, however, also raises new issues. In this paper we review some basic econo- metric methods that have been used to analyze such data sets. We also indicate areas of research where panel data may be useful.     

A lagrange multiplier test for the error components model with incomplete panels

This Paper extends the Breusch and Pagan(1980) lagrange Multiplier test for the random effects model to the incomplete panel data case. It is shown that this test retains the simple additive structure observed in the complete panel data case. It should prove useful for practitioners facing incomplete panel data applications.     

Hassles, hardiness and absenteeism: Results of a 3-year longitudinal study

In this 3-year longitudinal study of 229 full-time employees, the authors investigated the association between hassles, two measures of personality hardiness, and absenteeism verified from medical personnel records and self-reported hospitalization owing to injury and illness. Using stepwise multiple regression analysis, hassles, but neither of the hardiness measures, significantly predicted absenteeism when controlling for psychological well-being and relevent demographic variables over the 3-year period. The alternative measure of hardiness, but not any of the original Kobasa personality hardiness scales, predicted self-reported hospitalization for injury and illness. Little evidence for the predicitve validity of the Kobasa personality hardiness components, or composite hardiness score, existed for either absenteeism or self-reported hospitalization in this study. These findings support the concept that the current conceptualization, measurement and use of the original Kobasa hardiness scales should be re-evaluated.     

中西文指法训练与测试系统

本文提出一种新型指法训练软件．该软件采用逻辑程序设计语言ＴｕｒｂｏＰｒｏｌｏｇ２．０实现，较好地解决了其它指法训练软件或者全西文使用不便，或者全中文，只能训练汉字输入，并且用户无法更新训练内容等特点，适用于多种文字及数字、标点等符号的录入训练。     

Teacher stress and coping over an autumn term in Finland

Psychological stress, coping process and the relationships between stress and coping were examined among 66 teachers representing comprehensive and upper secondary schools in Finland. The research design was longitudinal. Each person was studied four times during the autumn term of 1991 using questionnaires, which were focused on teachers' stress feelings, ways of coping and social relations to colleagues and pupils during Mondays to Thursdays and Friday morning lessons. The results showed a clear accumulation of stress during the autumn term. Four different teacher groups emerged according to the type of stress reported: (1) teachers who were only moderately stressed, (2) those who were not at all stressed, (3) teachers who were exhausted throughout the term, and (4) those whose stress increased strongly during the term. These stress process groups differed with regard to their coping styles, but not background variables. On the level of the whole sample, different ways of coping were quite stable during the autumn term. The study is partially a replication of the longitudinal study on teachers' stress by Kinnunen (1989, 1988) and enables comparisons between stress process groupings between the years 1983 and 1991.     

提高BASIC应用程序执行效率的编程技巧

提高ＢＡＳＩＣ应用程序执行效率的编程技巧王晓霞，龚义荣（南阳师专数学系）（南阳三师）过去的电子计算机由于受硬件的影响，编程者在编制程序时，都要考虑到内存的容量，使程序尽量减少使用内存，甚至忽略了程序的可读性和不惜牺牲一些运行时间。随着电子计算机的不断...     

Nonparametric and non-linear models and data mining in time series: a case-study on the Canadian lynx data

Nonparametric regression methods are used as exploratory tools for formulating, identifying and estimating non-linear models for the Canadian lynx data, which have attained bench-mark status in the time series literature since the work of Moran in 1953. To avoid the curse of dimensionality in the nonparametric analysis of this short series with 114 observations, we confine attention to the restricted class of additive and projection pursuit regression (PPR) models and rely on the estimated prediction error variance to compare the predictive performance of various (non-)linear models. A PPR model is found to have the smallest (in-sample) estimated prediction error variance of all the models fitted to these data in the literature. We use a data perturbation procedure to assess and adjust for the effect of data mining on the estimated prediction error variances; this renders most models fitted to the lynx data comparable and nearly equivalent. However, on the basis of the mean-squared error of out-of-sample prediction error, the semiparametric model X_t =1.08+1.37 X_{t −1}+ f ( X_{t −2})+ e_t and Tong's self-exciting threshold autoregression model perform much better than the PPR and other models known for the lynx data.     

Longitudinal data analysis of mean passage time among malnutrition states: an application of Markov chains

Clinical prognosis of patients can be best described from a longitudinal study and a Markov regression model is an appropriate way of analyzing the prognosis of disease when the outcomes are serially dependent. Mean first passage time (MFPT) is a method to estimate the average number of transitions between the states of a Markov chain. The present study used the secondary data from a longitudinal study which was done during 1982–1986. This study was to illustrate the MFPT among the states of malnutrition, which were classified as Normal, Mild/Moderate and Severe among children aged 5–7 years, in South India. The 95% confidence interval (CI) for the MFPT was calculated using Monte Carlo simulation. Markov regression models were used to test for the association of state transitions across the risk factors. The average time taken for an underweight child to transit from Severe state of malnutrition to become Normal was nearly 2.73 (95% CI 2.60–2.86) years and 3.41 (95% CI 3.25–3.58) years in Rural area and 2.31(95% CI 2.20–2.42) in Urban area. The significant difference between the MFPT for some risk factors are useful to plan interventions. It will especially be useful to find the impact of duration among school-going children on their cognitive disorders.