Exploring the effects of workplace support on work-life experience: a study of Malaysia

To date most studies of workplace support and work-life experience have been of contexts where government policies and legislation to protect employee work-life balance interests are well established, such as US, UK, and other European countries. Little scrutiny has been given to these issues in less developed economies, where support and protection in terms of work-life policies and legislation at the national level is rather limited. Malaysia, the setting of this study, provides such a context. Two types of organizational support, work flexibility and superior support, are studied for their impact on work-life experience of Malaysians. The findings of this study are based on a national survey of working adults in Malaysia. Work-life experience is conceptualized to capture conflict and enrichment aspects, as well as bidirectional effects between work and nonwork. Results show that work flexibility and superior support lower work-life conflict marginally but facilitate greater work-life enrichment among Malaysians. The paper also draws out implications of these findings for human resource development professionals operating in workplace settings within national contexts with limited mandatory work-life provisions.     

Modeling Electricity Price Using A Threshold Conditional Autoregressive Geometric Process Jump Model

Electricity market prices are highly volatile and often have high spikes. Both government authorities and market participants require sophisticated models and techniques for forecasting future prices and managing relevant financial risks in such a volatile market. This article extends the conditional autoregressive geometric process (CARGP) model (Chan et al., 2012 Chan, J. S.K., Lam, C. P.Y., Yu, P. L.H., Choy, S. T.B., Chen, C. W.S. (2012). A Bayesian conditional autoregressive geometric process model for range data. Computat. Statist. Data Anal. 56:30063019.[Crossref], [Web of Science ®] , [Google Scholar]) to the CARGP model with thresholds and jumps, which is abbreviated as CARGP-TJ model in this article. We will demonstrate that the proposed CARGP-TJ model not only captures the unique features of the electricity price but also performs better than other existing models. For robustness consideration, a heavy-tailed error distribution is adopted. Model implementation relies on the powerful Bayesian Markov chain Monte Carlo simulation techniques via WinBUGS software. The analysis of the daily maximum electricity prices of the New South Wales, Australia reveals that the proposed CARGP-TJ model captures the price spikes well for both in-sample estimation and out-of-sample forecast.     

Recurrence relations for moments of record values from generalized extreme value distribution

In this paper some recurrence relations between the moments of record values from the generalized extreme value distribution are established. It is shown that using these recurrence relations, all the single and product moments of all record values can be obtained in a simple recursive manner.     

On the distribution of some ordered variables

Abstract

Motivated by a recent article published by Adam and Tawn, we characterize the distribution of two random variables X, Y ordered linearly like X < Y. We suppose that the random variables follow a bivariate extreme value distribution.     

Some issues in the design of EWMA Charts

The traditional design procedure for selecting the parameters of EWMA charts is based on the average run length (ARL). It is shown that for some types of EWMA charts, such a procedure may lead to high probability of a false out-of-control signal. An alternative procedure based on both the ARL and the standard deviation of run length (SRL) is recommended. It is shown that, with the new procedure, the EWMA chart using its exact variance can detect moderate and large shifts of the process mean faster.     

Multivariate modelling of spatial extremes based on copulas

To model extreme spatial events, a general approach is to use the generalized extreme value (GEV) distribution with spatially varying parameters such as spatial GEV models and latent variable models. In the literature, this approach is mostly used to capture spatial dependence for only one type of event. This limits the applications to air pollutants data as different pollutants may chemically interact with each other. A recent advancement in spatial extremes modelling for multiple variables is the multivariate max-stable processes. Similarly to univariate max-stable processes, the multivariate version also assumes standard distributions such as unit-Fréchet as margins. Additional modelling is required for applications such as spatial prediction. In this paper, we extend the marginal methods such as spatial GEV models and latent variable models into a multivariate setting based on copulas so that it is capable of handling both the spatial dependence and the dependence among multiple pollutants. We apply our proposed model to analyse weekly maxima of nitrogen dioxide, sulphur dioxide, respirable suspended particles, fine suspended particles, and ozone collected in Pearl River Delta in China.     

社会工作者素质模型设计

社会工作者素质模型的构建对于社会工作人才队伍建设有重要意义。文章以9位社会工作领域的优秀人才为访谈对象,通过行为事件访谈法,提炼出优秀社会工作者应当具备的四项基本素质。根据受访者体现素质的具体行为,文章还归纳出这四项基本素质的二次分级及其定义。     

外语能力磨蚀的性别差异初探

对300余名大学生进行了英语阅读和听力测试,并从中随机抽取男女生各50名进行了写作测试,得出如下结论:(1)外语磨蚀程度存在明显的性别差异;(2)男女生在深度阅读、短对话听力以及段落听力方面的磨蚀差异不显著;(3)女生在快速阅读能力方面的磨蚀程度明显超过男生;(4)女生写作能力在切题性、连贯性以及清楚性方面的磨蚀程度显著大于男生,而在语言的正确性方面的磨蚀没有明显超过男生.     

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.