Measuring Loneliness Among Middle-Aged and Older Adults: The UCLA and de Jong Gierveld Loneliness Scales

This study examined the measurement and invariance properties of the R-UCLA and de Jong Gierveld loneliness scales for research involving middle-aged and older adults. Confirmatory factor analyses (CFAs) of data from interviews with adults aged 45–84 revealed limited support for the unidimensionality of either scale while subsequent analyses indicated the multidimensionality of both. However, method effects associated with positive and negative item wording were also evident. Multiple group CFAs provided limited support for assumptions of measurement invariance across age groups and from baseline to follow-up with regard to the R-UCLA scale. In contrast, strong measurement invariance across age groups and successive measurements was established for the bi-dimensional dJG scale. Overall, the findings supported the relative utility of the dJG scale for research involving middle-aged and older adults but suggested a need for attention to the implications of method effects associated with item wording and lack of measurement invariance with respect to item residuals.     

On comparing,classifying and clustering several dependent regression models

Classifying several regression models fitted on a dataset is one of the most problems in data analysis. In other words, scientists are interested in comparing several regression models that can be used for a dataset. In this paper, an approach will be used to compare and classify several dependent regression models. Then the performance of the proposed method is investigated using simulation study and real example.     

The influence function of the optimal bandwidth for kernel density estimation

Theories about the bandwidth of kernel density estimation have been well established by many statisticians. However, the influence function of the bandwidth has not been well investigated. The influence function of the optimal bandwidth that minimizes the mean integrated square error is derived and the asymptotic property of the bandwidth selectors based on the influence function is provided.     

Testing the equality of two independent regression models

In some situations, for example in agriculture, biology, hydrology, and psychology, researchers wish to determine whether the relationship between response variable and predictor variables differs in two populations. In other words, we are interested in comparing two regression models for two independent datasets. In this work, we will use the parametric and nonparametric methods to establish hypothesis testing for the equality of two independent regression models. Then the simulation study is provided to investigate the performance of the proposed method.     

An Entropy‐Based Methodology for Valuation of Demand Uncertainty Reduction

We propose a distribution‐free entropy‐based methodology to calculate the expected value of an uncertainty reduction effort and present our results within the context of reducing demand uncertainty. In contrast to existing techniques, the methodology does not require a priori assumptions regarding the underlying demand distribution, does not require sampled observations to be the mechanism by which uncertainty is reduced, and provides an expectation of information value as opposed to an upper bound. In our methodology, a decision maker uses his existing knowledge combined with the maximum entropy principle to model both his present and potential future states of uncertainty as probability densities over all possible demand distributions. Modeling uncertainty in this way provides for a theoretically justified and intuitively satisfying method of valuing an uncertainty reduction effort without knowing the information to be revealed. We demonstrate the methodology's use in three different settings: (i) a newsvendor valuing knowledge of expected demand, (ii) a short life cycle product supply manager considering the adoption of a quick response strategy, and (iii) a revenue manager making a pricing decision with limited knowledge of the market potential for his product.     

Integration of project management,human resource development,and business teams: a partnership,planning model for organizational training and development initiatives

The changing business environment has challenged organizations to explore unique ways to ensure the success of online training initiatives. Thus, project teams, including training managers, should not only support organizational training and development initiatives; but, they also need to use project-focused, planning models for implementation. This paper proposes a model that is an extension of the model for business and training partnership developed by Price. The proposed model includes the integration of project management, human resource development (HRD), and business teams in order to enhance the success of training implementations through a sequential, planned framework. The model includes phases, best practices, and supporting tools to assist in managing and administering training and development projects. The approach developed in this research includes an account of the activities associated with initiating, planning, executing, monitoring and controlling, and closing processes for an online training initiative conducted in an oil and gas organization. This research also discusses implications of the proposed model for HRD professionals, because they are typically the champion of training and development initiatives within organizations today.     

$$$$-labeling number of Cartesian product of path and cycle

For positive numbers \(j\) and \(k\), an \(L(j,k)\)-labeling \(f\) of \(G\) is an assignment of numbers to vertices of \(G\) such that \(|f(u)-f(v)|\ge j\) if \(d(u,v)=1\), and \(|f(u)-f(v)|\ge k\) if \(d(u,v)=2\). The span of \(f\) is the difference between the maximum and the minimum numbers assigned by \(f\). The \(L(j,k)\)-labeling number of \(G\), denoted by \(\lambda _{j,k}(G)\), is the minimum span over all \(L(j,k)\)-labelings of \(G\). In this article, we completely determine the \(L(j,k)\)-labeling number (\(2j\le k\)) of the Cartesian product of path and cycle.     

The Minimum Density Power Divergence Estimation for the Lognormal Density

In this article, we implement the minimum density power divergence estimation for estimating the parameters of the lognormal density. We compare the minimum density power divergence estimator (MDPDE) and the maximum likelihood estimator (MLE) in terms of robustness and asymptotic distribution. The simulations and an example indicate that the MDPDE is less biased than MLE and is as good as MLE in terms of the mean square error under various distributional situations.     

DISPERSION CONTROL FOR MULTIVARIATE PROCESSES – SOME COMPARISONS

Tang & Barnett (1996) present some techniques for monitoring and controlling the dispersion of multivariate normal processes based on subgroup data. The current paper compares the proposed techniques and various competing procedures. Simulation results indicate that the proposed techniques are superior to existing procedures.