Minimally informative distributions with given rank correlation for use in uncertainty analysis

Minimum information bivariate distributions with uniform marginals and a specified rank correlation are studied in this paper. These distributions play an important role in a particular way of modeling dependent random variables which has been used in the computer code UNICORN for carrying out uncertainty analyses. It is shown that these minimum information distributions have a particular form which makes simulation of conditional distributions very simple. Approximations to the continuous distributions are discussed and explicit formulae are determined. Finally a relation is discussed to DAD theorems, and a numerical algorithm is given (which has geometric rate of covergence) for determining the minimum information distributions.     

Incomplete categorical data analysis: a bayesian perspective

In this paper the Bayesian analysis of incomplete categorical data under informative general censoring proposed by Paulino and Pereira (1995) is revisited. That analysis is based on Dirichlet priors and can be applied to any missing data pattern. However, the known properties of the posterior distributions are scarce and therefore severe limitations to the posterior computations remain. Here is shown how a Monte Carlo simulation approach based on an alternative parameterisation can be used to overcome the former computational difficulties. The proposed simulation approach makes available the approximate estimation of general parametric functions and can be implemented in a very straightforward way.     

LS estimation of periodic autoregressive models with non-Gaussian errors: a simulation study

In this article, the least squares (LS) estimates of the parameters of periodic autoregressive (PAR) models are investigated for various distributions of error terms via Monte-Carlo simulation. Beside the Gaussian distribution, this study covers the exponential, gamma, student-t, and Cauchy distributions. The estimates are compared for various distributions via bias and MSE criterion. The effect of other factors are also examined as the non-constancy of model orders, the non-constancy of the variances of seasonal white noise, the period length, and the length of the time series. The simulation results indicate that this method is in general robust for the estimation of AR parameters with respect to the distribution of error terms and other factors. However, the estimates of those parameters were, in some cases, noticeably poor for Cauchy distribution. It is also noticed that the variances of estimates of white noise variances are highly affected by the degree of skewness of the distribution of error terms.     

Simulation techniques for generalized Gaussian densities

This contribution deals with the Monte Carlo simulation of generalized Gaussian random variables. Such a parametric family of distributions has been proposed in many applications in science to describe physical phenomena and in engineering, and it seems to be also useful in modelling economic and financial data. For values of the shape parameter α within a certain range, the distribution presents heavy tails. In particular, the cases α=1/3 and α=1/2 are considered. For such values of the shape parameter, different simulation methods are assessed.     

Comparing diagnostic tests: test of hypothesis for likelihood ratios

Likelihood ratios (LRs) are used to characterize the efficiency of diagnostic tests. In this paper, we use the classical weighted least squares (CWLS) test procedure, which was originally used for testing the homogeneity of relative risks, for comparing the LRs of two or more binary diagnostic tests. We compare the performance of this method with the relative diagnostic likelihood ratio (rDLR) method and the diagnostic likelihood ratio regression (DLRReg) approach in terms of size and power, and we observe that the performances of CWLS and rDLR are the same when used to compare two diagnostic tests, while DLRReg method has higher type I error rates and powers. We also examine the performances of the CWLS and DLRReg methods for comparing three diagnostic tests in various sample size and prevalence combinations. On the basis of Monte Carlo simulations, we conclude that all of the tests are generally conservative and have low power, especially in settings of small sample size and low prevalence.     

Latin hypercube sampling with multidimensional uniformity

Complex models can only be realized a limited number of times due to large computational requirements. Methods exist for generating input parameters for model realizations including Monte Carlo simulation (MCS) and Latin hypercube sampling (LHS). Recent algorithms such as maximinLHS seek to maximize the minimum distance between model inputs in the multivariate space. A novel extension of Latin hypercube sampling (LHSMDU) for multivariate models is developed here that increases the multidimensional uniformity of the input parameters through sequential realization elimination. Correlations are considered in the LHSMDU sampling matrix using a Cholesky decomposition of the correlation matrix. Computer code implementing the proposed algorithm supplements this article. A simulation study comparing MCS, LHS, maximinLHS and LHSMDU demonstrates that increased multidimensional uniformity can significantly improve realization efficiency and that LHSMDU is effective for large multivariate problems.     

基于同城化视角的人口空间动态与区域经济发展耦合关联机理研究

人口空间动态与区域经济发展的耦合关系是同城化区域间共同面对的重要课题。本文在解构人口空间动态维度主体的基础上,将人口空间动态的诉求机制与动因机理架构于更低交易费用的利益诉求之上,解析人口空间动态的模式选择,深入探讨人口空间动态与区域经济发展的耦合关联性。在此基础上,提出引导人口流动与合理分布的策略。研究成果将有助于更好地理解同城化态势下人口空间演变的动因机理,为同城区域的经济发展、城市规划、文化交融等方面的科学设计提供重要参考。     

The duty to care and the need to split

This paper arises out of psychoanalytically oriented consultancy to teams of staff in the helping professions where there is a statutory 'duty to care'. It takes as its premise the seemingly paradoxical hypothesis that workers may need to split off part of their emotional experience in order to preserve their own mental health and provide reliable services to their clients. I argue that while a professional 'duty to care' requires us to be emotionally 'in touch', the demands of our clients together with the demands of the institutional response to the 'duty to care' cause us to split off parts of our awareness. I also argue that provided the splitting does not become extreme we are doing no more or less than the rest of society. In other words, there is a degree of 'normal splitting' which numbs our awareness of danger and destructiveness and seeks to protect us from too much anxiety and pain. Yet if professional workers are charged with the responsibility of assessing risk and acting accordingly for the protection of all concerned they need ways of being 'in touch' (re-integrating the splits) for some or enough of the time. Finally, I will describe ways of being 'in touch', illustrating the difficulty and the pain of re-integrating the splits and some of the insights that can arise out of this work with examples from my consultancy work.     

Monte carlo estimates of the distributions of the random polygons of the voronoi tessellation with respect to a poisson process

A large-scale study, in which two million random Voronoi polygons (with respect to a homogeneous Poisson point process) were generated and mensurated, is described. The polygon characteristics recorded are number of sides (or vertices), perimeter, area and interior angles. A feature is the efficient “quantile” method of replicating Poisson-type random structures, which it is hoped may find useful application elsewhere.     

Extending systems thinking in planning and evaluation using group concept mapping and system dynamics to tackle complex problems

Group concept mapping (GCM) has been successfully employed in program planning and evaluation for over 25 years. The broader set of systems thinking methodologies (of which GCM is one), have only recently found their way into the field. We present an overview of systems thinking emerging from a system dynamics (SD) perspective, and illustrate the potential synergy between GCM and SD. As with GCM, participatory processes are frequently employed when building SD models; however, it can be challenging to engage a large and diverse group of stakeholders in the iterative cycles of divergent thinking and consensus building required, while maintaining a broad perspective on the issue being studied. GCM provides a compelling resource for overcoming this challenge, by richly engaging a diverse set of stakeholders in broad exploration, structuring, and prioritization. SD provides an opportunity to extend GCM findings by embedding constructs in a testable hypothesis (SD model) describing how system structure and changes in constructs affect outcomes over time. SD can be used to simulate the hypothesized dynamics inherent in GCM concept maps. We illustrate the potential of the marriage of these methodologies in a case study of BECOMING, a federally-funded program aimed at strengthening the cross-sector system of care for youth with severe emotional disturbances.