Computational approach test for inference about several correlation coefficients: Equality and common

In this article, we consider inference about the correlation coefficients of several bivariate normal distributions. We first propose computational approach tests for testing the equality of the correlation coefficients. In fact, these approaches are parametric bootstrap tests, and simulation studies show that they perform very satisfactory, and the actual sizes of these tests are better than other existing approaches. We also present a computational approach test and a parametric bootstrap confidence interval for inference about the parameter of common correlation coefficient. At the end, all the approaches are illustrated using two real examples.     

Topics on Dynamic Panel Data Models with Random Effects Using Semi-Parametric Bayesian Approach

A common assumption in fitting panel data models is normality of stochastic subject effects. This can be extremely restrictive, making vague most potential features of true distributions. The objective of this article is to propose a modeling strategy, from a semi-parametric Bayesian perspective, to specify a flexible distribution for the random effects in dynamic panel data models. This is addressed here by assuming the Dirichlet process mixture model to introduce Dirichlet process prior for the random-effects distribution. We address the role of initial conditions in dynamic processes, emphasizing on joint modeling of start-up and subsequent responses. We adopt Gibbs sampling techniques to approximate posterior estimates. These important topics are illustrated by a simulation study and also by testing hypothetical models in two empirical contexts drawn from economic studies. We use modified versions of information criteria to compare the fitted models.     

Marginalized random-effects models for clustered binomial data through innovative link functions

AStA Advances in Statistical Analysis - Random-effects models are frequently used to analyze clustered binomial data. The direct computation of the marginal mean response, when integrated over the...     

INDETERMINACY AND VOLATILITY OF EXCHANGE RATES UNDER IMPERFECT CURRENCY SUBSTITUTION

We know that when currencies are perfect substitutes, exchange rates could become indeterminate. We show that even when currencies are less than perfect substitutes exchange rates could display volatility unrelated to economic fundamentals. With increases in currency substitution: (1) the exchange rate becomes more sensitive to changes in economic fundamentals, increasing its volatility; (2) the exchange rate could become indeterminate, and it is more likely to become so if governments pursue similar monetary policies; (3) currencies with high nominal interest rates would decline significantly and the exchange rate becomes more sensitive to changes in the supply of those currencies.     

A critical investigation of Industry 4.0 in manufacturing: theoretical operationalisation framework

Increasing global competition on product quality and production costs, and the need for flexibility in production petition for transformed production processes which enable high level of connectivity and integration between business processes and systems. Much of the conventional computer- integrated efforts and advanced manufacturing technologies are limited in scope and restricted to only some organisational areas. Such limited scope, which stems from limited connectivity and integration between manufacturing and enterprise systems, confines the achievement of full potential of these systems within manufacturing. Industry 4.0, characterised by computing developments, can create a platform for addressing integration challenge through enabling comprehensive connectivity. Hence, this paper, through following deductive research paradigm and using systems theory as the theoretical base, aims to investigate recent academic research and industrial reports in the area of Industry 4.0 and smart manufacturing to provide detailed insights on execution of Industry 4.0, and to propose a theoretical framework for operationalisation of Industry 4.0 in manufacturing.     

The Public Sector and income distribution in South Africa during the period 1970–82

This study considers the impact of fiscal and monetary policy on the distribution of income in South Africa. It analyses expenditure using two approaches: (1) the general approach which considers the extent of programmes having a relatively high distributive contact in favour of the poor and (2) the direct money‐flow approach which assumes that expenditure benefits those who receive it as their income. Although neither approach is perfect, it can be concluded that the general composition of expenditure has favoured the poor. It is also argued that the tax system is partly progressive and partly regressive and that it has become more regressive, although remaining in balance progressive. Monetary policy during the 1970s, however, has had definite regressive income distribution effects.     

A Hybrid Methodology for Modeling Risk of Adverse Events in Complex Health‐Care Settings

In spite of increased attention to quality and efforts to provide safe medical care, adverse events (AEs) are still frequent in clinical practice. Reports from various sources indicate that a substantial number of hospitalized patients suffer treatment‐caused injuries while in the hospital. While risk cannot be entirely eliminated from health‐care activities, an important goal is to develop effective and durable mitigation strategies to render the system “safer.” In order to do this, though, we must develop models that comprehensively and realistically characterize the risk. In the health‐care domain, this can be extremely challenging due to the wide variability in the way that health‐care processes and interventions are executed and also due to the dynamic nature of risk in this particular domain. In this study, we have developed a generic methodology for evaluating dynamic changes in AE risk in acute care hospitals as a function of organizational and nonorganizational factors, using a combination of modeling formalisms. First, a system dynamics (SD) framework is used to demonstrate how organizational‐level and policy‐level contributions to risk evolve over time, and how policies and decisions may affect the general system‐level contribution to AE risk. It also captures the feedback of organizational factors and decisions over time and the nonlinearities in these feedback effects. SD is a popular approach to understanding the behavior of complex social and economic systems. It is a simulation‐based, differential equation modeling tool that is widely used in situations where the formal model is complex and an analytical solution is very difficult to obtain. Second, a Bayesian belief network (BBN) framework is used to represent patient‐level factors and also physician‐level decisions and factors in the management of an individual patient, which contribute to the risk of hospital‐acquired AE. BBNs are networks of probabilities that can capture probabilistic relations between variables and contain historical information about their relationship, and are powerful tools for modeling causes and effects in many domains. The model is intended to support hospital decisions with regard to staffing, length of stay, and investments in safety, which evolve dynamically over time. The methodology has been applied in modeling the two types of common AEs: pressure ulcers and vascular‐catheter‐associated infection, and the models have been validated with eight years of clinical data and use of expert opinion.