Extending the Bradley–Terry model for paired comparisons to accommodate weights

In the method of paired comparisons (PCs), treatments are compared on the basis of qualitative characteristics they possess, in the light of their sensory evaluations made by judges. However, there may emerge the situations where in addition to qualitative merits/worths, judges may assign quantitative weights to reflect/specify the relative importance of the treatments. In this study, an attempt is made to reconcile the qualitative and the quantitative PCs through assigning quantitative weights to treatments having qualitative merits using/extending the Bradley–Terry (BT) model. Behaviors of the existing BT model and the proposed weighted BT model are studied through the test of goodness-of-fit. Experimental and simulated data sets are used for illustration.     

Savings–Investment Correlation and Capital Outflow: The Case of Pakistan

This study explores the savings–investment relationship in the context of financial liberalization and flexible exchange rate regime in Pakistan. Ng–Perron test is employed to examine the order of integration of the variables used in the model. For long-run analysis, ARDL bounds testing approach is used and short-run dynamics are captured from error correction model (ECM). Time series data are utilized covering the period 1976–2006. Empirical findings indicate that in the case of Pakistan, there is a weak correlation between savings and investment. The study suggests that in the presence of inadequate capital mobility within the country, domestic investors have financed investment projects from international market. Furthermore, devaluation and inflation have stimulated investment activities in the country and significantly contributed in closing the gap between domestic savings and investment.     

Optimal block designs for three treatments when observations are correlated

E$E$-optimal designs for comparing three treatments in blocks of size three are identified, where intrablock observations are correlated according to a first order autoregressive error process with parameter ρ∈(0,1)$\rho \in (0,1)$. For number of blocks b   of the form b=3n+1$b=3n+1$, there are two distinct optimal designs depending on the value of ρ$\rho$, with the best design being unequally replicated for large ρ$\rho$. For other values of b$b$, binary, equireplicate designs with specified within-block assignment patterns are best. In many cases, the stronger majorization optimality is established.     

EXPONENTIAL SMOOTHING AND NON‐NEGATIVE DATA

The most common forecasting methods in business are based on exponential smoothing, and the most common time series in business are inherently non‐negative. Therefore it is of interest to consider the properties of the potential stochastic models underlying exponential smoothing when applied to non‐negative data. We explore exponential smoothing state space models for non‐negative data under various assumptions about the innovations, or error, process. We first demonstrate that prediction distributions from some commonly used state space models may have an infinite variance beyond a certain forecasting horizon. For multiplicative error models that do not have this flaw, we show that sample paths will converge almost surely to zero even when the error distribution is non‐Gaussian. We propose a new model with similar properties to exponential smoothing, but which does not have these problems, and we develop some distributional properties for our new model. We then explore the implications of our results for inference, and compare the short‐term forecasting performance of the various models using data on the weekly sales of over 300 items of costume jewelry. The main findings of the research are that the Gaussian approximation is adequate for estimation and one‐step‐ahead forecasting. However, as the forecasting horizon increases, the approximate prediction intervals become increasingly problematic. When the model is to be used for simulation purposes, a suitably specified scheme must be employed.     

Description and Propagation of Uncertainty in Input Parameters in Environmental Fate Models

Today, chemical risk and safety assessments rely heavily on the estimation of environmental fate by models. The key compound‐related properties in such models describe partitioning and reactivity. Uncertainty in determining these properties can be separated into random and systematic (incompleteness) components, requiring different types of representation. Here, we evaluate two approaches that are suitable to treat also systematic errors, fuzzy arithmetic, and probability bounds analysis. When a best estimate (mode) and a range can be computed for an input parameter, then it is possible to characterize the uncertainty with a triangular fuzzy number (possibility distribution) or a corresponding probability box bound by two uniform distributions. We use a five‐compartment Level I fugacity model and reported empirical data from the literature for three well‐known environmental pollutants (benzene, pyrene, and DDT) as illustrative cases for this evaluation. Propagation of uncertainty by discrete probability calculus or interval arithmetic can be done at a low computational cost and gives maximum flexibility in applying different approaches. Our evaluation suggests that the difference between fuzzy arithmetic and probability bounds analysis is small, at least for this specific case. The fuzzy arithmetic approach can, however, be regarded as less conservative than probability bounds analysis if the assumption of independence is removed. Both approaches are sensitive to repeated parameters that may inflate the uncertainty estimate. Uncertainty described by probability boxes was therefore also propagated through the model by Monte Carlo simulation to show how this problem can be avoided.     

How community embeddedness of public sector employees is formed by organisational justice and leads to counterproductive work behaviour

Public Organization Review - This paper provides an empirical evidence based on conservation of resources theory that how organisational justice (OJ) forms the public employee’s embeddedness...     

Ignoring Leaders who Break Promises or Following God: How Depersonalization and Religious Faith Inform Employees’ Timely Work Efforts

This study investigates the relationship between employees’ perceptions of psychological contract breaches and their failure to meet work-related deadlines, with a particular focus on the mediating role of the depersonalization they assign to organizational authorities and the moderating role of their religious faith. Results based on multisource data, collected among employees and their supervisors in Pakistani organizations, show that an important factor that underpins the connection between beliefs about broken organizational promises and a diminished propensity to finish work on time is that employees depersonalize organizational leaders. This mediating effect is mitigated by employees’ religious faith. For organizations, this study thus identifies a key mechanism – exhibiting indifference to the people in charge – by which employees’ frustrations about resource-depleting contract breaches may inadvertently escalate into ineffective time management, and it identifies some workers among whom this counterproductive dynamic is less likely, namely, employees who can draw from their religious faith.     

The Impact of Organizational Change on Employee Turnover Intention: Does Stress Play a Mediating Role?

This study examined the relationship between change interventions and employee turnover intention to see if change interventions lead to increased employee turnover intention. Moreover, the mediating role of employee stress was tested. Data were collected from 162 respondents working at various administrative positions in a large public organization. The results indicate that employee turnover intention is positively correlated to human process, techno structural, human resources, and strategic interventions. Limitations and direction for future research are provided.     

Selection of suitable prior for the Bayesian mixture of a class of lifetime distributions under type-I censored datasets

This paper explores the study on mixture of a class of probability density functions under type-I censoring scheme. In this paper, we mold a heterogeneous population by means of a two-component mixture of the class of probability density functions. The parameters of the class of mixture density functions are estimated and compared using the Bayes estimates under the squared-error and precautionary loss functions. A censored mixture dataset is simulated by probabilistic mixing for the computational purpose considering particular case of the Maxwell distribution. Closed-form expressions for the Bayes estimators along with their posterior risks are derived for censored as well as complete samples. Some stimulating comparisons and properties of the estimates are presented here. A factual dataset has also been for illustration.     

Non Parametric Estimation of Second-Order Diffusion Equation by Using Asymmetric Kernels

In this paper, we study the non parametric estimation of drift coefficient and diffusion coefficient in the second-order diffusion equation by using the asymmetric kernel functions, based on the difference of discrete time observations. The basic idea relies upon replacing the symmetric kernel by asymmetric kernel and provides a new way of obtaining the non parametric estimation for second-order diffusion equation. Under the appropriate assumptions, we prove that the proposed estimators of second-order diffusion equation are consistent and asymptotically follow normal distribution.