Separating trend and cyclical dynamics in state space models with exogenous inputs

In the present study we compare three state rotation methods in modelling the impact of the US economy on the Finnish economy, i.e. Schur decomposition, eigenvalue analysis and singular value decomposition. Singular value decomposition is seen to provide a robust approximation of the state rotation in most cases studied, irrespective of whether the characteristic roots of the state transition matrix are complex. Thus, singular value decomposition seems to be a viable computational device not only in estimating the system matrices of the state space model, but also in state rotation, as compared to the more involved techniques based on eigenvalue analysis or Schur decomposition.     

Exploring the Michigan model: The relationship of personality, managerial support and organizational structure with health outcomes in entrants to the healthcare environment

Few studies have examined the role of managerial support in the adaptation of entrants to healthcare. The purpose of this study was to identify the job characteristic and role-related variables that mediate the effects of managerial support and personality on the mental and physical health and sickness absence in two independent groups of new recruits to a healthcare organization. Entrants to a healthcare environment (student nurses) were surveyed 25 weeks after entry. Latent variables represented neuroticism, demand, control, role clarity and affective outcome variables in a simplified version of the Michigan model of work stress (House, 1981). This study used a two-stage approach to structural equation modelling, exploring hypothesized structural models on an initial data set (N=195) prior to confirmation on an independent replication data set (N=239) using multi-sample methods. Finally the confirmed model of choice was evaluated using the combined data (N=434). Emotional distress reported at 6 months was found to be associated with dispositional characteristics of the entrant and indirectly, via job satisfaction, with managerial support from academic and clinical sources. Managerial support from academic and clinical sources had an indirect influence on job satisfaction, but by differing mechanisms involving role clarity and control. The demands perceived by the entrants arose from neuroticism and were independent of any other variable. Emotional distress had its deleterious association with sickness absence via somatic health. This study details a series of mechanisms that will subsequently be tested longitudinally.     

Interval estimates for diatom-inferred lake pH histories

We investigate ordinary least-squares and Bayesian methods for constructing interval estimates for historical lake pH's inferred from diatom sediments. The Bayesian method explicitly models several forms of variability, including the sampling and classification variability of the diatom records, estimation variability, and measurement error in observed pH's. The two methods produce similar interval estimates, but the Bayesian model allows design recommendations to be made.     

Analysis of fracture toughness data

In studies of the fracture toughness of irradiated weld metal, specimens are subjected to an increasing load. The test on any one specimen might be terminated by choice or because the specimen ruptures. Prior to termination, ductile tearing might or might not have occurred. The situation is thus basically one of competing risks, with different types of termination, but there are additional features. The major purpose of statistical analysis is to estimate probabilities concerning the values of toughness and crack length. The analysis has been based on a model developed for the joint survivor function of these quantities.     

On tail index estimation based on multivariate data

This article is devoted to the study of tail index estimation based on i.i.d. multivariate observations, drawn from a standard heavy-tailed distribution, that is, of which Pareto-like marginals share the same tail index. A multivariate central limit theorem for a random vector, whose components correspond to (possibly dependent) Hill estimators of the common tail index α, is established under mild conditions. We introduce the concept of (standard) heavy-tailed random vector of tail index α and show how this limit result can be used in order to build an estimator of α with small asymptotic mean squared error, through a proper convex linear combination of the coordinates. Beyond asymptotic results, simulation experiments illustrating the relevance of the approach promoted are also presented.     

Parameter estimation in semi-linear models using a maximal invariant likelihood function

In this paper, we consider the problem of estimation of semi-linear regression models. Using invariance arguments, Bhowmik and King [2007. Maximal invariant likelihood based testing of semi-linear models. Statist. Papers 48, 357–383] derived the probability density function of the maximal invariant statistic for the non-linear component of these models. Using this density function as a likelihood function allows us to estimate these models in a two-step process. First the non-linear component parameters are estimated by maximising the maximal invariant likelihood function. Then the non-linear component, with the parameter values replaced by estimates, is treated as a regressor and ordinary least squares is used to estimate the remaining parameters. We report the results of a simulation study conducted to compare the accuracy of this approach with full maximum likelihood and maximum profile-marginal likelihood estimation. We find maximising the maximal invariant likelihood function typically results in less biased and lower variance estimates than those from full maximum likelihood.     

Material planning with uncertain product quality†

Traditional material requirement planning (MRP) is a system procedure to determine input quantities and ordering times to meet a specified demand in a deterministic framework. A variety of modifications to MRP have been proposed to incorporate different forms of uncertainty in production systems. In this paper, wc consider the case where the uncertainty is due to quality variations in the production process, and we develop an approach which links material planning with quality variations and control.     

Frugal innovation for supply chain sustainability in SMEs: multi-method research design

Abstract

In this study, we attempt to establish the missing links between supply chain sustainability and frugal innovation. Our study motivations stem from two facets of the emerging markets: first, the institutional barriers and, second, the resource constraints. We argue that there is a synergy in the concepts of frugal innovation and sustainability in supply chains and there is a need to further explore this synergy. Furthermore, we claim that even in the wake of many success stories in the frugal innovative supply chain management practices from emerging markets such as India, there are very few, if any, attempts made to understand the implications of a sustainability oriented frugal innovations in the particular context. To address this gap, we develop a model to establish the linkage between sustainable supply chains and frugal innovations. Our proposed conceptual framework depicts the hierarchy and interlinks of the identified enablers in developing sustainability oriented frugal innovative capabilities in supply chains. Furthermore, we have empirically validated our theoretical framework using survey data. We observed that most of the interpretive links are supported. These findings extend the understanding of frugal innovation for supply chain sustainability using multi-method research design, while also providing theoretically guidance to managers in the development of frugal innovation capability to achieve sustainability in supply chain in resource constrained environment.     

Meta-analysis using multilevel models with an application to the study of class size effects

Meta-analysis is formulated as a special case of a multilevel (hierarchical data) model in which the highest level is that of the study and the lowest level that of an observation on an individual respondent. Studies can be combined within a single model where the responses occur at different levels of the data hierarchy and efficient estimates are obtained. An example is given from studies of class sizes and achievement in schools, where study data are available at the aggregate level in terms of overall mean values for classes of different sizes, and also at the student level.     

An integrated functional representation of concurrent engineering

A time-based competitive strategy is a driving force for enterprises that are striving to be competitive on a global basis. One of the methods to help in the time compression of the product development life cycle is concurrent engineering. Concurrent engineering is not only an approach that is used by the engineering function of the organization, but it is also one that crosses organizational boundaries. In this article an integrated functional model of the concurrent engineering process and its relationship with the product development life cycle is presented. A functional comparison to the traditional engineering approach for product development is also provided.