Analysis of Robust Parameter Designs

The analysis of robust parameter design is discussed via a model incorporating mean-variance relationship which, when ignored as in the classical regression approach, can be problematic. The model is also capable of alleviating the difficulties of the regression approach in the search of the minimum variance occurring region

Factor Analysis and Methods of Supplier Selection

We discuss in this paper the decision making in choosing the best alternative from some available options based on possibly a large number of selection criteria. This multi-criteria decision problem typically arises in supplier selection in supply chain management. Recently, there has been an increasing interest in the applications of dimensional reduction methods such as factor analysis to such decision processes. They have been widely applied in conjunction with some classical methods such as AHP to create a hierarchical structure and identify the underlying factors or constructs. There are, however, a number of inherent issues and difficulties which have not been adequately addressed in the literature. For instance, there may be some criteria which load significantly on more than one factor, creating considerable difficulties in categorizing the criteria into mutually exclusive groups. More importantly, it is seen in this paper that it is not always sensible to determine the importance of an identified factor according to its amount of shared common variance or explained variation. Similarly, attempts to routinely determine the local relative weight (within a factor) of importance of a criterion based on its factor loading or correlation with the factor may also lead to results markedly different from those based on the views or judgement of the practitioner or expert. To circumvent these difficulties, a simple, practical and easily implemented procedure is proposed. Although factor analysis is employed, it merely serves as a means of facilitating the direct rating of importance of each criterion, alleviating many of the difficulties of the classical factor analysis approach. Two examples are given to illustrate the proposed method and illustrate some potential problems of current approaches in the literature

Inverse Gaussian model for small area estimation via Gibbs sampling

We present a Bayesian method for estimating small area parameters under an inverse Gaussian model. The method is extended to estimate small area parameters for finite populations. The Gibbs sampler is proposed as a mechanism for implementing the Bayesian paradigm. We illustrate the method by application to household income survey data, comparing it against the usual lognormal model for positively skewed data. Key words/phrases: Finite population sampling, hierarchical Bayesian inference, lognormal model, MCMC integration, shrinkage estimates SINET: Ethiopian Journal of Science Vol. 28 (1) 2005: 1–1

Estimation of Process Variances in Robust Parameter Designs

The modeling of variation through interactions is appealing in crossed array design as it leads to greater robustness to certain type of model misspecification. As an alternative to signal-to-noise analysis, a new, systematic method based on Taguchi type crossed array design is given. It is shown in this article that when fractional factorial design is used for the outer array, the crossed array design is not robust to the presence of noise-noise interactions and a method of rectifying the problem is suggested