We consider an extended notion of parameter orthogonality for estimating functions, called nuisance parameter insensitivity, which allows a unified treatment of nuisance parameters for a wide range of methods, including Liang and Zeger's generalized estimating equations. Nuisance parameter...

We introduce a new class of generalized linear mixed models based on the Tweedie exponential dispersion model distributions, accommodating a wide range of discrete, continuous and mixed data. Using the best linear unbiased predictor of random effects, we obtain an optimal estimating function for...

We introduce a class of multivariate dispersion models suitable as error distributions for generalized linear models with multivariate non-normal responses. The models preserve some of the main properties of the multivariate normal distribution, and include the elliptically contoured...

Estimation of Taylor's power law for species abundance data may be performed by linear regression of the log empirical variances on the log means, but this method suffers from a problem of bias for sparse data. We show that the bias may be reduced by using a bias-corrected Pearson estimating...

In their struggle for operational excellence, global companies face challenges in finding a common denominator to streamline and align supply chain operations across business units. The ever growing complexity of managing global flows of products, information and capital increases the risk of...

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This paper proposes a research program on Business Process Innovation: Towards Global Supply Chain Intelligence. Few words are more ubiquitous in business or society today than "innovation". This reflects that businesses are striving for ways to survive and thrive in an increasingly complex and...

Most organizations today are required not only to establish effective business processes but they are required to accommodate for changing business conditions at an increasing rate. Many business processes extend beyond the boundary of the enterprise into the supply chain and the information...