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We propose an approach for fitting linear regression models that splits the set of covariates into groups. The optimal split of the variables into groups and the regularized estimation of the regression coefficients are performed by minimizing an objective function that encourages sparsity within each group and diversity among them. The estimated coefficients are then pooled together to form the final fit. Our procedure works on top of a given penalized linear regression estimator (e.g., Lasso, elastic net) by fitting it to possibly overlapping groups of features, encouraging diversity among these groups to reduce the correlation of the corresponding predictions. For the case of two groups, elastic net penalty and orthogonal predictors, we give a closed form solution for the regression coefficients in each group. We establish the consistency of our method with the number of predictors possibly increasing with the sample size. An extensive simulation study and real-data applications show that in general the proposed method improves the prediction accuracy of the base estimator used in the procedure. Possible extensions to GLMs and other models are discussed. The supplemental material for this article, available online, contains the proofs of our theoretical results and the full results of our simulation study.

This report describes the complete mouth rehabilitation of worn dentition by using a digital workflow on a non-hinge simulated patient. A dentiform was used to simulate a patient with loss of occlusal vertical dimension. Physical diagnostic waxing along with a digital workflow was then used to re-establish the occlusal vertical dimension at the interim and definitive restoration stages.

Purpose The purpose of this paper is to apply the aspects of decision theory (DT) to performance measurement and management (PMM), thereby enabling the theoretical elaboration of volatility, uncertainty, complexity and ambiguity in the business environment, which are identified as barriers to effective PMM. Design/methodology/approach A review of decision theory and PMM literature establishes the Cynefin framework as the basis for extending the performance alignment matrix. Case research with seven companies explores the relationship between two concepts under-examined in the performance alignment matrix – internal dominant logic (DL) as the attribute of organisational culture affecting decision making, and the external environment – in line with the concept of alignment or fit in PMM. A focus area is PMM related to sustainable operations and sustainable supply chain management. Findings Alignment between DL, external environment and PMM is found, as are instances of misalignment. The Cynefin framework offers a deeper theoretical explanation about the nature of this alignment. Other findings consider the nature of organisational ownership on DL. Research limitations/implications The cases are exploratory not exhaustive, and limited in number. Organisations showing contested logic were excluded. Practical implications Some organisations have cultures of predictability and control; others have cultures that recognise their external environment as fundamentally unpredictable, and hence there is a need for responsive, decentralised PMM. Some have sought to change their culture and PMM. Being attentive to how cultural logic affects decision making can help reduce the misalignment in PMM. Originality/value A novel contribution is made by applying decision theory to PMM, extending the theoretical depth of the subject.

This article conducts a literature review that finds three dominant topics; supplier selection, supplier development and supplier evaluation for sustainability-related supplier risk management (SSRM). It also brings together rationalist decision models with behavioral models. The main contributions and discoveries of our review are: (a) we propose a four stage typology combining a number of decision making frameworksapplied for the first time to understanding SSRM; (b) we found that multi-criteria decision models (MCDM) as the most prevalent, but with an increasing use of fuzzy heuristics; (c) a significant quantity of articles had an approach on sustainability risk centered on ethical business conduct issues, and corporate social responsibility (CSR) is employed can be considered an emerging topic. We therefore propose a future research agenda targeted at enriching a prescriptive decision analysis where both ramifications - rationalist and behavioral - are blended to encapsulate both psychological and political sources of behavioral bias and distortion, in order to better define the complex contexts of and ambiguities in problem formulation in rationalist models.