Mahalanobis distance

About: Mahalanobis distance is a research topic. Over the lifetime, 4616 publications have been published within this topic receiving 95294 citations.

Conceptualizing and measuring distance in international business research: Recurring questions and best practice guidelines

Abstract: Distance is a central concept in international business research, yet there is debate about the construct as well as its operationalization. In this editorial, we address three of the most important recurring questions posed by authors, editors, and reviewers by examining the theory, methods, and data of distance research. We discuss (1) how to theorize on distance, and (2) what method and (3) what data to use when constructing a distance index. We develop practical recommendations grounded in theory, illustrating and supporting them by calculating cross-country distance indices for all available country pairs and two of the most used distance indices: cultural and institutional. We show that, whereas a specific method to calculate distance may matter to some extent, the choice for a specific cultural or institutional framework to measure cultural or institutional distance has a major impact on country–pair distances. Overall, this editorial highlights the importance of matching data and method to the theoretical argument.

Mahalanobis-Taguchi System

Abstract: From the Publisher: Learn how you can harness the power of an amazing new pattern-recognition and forecasting method from Dr. Genichi Taguchi, a world-renowned quality genius. 15 case studies from around the U.S. and Japan show how industry giants used the MTS effectively in their organizations. With this important and authoritative book, you can achieve the same success.

Semisupervised JITL Framework for Nonlinear Industrial Soft Sensing Based on Locally Semisupervised Weighted PCR

Abstract: Just-in-time learning (JITL) is a commonly used technique for industrial soft sensing of nonlinear processes. However, traditional JITL approaches mainly focus on equal sample sizes between process (input) variables and quality (output) variables, which may not be practical in industrial processes since quality variables are usually much harder to obtain than other process variables. In order to handle unequal length dataset with only a few labeled data, a novel semisupervised JITL framework is proposed for soft sensor modeling for nonlinear processes, which is based on semisupervised weighted probabilistic principal component regression (SWPPCR). In the new semisupervised JITL framework, traditional Mahalanobis distance and a new proposed scaled Mahalanobis distance are used for similarity measurement and weight assignment. By selecting the most relevant labeled and unlabeled samples and assigning them with the corresponding weights, a local SWPPCR can be built to estimate the output variables of the query sample. Case studies are carried out to evaluate the prediction performance of the proposed semisupervised JITL framework on a numerical example and an industrial process. The effectiveness and flexibility of the proposed method are demonstrated by the prediction results.