Archetypoids: A new approach to define representative archetypal data
[EN] The new concept archetypoids is introduced. Archetypoid analysis represents each observation in a dataset as a mixture of actual observations in the dataset, which are pure type or archetypoids. Unlike archetype analysis, archetypoids are real observations, not a mixture of observations. This is relevant when existing archetypal observations are needed, rather than fictitious ones. An algorithm is proposed to find them and some of their theoretical properties are introduced. It is also shown how they can be obtained when only dissimilarities between observations are known (features are unavailable). Archetypoid analysis is illustrated in two design problems and several examples, compar…
Archetypal analysis: contributions for estimating boundary cases in multivariate accommodation problem
[EN] The use of archetypal analysis is proposed in order to determine a set of representative cases that entail a certain percentage of the population, in the accommodation problem. A well-known anthropometric database has been used in order to compare our methodology with the common used PCA-approach, showing the advantages of our methodology: the level of accommodation is reached unlike the PCA approach, no more adjustments are necessary, the user can decide the number of archetypes to consider or leave the selection by a criterion. Unlike PCA, the objective of the archetypal analysis is obtaining extreme individuals, so it is the appropriate statistical technique for solving this type of…
Anthropometry: An R Package for Analysis of Anthropometric Data
The development of powerful new 3D scanning techniques has enabled the generation of large up-to-date anthropometric databases which provide highly valued data to improve the ergonomic design of products adapted to the user population. As a consequence, Ergonomics and Anthropometry are two increasingly quantitative fields, so advanced statistical methodologies and modern software tools are required to get the maximum benefit from anthropometric data. This paper presents a new R package, called Anthropometry, which is available on the Comprehensive R Archive Network. It brings together some statistical methodologies concerning clustering, statistical shape analysis, statistical archetypal an…
Looking for representative fit models for apparel sizing
This paper is concerned with the generation of optimal fit models for use in apparel design. Representative fit models or prototypes are important for defining a meaningful sizing system. However, there is no agreement among apparel manufacturers and each one has their own prototypes and size charts i.e. there is a lack of standard sizes in garments from different apparel manufacturers. We propose two algorithms based on a new hierarchical partitioning around medoids clustering method originally developed for gene expression data. We are concerned with a different application; therefore, the dissimilarity between the objects has to be different and must be designed to deal with anthropometr…
Apparel sizing using trimmed PAM and OWA operators
This paper is concerned with apparel sizing system design. One of the most important issues in the apparel development process is to define a sizing system that provides a good fit to the majority of the population. A sizing system classifies a specific population into homogeneous subgroups based on some key body dimensions. Standard sizing systems range linearly from very small to very large. However, anthropometric measures do not grow linearly with size, so they can not accommodate all body types. It is important to determine each class in the sizing system based on a real prototype that is as representative as possible of each class. In this paper we propose a methodology to develop an …
Forecasting basketball players' performance using sparse functional data*
Statistics and analytic methods are becoming increasingly important in basketball. In particular, predicting players’ performance using past observations is a considerable challenge. The purpose of this study is to forecast the future behavior of basketball players. The available data are sparse functional data, which are very common in sports. So far, however, no forecasting method designed for sparse functional data has been used in sports. A methodology based on two methods to handle sparse and irregular data, together with the analogous method and functional archetypoid analysis is proposed. Results in comparison with traditional methods show that our approach is competitive and additio…
A Web Application for Interactive Visualization of European Basketball Data
The statistical analysis of basketball games is a fast-growing field. Certainly, basketball data are scientifically relevant because an appropriate analysis provides a great deal of information about the performance of both players and teams. The number of games played each season generates a large amount of data worth analyzing. Basketball analytics is well established in U.S. leagues. In Europe, however, it has not been duly developed. This study focuses on the top three European team competitions: the EuroLeague, the EuroCup, and the Spanish ACB (Association of Basketball Clubs, acronym in Spanish) league. Their official websites provide access to game data for anyone who is interested, …