The location model for mixtures of categorical and continuous variables

Recent research into graphical association models has focussed interest on the conditional Gaussian distribution for analyzing mixtures of categorical and continuous variables. A special case of such models, utilizing the homogeneous conditional Gaussian distribution, has in fact been known since 1961 as the location model, and for the past 30 years has provided a basis for the multivariate analysis of mixed categorical and continuous variables. Extensive development of this model took place throughout the 1970’s and 1980’s in the context of discrimination and classification, and comprehensive methodology is now available for such analysis of mixed variables. This paper surveys these developments and summarizes current capabilities in the area. Topics include distances between groups, discriminant analysis, error rates and their estimation, model and feature selection, and the handling of missing data.     

Robust Double Clustering: A Method Based on Alternating Concentration Steps

We propose two algorithms for robust two-mode partitioning of a data matrix in the presence of outliers. First we extend the robust k-means procedure to the case of biclustering, then we slightly relax the definition of outlier and propose a more flexible and parsimonious strategy, which anyway is inherently less robust. We discuss the breakdown properties of the algorithms, and illustrate the methods with simulations and three real examples. The author is grateful to four referees for detailed suggestions that led to an improved paper, and to Professor Vichi for support and careful reading of a first draft. Acknowledgements go also to Francesca Martella for advice.     

Clustering criteria for discrete data and latent class models

We show that a well-known clustering criterion for discrete data, the information criterion, is closely related to the classification maximum likelihood criterion for the latent class model. This relation can be derived from the Bryant-Windham construction. Emphasis is placed on binary clustering criteria which are analyzed under the maximum likelihood approach for different multivariate Bernoulli mixtures. This alternative form of criterion reveals non-apparent aspects of clustering techniques. All the criteria discussed can be optimized with the alternating optimization algorithm. Some illustrative applications are included.

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Résumé Nous montrons que le critère de classification de l'information, souvent utilisé pour les données discrètes, est très lié au critère du maximum de vraisemblance classifiante appliqué au modèle des classes latentes. Ce lien peut être analysé sous l'approche de la paramétrisation de Bryant-Windham. L'accent est mis sur le cas des données binaires qui sont analysées sous l'approche du maximum de vraisemblance pour les mélanges de distributions multivariées de Bernoulli. Cette forme de critère permet de mettre en évidence des aspects cachés des méthodes de classification de données binaires. Tous les critères envisagés ici peuvent être optimisés avec l'algorithme d'optimisation alternée. Des exemples concluent cet article.

     

Pyramidal classification based on incomplete dissimilarity data

Two algorithms for pyramidal classification — a generalization of hierarchical classification — are presented that can work with incomplete dissimilarity data. These approaches — a modification of the pyramidal ascending classification algorithm and a least squares based penalty method — are described and compared using two different types of complete dissimilarity data in which randomly chosen dissimilarities are assumed missing and the non-missing ones are subjected to random error. We also consider relationships between hierarchical classification and pyramidal classification solutions when both are based on incomplete dissimilarity data.     

On the null distribution of distance between two groups,using mixed continuous and categorical variables

The location model is a useful tool in parametric analysis of mixed continuous and categorical variables. In this model, the continuous variables are assumed to follow different multivariate normal distributions for each possible combination of categorical variable values. Using this model, a distance between two populations involving mixed variables can be defined. To date, however, no distributional results have been available, against which to assess the outcomes of practical applications of this distance. The null distribution of estimated distance is therefore considered in this paper, for a range of possible situations. No explicit analytical expressions are derived for this distribution, but easily implementable Monte Carlo schemes are described. These are then applied to previously cited examples.     

基于B/S高校科技成果转化信息系统分析设计

随着网络经济、市场经济的迅猛发展,传统的高校科技成果转化中介服务手段与先进的网络信息技术已形成了巨大反差,建设高校科技成果转化的信息化中介服务体系已具备条件.本文在分析国内网络业的现状、特征和发展趋势的基础上,提出一种基于网络高校科技成果转化系统的新思想,并进行了系统分析与设计.