A Hybrid Model for Preference Data

Preference scores to n objects of N individuals is a popular data collected in Marketing, Behavior Science, etc. A vector model or an unfolding distance model have been used to analyze these type of data matrix. However, it is difficult to understand what attributes contribute on preference evaluation using these continuous mapping models as the decomposition of data is not unique. The overlapping cluster models and methods such as ADCLUS (Shepard and Arabie, 1979) have interesting features to find the attributes in similarity data. So we propose a modified model of overlapping model, a hybrid model, to discover the hidden attributes of objects by putting a decomposition constraints. And we also show an application to real data set

Mixed Tree and Spatial Representation of Dissimilarity Judgments

Whereas previous research has shown that either tree or spatial representations of dissimilarity judgments may be appropriate, focussing on the comparative fit at the aggregate level, we investigate whether there is heterogeneity among subjects in the extent to which their dissimilarity judgments are better represented by ultrametric tree or spatial multidimensional scaling models. We develop a mixture model for the analysis of dissimilarity data, that is formulated in a stochastic context, and entails a representation and a measurement model component. The latter involves distributional assumptions on the measurement error, and enables estimation by maximum likelihood. The representation component allows dissimilarity judgments to be represented either by a tree structure or by a spatial configuration, or a mixture of both. In order to investigate the appropriateness of tree versus spatial representations, the model is applied to twenty empirical data sets. We compare the fit of our model with that of aggregate tree and spatial models, as well as with mixtures of pure trees and mixtures of pure spaces, respectively. We formulate some empirical generalizations on the relative importance of tree versus spatial structures in representing dissimilarity judgments at the individual level.Multidimensional scaling;tree models;mixture models;dissimilarity judgments

Three-way methods for multiattribute genotype by environment data: An illustrated partial survey

FSW - Gezinsopvoeding - Ou

Detection and classification of gastrointestinal cancer and other pathologies through quantitative analysis of optical coherence tomography data and goniophotometry

The changes in light interaction between healthy and diseased tissues have been investigated as a potential diagnostic application. Here we attempt to differentiate between healthy and pathological gastrointestinal tissues using quantitative analysis of optical coherence tomography (OCT) data and goniophotometry. A goniophotometer was constructed and calibrated using titanium oxide and microsphere phantoms. Measurements were carried out on human gastrointestinal tissue sections collected using the methodology described below. The anisotropy factor g was extracted from the scattering curves by fitting the Henyey-Greenstein function. Measurements on human samples were in the forward scattering range with g 0.6-0.7, in agreement with the literature. Optical coherence tomography imaging was carried out on gastrointestinal tissues collected from patients undergoing elective surgery or endoscopy at St. Mary’s Hospital, London. In total 146 patients were included. Data was processed using gradient analysis of signal attenuation and morphological analysis with kNN classification. Results were correlated with histological diagnoses. Gradient analysis results were statistically significant across most categories, showing particularly good differences in the gradient distributions between healthy and diseased oesophageal tissues. Morphological analysis and kNN classification produced sensitivity and specificity values for healthy oesophagus and cancer in surgical specimens reaching 100% / 97.87% and 99.99% / 99.91% respectively and high accuracy in detecting Barrett's oesophagus in endoscopic specimens, with sensitivity and specificity values of 99.80% and 99.02%. Results in rectal tissue where also noteworthy, with detection of dysplasia reaching a sensitivity and specificity of 99.55% / 96.01%. Despite limitations in our work, we have shown that the detection of gastrointestinal pathologies using quantitative analysis of OCT data is a promising technique with good ex vivo results. Transferring the methodology to the in vivo domain holds a lot of potential as a future quick and reliable diagnostic technique.Open Acces

Inferring Market Structure from Customer Response to Competing and Complementary Products

We consider customer influences on market structure, arguing that market structure should explain the extent to which any given set of market offerings are substitutes or complements. We describe recent additions to the market structure analysis literature and identify promising directions for new research in market structure analysis. Impressive advances in data collection, statistical methodology and information technology provide unique opportunities for researchers to build market structure tools that can assist “real-time” marketing decision-making.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46981/1/11002_2004_Article_5088105.pd

Biclustering models for structured microarray data

©2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.Microarrays have become a standard tool for investigating gene function and more complex microarray experiments are increasingly being conducted. For example, an experiment may involve samples from several groups or may investigate changes in gene expression over time for several subjects, leading to large three-way data sets. In response to this increase in data complexity, we propose some extensions to the plaid model, a biclustering method developed for the analysis of gene expression data. This model-based method lends itself to the incorporation of any additional structure such as external grouping or repeated measures. We describe how the extended models may be fitted and illustrate their use on real data

Three-mode analytical methods for crop improvement programs

Datatheorie voor analyse van individuele verschille

Feature network models for proximity data : statistical inference, model selection, network representations and links with related models

Feature Network Models (FNM) are graphical structures that represent proximity data in a discrete space with the use of features. A statistical inference theory is introduced, based on the additivity properties of networks and the linear regression framework. Considering features as predictor variables leads in a natural way to a univariate multiple regression problem with positivity restrictions on the parameters, which represent edge lengths in the network representation. Theoretical standard errors and confidence intervals are obtained for the parameters and their performance is evaluated by Monte Carlo simulation. When the feature structure is not known in advance, a strategy is proposed to select an adequate subset of features that takes into account a good compromise between model fit and model complexity using Gray codes and the positive lasso. The same statistical inference theory also holds for additive trees that are special cases of FNM. Standard errors and confidence intervals, model tests and prediction error are obtained for the estimates of the branch lengths of additive trees. The dissertation concludes by demonstrating that there exists a universal network representation of city-block models based on key elements of the network representation consisting of betweenness, metric segmental additivity and internal nodes.LEI Universiteit LeidenMultivariate analysis of psychological data - ou

Clusterwise regression and market segmentation : developments and applications

The present work consists of two major parts. In the first part the literature on market segmentation is reviewed, in the second part a set of new methods for market segmentation are developed and applied.Part 1 starts with a discussion of the segmentation concept, and proceeds with a discussion on marketing strategies for segmented markets. A number of criteria for effective segmentation are summarized. Next, two major streams of segmentation research are identified on the basis of their theoretical foundation, which is either of a microeconomic or of a behavioral science nature. These two streams differ according to both the bases and the methods used for segmenting markets.After a discussion of the segmentation bases that have been put forward as the normative ideal but have been applied in practice very little, different bases are classified into four categories, according to their being observable or unobservable, and general or product- specific. The bases in each of the four categories are reviewed and discussed in terms of the criteria for effective segmentation. Product benefits are identified as one of the most effective bases by these criteria.Subsequently, the statistical methods available for segmentation are discussed, according to a classification into four categories, being either a priori or post hoc, and either descriptive or predictive. Post hoc (clustering) methods are appealing because they deal adequately with the complexity of markets, while the predictive methods within this class (AID, clusterwise regression) combine this advantage with prediction of purchase (predisposition).Within the two major segmentation streams, segmentation methods have been developed that are specifically tailored to the segmentation problems at hand. These are discussed. For the microeconomic school focus is upon recently developed latent class approaches that simultaneously estimate consumer segments and market characteristics (market shares, switching, elasticities) within these segments. For the behavioral science school focus is on benefit segmentation. Disadvantages of the traditional two-stage approach, in which consumers are clustered into segments on the basis of benefit importances estimated at the individual level, are revealed and procedures that have been addressed to one or more of these problems are reviewed.In Part 2, three new methods for benefit segmentation are developed: clusterwise regression, fuzzy clusterwise regression and generalized fuzzy clusterwise regression.The first method is a clustering method that simultaneously groups consumers in a number of nonoverlapping segments, and estimates the benefit importances within segments. The performance of the algorithm on synthetic data is investigated in a Monte Carlo study. Empirically, the method is shown to outperform the two-stage procedure. Special attention is paid to significance testing with Monte Carlo test procedures, and convergence to local optima. An application to segmentation of the meat-market in the Netherlands on the basis of data on elderly peoples preferences for meat products is given. Three segments are identified. The first segment weights sensory quality against exclusiveness (price), in the second segment quality is traded off against fatness. This segment, comprising predominantly of females, had the best knowledge of nutrition. In the third segment preference is based on quality only. Regional differences were identified among segments.Fuzzy clusterwise regression extends clusterwise regression in that it allows consumers to be a member of more than one segment. It simultaneously estimates the preference functions within segments, as well as the degree of membership of consumers in those segments. Using synthetic data, the performance of the method is evaluated. Empirical comparisons with two other methods are provided, and the cross-validity of the method with respect to classification and prediction is assessed. Attention is given in particular to the selection of the appropriate number of segments, the setting of the user defined fuzzy weight parameter, and Monte Carlo significance test procedures. An application to data on preferences for meatproducts used on bread in the Netherlands revealed three segments. In the first segment, taste and fitness for common use are important. In the second segment, taste overridingly determines preference, but products that are considered more exclusive and natural and less fat and salt are also preferred. In segment three the health related product benefits are even more important. The importance of taste decreases from segment one to three, while the importance of health-related aspects increases in that direction. The health oriented segments comprised more females, older people and people who attributed causality of their behavior more to themselves.The method was also applied to data on consumers image for stores that sell meat. Again three segments were revealed. The value shoppers, trade off quality and price.They come from smaller families and spend less on meat. In the largest segment store image is based upon product quality. Females have higher membership in this segment, that is more involved with the store where they buy meat. For service shoppers, both service and atmosphere are important. This segment tends to be more store-loyal.Next, a generalization of fuzzy clusterwise regression is proposed, which incorporates both benefit segmentation and market structuring within the framework of preference analysis. The method simultaneously estimates the preference functions within each of a number of clusters, and the parameters indicating the degree of membership of both subjects and products in these clusters. The performance of this method is assessed in a Monte Carlo study on synthetic data. The method is compared empirically with clusterwise regression and fuzzy clusterwise regression. The significance testing with Monte Carlo test procedures, and the selection of the fuzzy weight parameters is treated in detail. Two segments were revealed in an analysis of consumer preferences of butter and margarine brands. The segments differed mainly in the importance attached to exclusiveness and fitness for multiple purposes. The brands competing within these segments were revealed. Females and consumers with a higher socioeconomic status had higher memberships in the segments in which exclusiveness was important.Finally, the clusterwise regression methods developed in this work are compared with other recently developed procedures in terms of the assumptions involved. The substantive results obtained in the empirical studies concerning foods are summarized and their implications for future research are given. The implications and the contribution of the methods to the development of marketing strategies for segmented markets are discussed