記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：TAYLOR & FRANCIS INC  

It is widely believed that unlabeled data are promising for improving prediction accuracy in classification problems. Although theoretical studies about when/how unlabeled data are beneficial exist, an actual prediction improvement has not been sufficiently investigated for a finite sample in a systematic manner. We investigate the impact of unlabeled data in linear discriminant analysis and compare the error rates of the classifiers estimated with/without unlabeled data. Our focus is a labeling mechanism that characterizes the probabilistic structure of occurrence of labeled cases. Results imply that an extremely small proportion of unlabeled data has a large effect on the analysis results.

DOI： 10.1080/03610918.2014.957847

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記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：Institute of Electrical and Electronics Engineers Inc.  

Prior research identifies that «visit» and «stay» relate positively to individual's purchase outcome. However, the mechanism that how do these two factors affect sales outcomes at an aggregated level is still unclear. This study posits that shop area visit ratio leads to sales outcomes through the effects on stay time, and the influence paths of shop area visit ratio and stay time on sales outcomes differ across products. To further clarify the mechanism, we propose a mediation model, to study how shop area visit ratio and stay time affect purchasers ratio and purchase volume. The data in our study are used from ratio frequency identification (RFID) technology, and matched with point-of-sale (POS) data. The major result suggests that for «selected products», the two sales outcomes (i.e., purchasers ratio and purchase volume) are mainly determined by stay time, while for «planned products», which depends more on shop area visit ratio than stay time. This finding can help retailers to visualize the process of customers' in-store shopping, and to better understand the importance of shop area visit ratio and stay time.

DOI： 10.1109/APWCCSE.2015.7476231

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：ELSEVIER SCIENCE BV  

Semi-supervised learning, i.e., the estimation of parameters based on both labeled and unlabeled data, is widely believed to be effective in constructing a boundary in classification problems. The present paper investigates whether this belief is true in the case of normal discrimination in terms of the classification error for normal and nonnormal data. For this investigation, we use the framework of missing-data analysis because data consisting of labeled and unlabeled individuals can be regarded as missing data. Based on this framework, we introduce two labeling mechanisms: feature-independent labeling and feature-dependent labeling. For each of these labeling mechanisms, we analytically derive the asymptotic relative efficiency based on the labeled data alone and based on both the labeled and unlabeled data. Numerical computations reveal that (i) under the feature-independent labeling mechanism, unlabeled data tend to contribute to the improvement of the classification error even for nonnormal data and (ii) under the feature-dependent labeling mechanism, unlabeled data from both normal and nonnormal distributions are helpful when the labeled data are informative, but unlabeled data can augment the classification error when the labeled data are not informative. Finally, we describe some future areas of research. (C) 2013 Elsevier B.V. All rights reserved.

DOI： 10.1016/j.jspi.2013.11.004

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：SPRINGER  

Due to technological developments, data about how many items a customer buys and how long the customer spends in a supermarket are available. A major problem with the data, however, is that there is no framework that considers the heterogeneity hidden in the data. In this article, we propose a framework that considers heterogeneity in the number of items a customer buys. The first step of our framework is based on the Poisson mixture regression model using a stationary time in the department where the items are sold as its independent variable. This model finds latent homogeneous groups of customers and gives the regression models within each group. It simultaneously classifies the customers into the homogeneous groups. In the second step of our framework, a method to investigate whether another factor (variable) influences the classification into homogeneous groups is presented. This proposed framework is applied to real data collected from the customers, and the effectiveness of the framework is shown. The managerial implications are drawn from the result of the analysis.

DOI： 10.1007/s10844-012-0223-6

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記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：IEEE  

In research of purchase behavior in a supermarket, it is important to understand dependencies among sections of the supermarket, with each section corresponding to a category of items. An undirected graphical model is a powerful tool for this purpose. A problem with the application of an undirected graphical model is that there are many variables and, thus, a lot of computation is needed. In this article, we first apply a tree-structured undirected graphical model to reduce the computational amount, and second, propose a method to impose a restriction, based on our needs, on the tree-structured undirected graphical model. The variables we use are the length of time spent in each section and the number of items bought from each section. We found that some of the sections have influence on the adjacent sections and that some of the other sections have no influence on the adjacent sections, but do have influence on the nonadjacent sections. We also found that the number of items and the length of stationary time in the sections that influence a large number of sections are negatively related to those same variables in the other sections. Based on this result, managerial implications are described. Finally, we summarize this article and discuss some problems in the application of graphical models.

DOI： 10.1109/ICDMW.2012.105

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：ELSEVIER INC  

It is natural to assume that a missing-data mechanism depends on latent variables in the analysis of incomplete data in latent variate modeling because latent variables are error-free and represent key notions investigated by applied researchers. Unfortunately, the missing-data mechanism is then not missing at random (NMAR). In this article, a new estimation method is proposed, which leads to consistent and asymptotically normal estimators for all parameters in a linear latent variate model, where the missing mechanism depends on the latent variables and no concrete functional form for the missing-data mechanism is used in estimation. The method to be proposed is a type of multi-sample analysis with or without mean structures, and hence, it is easy to implement. Complete-case analysis is shown to produce consistent estimators for some important parameters in the model. (C) 2011 Elsevier Inc. All rights reserved.

DOI： 10.1016/j.jmva.2011.04.007

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記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

Customer orientation is one of the important yet underresearched topics in the retailing management. In this paper we replicate and extend research by Groeppel-Klein and Bartmann (2008), analyzing the new type of data, namely RFID (Radio Frequency Identification) data, with the purpose to examine grocery shoppers' moving direction within the store and its influence on their buying behavior. As a result, we found out that attributes of shopping process, such as purchases and walking distance, vary significantly, depending on shoppers' clockwise or anti-clockwise moving direction. As retailers would benefit from knowledge about how the moving direction of customers relate to the buying behavior, managerial implications are proposed. © 2011 Springer-Verlag.

DOI： 10.1007/978-3-642-23854-3_32

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記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：IEEE  

Due to developments in technology, movement data tracking a customer's movements in a supermarket in addition to conventional POS data are now available. A problem in analyzing such data is that an ordinary statistical model assuming customer homogeneity does not fit well to such data. In this article, we propose a framework for analyzing such data in a collection of supermarket departments. The framework is based on the mixture regression model assuming the customers' heterogeneity. By the model, we find the latent homogenous groups of the customers and explain the number of items by a stationary time based on the regression model in each latent group. The method of the mixture regression model is explained in addition to the estimation method. We found that a small number of the customers buy more items by going to the supermarket departments and are more sensitive to the stationary time, while a large number of the customers buy less and are less sensitive.

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記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：SPRINGER-VERLAG BERLIN  

Radio Frequency Identification (RFID) technology uses radio waves to track an object to which a small tag is attached. In a Japanese supermarket, we attach the RFID device to the cart and collect data on purchase behavior. In this article, we clarify the relation between purchase probability and the time customers spend in the store section by analyzing the RFID data with main use of descriptive methods. We clarify the way how the stay-time explains the purchase probability and characteristics of each store section. Based on the result, some implications for business are made as well.

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：TAYLOR & FRANCIS INC  

A simple computational method for estimation of parameters via a type of EM algorithm is proposed in restricted latent class analysis, where equality and constant constraints are considered. These constraints create difficulty in estimation. In order to simply and stably estimate parameters in restricted latent class analysis, a simple computational method using only first-order differentials is proposed, where the step-halving method is adopted. A simulation study shows that in almost all cases the new method gives parameter sequences monotonously increasing the Q-function in the EM algorithm. Analysis of real data is provided.

DOI： 10.1080/03610910802604179

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1016/j.csda.2008.04.029

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