New Statistical Matching Method Using Multinomial Logistic Regression Model
Statistical matching techniques aim to build a dataset by combining different data sources. In recent years, matching techniques have been employed in various fields. However, because of some difficulties, there are only a few applications to company data
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Tadashi Imaizumi · Akinori Okada Sadaaki Miyamoto · Fumitake Sakaori Yoshiro Yamamoto · Maurizio Vichi Editors
Advanced Studies in Classification and Data Science
Studies in Classification, Data Analysis, and Knowledge Organization
Managing Editors
Editorial Board Members
Wolfgang Gaul, Karlsruhe, Germany Maurizio Vichi, Rome, Italy Claus Weihs, Dortmund, Germany
Daniel Baier, Bayreuth, Germany Frank Critchley, Milton Keynes, UK Reinhold Decker, Bielefeld, Germany Edwin Diday, Paris, France Michael Greenacre, Barcelona, Spain Carlo Natale Lauro, Naples, Italy Jacqueline Meulman, Leiden, The Netherlands Paola Monari, Bologna, Italy Shizuhiko Nishisato, Toronto, Canada Noboru Ohsumi, Tokyo, Japan Otto Opitz, Augsburg, Germany Gunter Ritter, Passau, Germany Martin Schader, Mannheim, Germany
More information about this series at http://www.springer.com/series/1564
Tadashi Imaizumi • Akinori Okada • Sadaaki Miyamoto • Fumitake Sakaori • Yoshiro Yamamoto • Maurizio Vichi Editors
Advanced Studies in Classification and Data Science
Editors Tadashi Imaizumi School of Management and Information Sciences Tama University Tokyo, Japan
Akinori Okada Rikkyo University Tokyo, Japan
Sadaaki Miyamoto University of Tsukuba Tsukuba, Japan
Fumitake Sakaori Department of Mathematics Chuo University Tokyo, Japan
Yoshiro Yamamoto Department of Mathematics Tokai University Hiratsuka-shi, Japan
Maurizio Vichi Department of Statistical Sciences Sapienza University of Rome Roma, Italy
ISSN 1431-8814 ISSN 2198-3321 (electronic) Studies in Classification, Data Analysis, and Knowledge Organization ISBN 978-981-15-3310-5 ISBN 978-981-15-3311-2 (eBook) https://doi.org/10.1007/978-981-15-3311-2 © Springer Nature Singapore Pte Ltd. 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered
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