Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Multivariate Customer Demand PDF full book. Access full book title Multivariate Customer Demand by Catalina Stefanescu. Download full books in PDF and EPUB format.
Author: Catalina Stefanescu Publisher: ISBN: Category : Languages : en Pages : 33
Book Description
Demand modeling and forecasting is important for inventory management, retail assortment and revenue management applications. Current practice focuses on univariate demand forecasting, where models are built separately for each product. However, in many industries there is empirical evidence of correlated product demand. In addition, demand is usually observed in several periods during a selling horizon, and it may be truncated due to inventory constraints so that in practice only censored sales data are recorded. Ignoring the inter-product demand correlation or the serial correlation of demand from one selling period to the next leads to biased and inefficient estimates of the true demand distributions. In this paper we propose a class of models for multi-product multiperiod aggregate demand forecasting. We develop an approach for estimating the parameters of the demand models from censored sales data in a maximum likelihood framework using the Expectation-Maximization (EM) algorithm. Through a simulation study, we show that the algorithm is computationally attractive and leads to maximum likelihood estimates with good properties, under different demand and censoring scenarios. We exemplify the methodology with the analysis of two booking data sets from the entertainment and the airline industries, and show that the use of these models in a revenue management setting for airlines increases the revenue by up to 11% relative to the use of alternative demand forecasting methods.
Author: Catalina Stefanescu Publisher: ISBN: Category : Languages : en Pages : 33
Book Description
Demand modeling and forecasting is important for inventory management, retail assortment and revenue management applications. Current practice focuses on univariate demand forecasting, where models are built separately for each product. However, in many industries there is empirical evidence of correlated product demand. In addition, demand is usually observed in several periods during a selling horizon, and it may be truncated due to inventory constraints so that in practice only censored sales data are recorded. Ignoring the inter-product demand correlation or the serial correlation of demand from one selling period to the next leads to biased and inefficient estimates of the true demand distributions. In this paper we propose a class of models for multi-product multiperiod aggregate demand forecasting. We develop an approach for estimating the parameters of the demand models from censored sales data in a maximum likelihood framework using the Expectation-Maximization (EM) algorithm. Through a simulation study, we show that the algorithm is computationally attractive and leads to maximum likelihood estimates with good properties, under different demand and censoring scenarios. We exemplify the methodology with the analysis of two booking data sets from the entertainment and the airline industries, and show that the use of these models in a revenue management setting for airlines increases the revenue by up to 11% relative to the use of alternative demand forecasting methods.
Author: Brian Everitt Publisher: Springer Science & Business Media ISBN: 1441996508 Category : Mathematics Languages : en Pages : 284
Book Description
The majority of data sets collected by researchers in all disciplines are multivariate, meaning that several measurements, observations, or recordings are taken on each of the units in the data set. These units might be human subjects, archaeological artifacts, countries, or a vast variety of other things. In a few cases, it may be sensible to isolate each variable and study it separately, but in most instances all the variables need to be examined simultaneously in order to fully grasp the structure and key features of the data. For this purpose, one or another method of multivariate analysis might be helpful, and it is with such methods that this book is largely concerned. Multivariate analysis includes methods both for describing and exploring such data and for making formal inferences about them. The aim of all the techniques is, in general sense, to display or extract the signal in the data in the presence of noise and to find out what the data show us in the midst of their apparent chaos. An Introduction to Applied Multivariate Analysis with R explores the correct application of these methods so as to extract as much information as possible from the data at hand, particularly as some type of graphical representation, via the R software. Throughout the book, the authors give many examples of R code used to apply the multivariate techniques to multivariate data.
Author: Abdelmonem Afifi Publisher: CRC Press ISBN: 1351788906 Category : Mathematics Languages : en Pages : 528
Book Description
This is the sixth edition of a popular textbook on multivariate analysis. Well-regarded for its practical and accessible approach, with excellent examples and good guidance on computing, the book is particularly popular for teaching outside statistics, i.e. in epidemiology, social science, business, etc. The sixth edition has been updated with a new chapter on data visualization, a distinction made between exploratory and confirmatory analyses and a new section on generalized estimating equations and many new updates throughout. This new edition will enable the book to continue as one of the leading textbooks in the area, particularly for non-statisticians. Key Features: Provides a comprehensive, practical and accessible introduction to multivariate analysis. Keeps mathematical details to a minimum, so particularly geared toward a non-statistical audience. Includes lots of detailed worked examples, guidance on computing, and exercises. Updated with a new chapter on data visualization.
Author: Robert L. Mason Publisher: SIAM ISBN: 0898714966 Category : Technology & Engineering Languages : en Pages : 271
Book Description
Detailed coverage of the practical aspects of multivariate statistical process control (MVSPC) based on the application of Hotelling's T2 statistic. MVSPC is the application of multivariate statistical techniques to improve the quality and productivity of an industrial process. Provides valuable insight into the T2 statistic.
Author: Richard A. Johnson Publisher: Pearson ISBN: 9780134995397 Category : Multivariate analysis Languages : en Pages : 808
Book Description
This title is part of the Pearson Modern Classics series. Pearson Modern Classics are acclaimed titles at a value price. Please visit www.pearsonhighered.com/math-classics-series for a complete list of titles. For courses in Multivariate Statistics, Marketing Research, Intermediate Business Statistics, Statistics in Education, and graduate-level courses in Experimental Design and Statistics. Appropriate for experimental scientists in a variety of disciplines, this market-leading text offers a readable introduction to the statistical analysis of multivariate observations. Its primary goal is to impart the knowledge necessary to make proper interpretations and select appropriate techniques for analyzing multivariate data. Ideal for a junior/senior or graduate level course that explores the statistical methods for describing and analyzing multivariate data, the text assumes two or more statistics courses as a prerequisite.
Author: Francis Thomas Juster Publisher: Princeton University Press ISBN: 1400879698 Category : Business & Economics Languages : en Pages : 322
Book Description
The author is concerned with whether or not surveys of consumer anticipations can improve predictions of purchase behavior relative to predictions that use only objective variables obtainable at the same date. The basic objective of the study is improved predictions of changes over time. Originally published in 1964. The Princeton Legacy Library uses the latest print-on-demand technology to again make available previously out-of-print books from the distinguished backlist of Princeton University Press. These editions preserve the original texts of these important books while presenting them in durable paperback and hardcover editions. The goal of the Princeton Legacy Library is to vastly increase access to the rich scholarly heritage found in the thousands of books published by Princeton University Press since its founding in 1905.
Author: John Spicer Publisher: SAGE ISBN: 9781412904018 Category : Mathematics Languages : en Pages : 256
Book Description
A short introduction to the subject, this text is aimed at students & practitioners in the behavioural & social sciences. It offers a conceptual overview of the foundations of MDA & of a range of specific techniques including multiple regression, logistic regression & log-linear analysis.
Author: Klaus Backhaus Publisher: Springer Nature ISBN: 3658325895 Category : Business & Economics Languages : en Pages : 614
Book Description
Data can be extremely valuable if we are able to extract information from them. This is why multivariate data analysis is essential for business and science. This book offers an easy-to-understand introduction to the most relevant methods of multivariate data analysis. It is strictly application-oriented, requires little knowledge of mathematics and statistics, demonstrates the procedures with numerical examples and illustrates each method via a case study solved with IBM’s statistical software package SPSS. Extensions of the methods and links to other procedures are discussed and recommendations for application are given. An introductory chapter presents the basic ideas of the multivariate methods covered in the book and refreshes statistical basics which are relevant to all methods. Contents Introduction to empirical data analysis Regression analysis Analysis of variance Discriminant analysis Logistic regression Contingency analysis Factor analysis Cluster analysis Conjoint analysis The original German version is now available in its 16th edition. In 2015, this book was honored by the Federal Association of German Market and Social Researchers as “the textbook that has shaped market research and practice in German-speaking countries”. A Chinese version is available in its 3rd edition. On the website www.multivariate-methods.info, the authors further analyze the data with Excel and R and provide additional material to facilitate the understanding of the different multivariate methods. In addition, interactive flashcards are available to the reader for reviewing selected focal points. Download the Springer Nature Flashcards App and use exclusive content to test your knowledge.
Author: Björn Schmolck Publisher: Springer Science & Business Media ISBN: 3642583245 Category : Business & Economics Languages : en Pages : 149
Book Description
This book deals with the omitted variable test for a multivariate time-series regression model. The empirical motivation is the homogeneity test for a consumer demand system. The consequences of using a dynamically misspecified omitted variable test are shown in detail. The analysis starts with the univariate t-test and is then extended to the multivariate regression system. The small sample performance of the dynamically correctly specified omitted variable test is analysed by simulation. Two classes of tests are considered: versions of the likelihood ratio test and the robust Wald test which is based on a heteroskedasticity and autocorrelation consistent variance-covariance estimator (HAC).
Author: Catalina Stefanescu
Author: Catalina Stefanescu
Author: Brian Everitt
Author: Abdelmonem Afifi
Author: Robert L. Mason
Author: Richard A. Johnson
Author: Francis Thomas Juster
Author: Alexander Basilevsky
Author: John Spicer
Author: Klaus Backhaus
Author: Björn Schmolck