Download or read book Bayesian Hierarchical Semiparametric and Nonparametric Methods for International New Product Diffusion written by Brian Matthew Hartman and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Global marketing managers are keenly interested in being able to predict the sales of their new products. Understanding how a product is adopted over time allows the managers to optimally allocate their resources. With the world becoming ever more global, there are strong and complex interactions between the countries in the world. My work explores how to describe the relationship between those countries and determines the best way to leverage that information to improve the sales predictions. In Chapter II, I describe how diffusion speed has changed over time. The most recent major study on this topic, by Christophe Van den Bulte, investigated new product di ffusions in the United States. Van den Bulte notes that a similar study is needed in the international context, especially in developing countries. Additionally, his model contains the implicit assumption that the diffusion speed parameter is constant throughout the life of a product. I model the time component as a nonparametric function, allowing the speed parameter the flexibility to change over time. I find that early in the product's life, the speed parameter is higher than expected. Additionally, as the Internet has grown in popularity, the speed parameter has increased. In Chapter III, I examine whether the interactions can be described through a reference hierarchy in addition to the cross-country word-of-mouth eff ects already in the literature. I also expand the word-of-mouth e ffect by relating the magnitude of the e ffect to the distance between the two countries. The current literature only applies that e ffect equally to the n closest countries (forming a neighbor set). This also leads to an analysis of how to best measure the distance between two countries. I compare four possible distance measures: distance between the population centroids, trade ow, tourism ow, and cultural similarity. Including the reference hierarchy improves the predictions by 30 percent over the current best model. Finally, in Chapter IV, I look more closely at the Bass Diffusion Model. It is prominently used in the marketing literature and is the base of my analysis in Chapter III. All of the current formulations include the implicit assumption that all the regression parameters are equal for each country. One dollar increase in GDP should have more of an eff ect in a poor country than in a rich country. A Dirichlet process prior enables me to cluster the countries by their regression coefficients. Incorporating the distance measures can improve the predictions by 35 percent in some cases.
Download or read book Bayesian Non and Semi parametric Methods and Applications written by Peter Rossi and published by Princeton University Press. This book was released on 2014-04-27 with total page 219 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reviews and develops Bayesian non-parametric and semi-parametric methods for applications in microeconometrics and quantitative marketing. Most econometric models used in microeconomics and marketing applications involve arbitrary distributional assumptions. As more data becomes available, a natural desire to provide methods that relax these assumptions arises. Peter Rossi advocates a Bayesian approach in which specific distributional assumptions are replaced with more flexible distributions based on mixtures of normals. The Bayesian approach can use either a large but fixed number of normal components in the mixture or an infinite number bounded only by the sample size. By using flexible distributional approximations instead of fixed parametric models, the Bayesian approach can reap the advantages of an efficient method that models all of the structure in the data while retaining desirable smoothing properties. Non-Bayesian non-parametric methods often require additional ad hoc rules to avoid "overfitting," in which resulting density approximates are nonsmooth. With proper priors, the Bayesian approach largely avoids overfitting, while retaining flexibility. This book provides methods for assessing informative priors that require only simple data normalizations. The book also applies the mixture of the normals approximation method to a number of important models in microeconometrics and marketing, including the non-parametric and semi-parametric regression models, instrumental variables problems, and models of heterogeneity. In addition, the author has written a free online software package in R, "bayesm," which implements all of the non-parametric models discussed in the book.
Download or read book New Product Diffusion Models written by Vijay Mahajan and published by Springer Science & Business Media. This book was released on 2000-09-30 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: Product sales, especially for new products, are influenced by many factors. These factors are both internal and external to the selling organization, and are both controllable and uncontrollable. Due to the enormous complexity of such factors, it is not surprising that product failure rates are relatively high. Indeed, new product failure rates have variously been reported as between 40 and 90 percent. Despite this multitude of factors, marketing researchers have not been deterred from developing and designing techniques to predict or explain the levels of new product sales over time. The proliferation of the internet, the necessity or developing a road map to plan the launch and exit times of various generations of a product, and the shortening of product life cycles are challenging firms to investigate market penetration, or innovation diffusion, models. These models not only provide information on new product sales over time but also provide insight on the speed with which a new product is being accepted by various buying groups, such as those identified as innovators, early adopters, early majority, late majority, and laggards. New Product Diffusion Models aims to distill, synthesize, and integrate the best thinking that is currently available on the theory and practice of new product diffusion models. This state-of-the-art assessment includes contributions by individuals who have been at the forefront of developing and applying these models in industry. The book's twelve chapters are written by a combined total of thirty-two experts who together represent twenty-five different universities and other organizations in Australia, Europe, Hong Kong, Israel, and the United States. The book will be useful for researchers and students in marketing and technological forecasting, as well as those in other allied disciplines who study relevant aspects of innovation diffusion. Practitioners in high-tech and consumer durable industries should also gain new insights from New Product Diffusion Models. The book is divided into five parts: I. Overview; II. Strategic, Global, and Digital Environments for Diffusion Analysis; III. Diffusion Models; IV. Estimation and V. Applications and Software. The final section includes a PC-based software program developed by Gary L. Lilien and Arvind Rangaswamy (1998) to implement the Bass diffusion model. A case on high-definition television is included to illustrate the various features of the software. A free, 15-day trial access period for the updated software can be downloaded from http://www.mktgeng.com/diffusionbook. Among the book's many highlights are chapters addressing the implications posed by the internet, globalization, and production policies upon diffusion of new products and technologies in the population.
Download or read book Bayesian Nonparametric Data Analysis written by Peter Müller and published by Springer. This book was released on 2015-06-17 with total page 203 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. Rather than providing an encyclopedic review of probability models, the book’s structure follows a data analysis perspective. As such, the chapters are organized by traditional data analysis problems. In selecting specific nonparametric models, simpler and more traditional models are favored over specialized ones. The discussed methods are illustrated with a wealth of examples, including applications ranging from stylized examples to case studies from recent literature. The book also includes an extensive discussion of computational methods and details on their implementation. R code for many examples is included in online software pages.
Download or read book Factors Affecting Diffusion Patterns for New Products written by Jun Yu and published by . This book was released on 2002 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Bayesian Semi Parametric and Non Parametric Methods in Marketing and Micro Econometrics written by Peter E. Rossi and published by . This book was released on 2013 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: I review and develop Bayesian non-parametric and semi-parametric methods based on finite and infinite mixtures of normals. Applications include regression, IV methods, and random coefficient models.
Download or read book Investigating New Product Diffusion Across Products and Countries written by Debabrata Talukdar and published by . This book was released on 2014 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: As firms jockey to position themselves in emerging markets, firms need to evaluate the relative attractiveness of market expansion in different countries. Since the attractiveness of a market is a function of the eventual market potential and the speed at which the product diffuses through the market, a better understanding of the determinants of market potential and diffusion speed across different countries is of particular relevance to firms deliberating their market expansion strategies. Despite a recent spurt in research on multinational diffusion, there exist significant gaps in the literature. First, existing studies tend to limit their analysis to industrialized countries, thus reducing the ability to generalize the insights to many emerging markets. Second, these studies tend to focus on the coefficients of external and internal influence in the Bass diffusion model, but do not analyze the determinants of market potential. Third, the choice of variables that affect the parameters of the Bass diffusion model has been rather limited. In this paper, we seek to address these gaps in the literature. To address the scope issue, we assembled a novel dataset that captures the diffusion of 6 products in 31 developed and developing countries from Europe, Asia, North and South America. The set of countries in our dataset encompasses 60% of the world population and includes such emerging economies as China, India, Brazil and Thailand. This should provide us a stronger basis to make empirical generalizations about the diffusion process. For firms seeking to expand into emerging international markets, our findings about penetration potential have considerable significance. For example, we find that for the set of products that we analyze the average penetration potential for developing countries is about one-third (0.17 versus 0.52) of that for developed countries. We also find that it takes developing countries on average 17.9% (19.25 versus 16.33 years) longer to achieve peak sales. Thus, despite the well-known positive effect of product introduction delays on diffusion speed, we find that developing countries still continue to experience a slower adoption rate compared to developed countries. Our study also investigated the impact of several new macro-environmental variables on penetration potential and speed. For example, our findings indicate that a 1% change in international trade or urbanization level can potentially change the penetration potential by about 0.5% and 0.2% respectively. These are some of the key variables projected to change significantly over the coming years for developing countries. While business managers have relatively little influence on such variables, our findings can still serve as a valuable empirical guide for the variables that they should consider in evaluating diverse international markets, and for performing sensitivity analysis with respect to their projected trends. Finally, our study also holds implications for managers seeking to combine information about past diffusion patterns across products and countries for better prediction. We pool information efficiently across multiple products and countries using a Hierarchical Bayes estimation methodology. By sharing information across countries and products in a single, coherent framework, we find that this pooling approach leads to substantial improvements in prediction accuracy. Our technique is particularly superior in predicting sales and BDM parameter values in the early years of a new product introduction in a new country, when forecast estimates are managerially most useful. We also decompose the variance in the BDM model parameters into product, country and product-country components. These results give guidelines to managers about which market experience they should weigh more to arrive at forecasts of market potential and diffusion speed. We find that while past experiences of other products in a country (country effects) are relatively more useful to explain penetration level (cumulative sales), past experiences in other countries where a product was earlier introduced (product effects) are more useful to explain the coefficients of external and internal influence (and thus the speed with which the product will attain peak sales).
Download or read book Investigating New Product Diffusion Speed written by Brian M. Hartman and published by . This book was released on 2012 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Global marketing managers are interested in understanding the speed of the new product diffusion process and how the speed has changed in our ever more technologically advanced and global marketplace. Understanding the process allows firms to forecast the expected rate of return on their new products and develop effective marketing strategies. The most recent major study on this topic (Van den Bulte, 2000) investigated new product diffusions in the United States. We expand upon that study in three important ways. (1) Van den Bulte notes that a similar study is needed in the international context, especially in developing countries. Our study covers four new product diffusions across 31 developed and developing nations from 1980-2004. Our sample accounts for about 80% of the global economic output and 60% of the global population allowing us to examine more general phenomena. (2) His model contains the implicit assumption that the diffusion speed parameter is constant throughout the diffusion life cycle of a product. Recognizing the likely effects on the speed parameter of recent changes in the marketplace, we model the parameter as a semiparametric function allowing it the flexibility to change over time. (3) We perform a variable selection to determine that the number of internet users and the consumer price index are strongly associated with the speed of diffusion.
Download or read book Nonparametric Method and Hierarchical Bayesian Approach for Parameter Estimation and Prediction written by Jing Cai and published by . This book was released on 2011 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Obtaining accurate estimates or prediction from available data is one of the important goals in statistical research. In this thesis, we propose two new statistical methods, with examples of application and simulation studies, to achieve this goal. The parametric penalized spline smoothing procedure is a flexible algorithm that requires no restricted parametric assumption and is proved to obtain more accurate estimates of curves and derivatives than available methods. In the second part of thesis, we propose a hierarchical Bayesian approach to estimate dynamic engineering model parameters and their mixed effects. This approach has the benefits of solving the identifiability problem of model parameters and accurately estimating these parameters from right censored data. It is further investigated with simulated data to perform predictions. Predicting quality with this method is proved to be better than that from procedures without considering censoring situation.
Download or read book Bayesian Nonparametric Modeling and Inference for Multiple Object Tracking written by Bahman Moraffah and published by . This book was released on 2019 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: The problem of multiple object tracking seeks to jointly estimate the time-varying cardinality and trajectory of each object. There are numerous challenges that are encountered in tracking multiple objects including a time-varying number of measurements, under varying constraints, and environmental conditions. In this thesis, the proposed statistical methods integrate the use of physical-based models with Bayesian nonparametric methods to address the main challenges in a tracking problem. In particular, Bayesian nonparametric methods are exploited to efficiently and robustly infer object identity and learn time-dependent cardinality; together with Bayesian inference methods, they are also used to associate measurements to objects and estimate the trajectory of objects. These methods differ from the current methods to the core as the existing methods are mainly based on random finite set theory. The first contribution proposes dependent nonparametric models such as the dependent Dirichlet process and the dependent Pitman-Yor process to capture the inherent time-dependency in the problem at hand. These processes are used as priors for object state distributions to learn dependent information between previous and current time steps. Markov chain Monte Carlo sampling methods exploit the learned information to sample from posterior distributions and update the estimated object parameters. The second contribution proposes a novel, robust, and fast nonparametric approach based on a diffusion process over infinite random trees to infer information on object cardinality and trajectory. This method follows the hierarchy induced by objects entering and leaving a scene and the time-dependency between unknown object parameters. Markov chain Monte Carlo sampling methods integrate the prior distributions over the infinite random trees with time-dependent diffusion processes to update object states. The third contribution develops the use of hierarchical models to form a prior for statistically dependent measurements in a single object tracking setup. Dependency among the sensor measurements provides extra information which is incorporated to achieve the optimal tracking performance. The hierarchical Dirichlet process as a prior provides the required flexibility to do inference. Bayesian tracker is integrated with the hierarchical Dirichlet process prior to accurately estimate the object trajectory. The fourth contribution proposes an approach to model both the multiple dependent objects and multiple dependent measurements. This approach integrates the dependent Dirichlet process modeling over the dependent object with the hierarchical Dirichlet process modeling of the measurements to fully capture the dependency among both object and measurements. Bayesian nonparametric models can successfully associate each measurement to the corresponding object and exploit dependency among them to more accurately infer the trajectory of objects. Markov chain Monte Carlo methods amalgamate the dependent Dirichlet process with the hierarchical Dirichlet process to infer the object identity and object cardinality. Simulations are exploited to demonstrate the improvement in multiple object tracking performance when compared to approaches that are developed based on random finite set theory.
Download or read book Advanced Methods for Modeling Markets written by Peter S. H. Leeflang and published by Springer. This book was released on 2017-08-29 with total page 725 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents advanced techniques to modeling markets, with a wide spectrum of topics, including advanced individual demand models, time series analysis, state space models, spatial models, structural models, mediation, models that specify competition and diffusion models. It is intended as a follow-on and companion to Modeling Markets (2015), in which the authors presented the basics of modeling markets along the classical steps of the model building process: specification, data collection, estimation, validation and implementation. This volume builds on the concepts presented in Modeling Markets with an emphasis on advanced methods that are used to specify, estimate and validate marketing models, including structural equation models, partial least squares, mixture models, and hidden Markov models, as well as generalized methods of moments, Bayesian analysis, non/semi-parametric estimation and endogeneity issues. Specific attention is given to big data. The market environment is changing rapidly and constantly. Models that provide information about the sensitivity of market behavior to marketing activities such as advertising, pricing, promotions and distribution are now routinely used by managers for the identification of changes in marketing programs that can improve brand performance. In today’s environment of information overload, the challenge is to make sense of the data that is being provided globally, in real time, from thousands of sources. Although marketing models are now widely accepted, the quality of the marketing decisions is critically dependent upon the quality of the models on which those decisions are based. This volume provides an authoritative and comprehensive review, with each chapter including: · an introduction to the method/methodology · a numerical example/application in marketing · references to other marketing applications · suggestions about software. Featuring contributions from top authors in the field, this volume will explore current and future aspects of modeling markets, providing relevant and timely research and techniques to scientists, researchers, students, academics and practitioners in marketing, management and economics.
Download or read book Perspectives on the Transition Toward Green and Climate Neutral Economies in Asia written by Ordóñez de Pablos, Patricia and published by IGI Global. This book was released on 2023-07-24 with total page 493 pages. Available in PDF, EPUB and Kindle. Book excerpt: Knowledge management and advanced information technologies such as AI, IoT, machine learning, and more can create digital tools and solutions to build more resilient, climate neutral, and green economies and societies. These digital tools and solutions and knowledge management can have a heavy impact on the achievement of sustainable development goals (SDGs) in Asia. Perspectives on the Transition Toward Green and Climate Neutral Economies in Asia offers innovative conceptual frameworks and theories, case studies, and empirical studies to understand how knowledge management and digital innovation can foster the transition towards more circular and climate neutral economies as well as greener economies in Asia. This book discusses how key and enabling digital tools and solutions and knowledge management can support the achievement of SDGs by 2030. Covering topics such as climate neutral economies, image recognition, and usability evaluation, this premier reference source is an excellent resource for deans, heads of departments, directors, politicians, policymakers, corporate heads, senior general managers, managing directors, librarians, students and educators of higher education, academicians, and researchers.
Download or read book Current Index to Statistics Applications Methods and Theory written by and published by . This book was released on 1998 with total page 798 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Current Index to Statistics (CIS) is a bibliographic index of publications in statistics, probability, and related fields.
Download or read book Bayesian Nonparametrics written by J.K. Ghosh and published by Springer Science & Business Media. This book was released on 2006-05-11 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the first systematic treatment of Bayesian nonparametric methods and the theory behind them. It will also appeal to statisticians in general. The book is primarily aimed at graduate students and can be used as the text for a graduate course in Bayesian non-parametrics.
Download or read book Ecological Inference written by Gary King and published by Cambridge University Press. This book was released on 2004-09-13 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: Drawing upon the recent explosion of research in the field, a diverse group of scholars surveys the latest strategies for solving ecological inference problems, the process of trying to infer individual behavior from aggregate data. The uncertainties and information lost in aggregation make ecological inference one of the most difficult areas of statistical inference, but these inferences are required in many academic fields, as well as by legislatures and the Courts in redistricting, marketing research by business, and policy analysis by governments. This wide-ranging collection of essays offers many fresh and important contributions to the study of ecological inference.
Download or read book A First Course in Bayesian Statistical Methods written by Peter D. Hoff and published by Springer Science & Business Media. This book was released on 2009-06-02 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: A self-contained introduction to probability, exchangeability and Bayes’ rule provides a theoretical understanding of the applied material. Numerous examples with R-code that can be run "as-is" allow the reader to perform the data analyses themselves. The development of Monte Carlo and Markov chain Monte Carlo methods in the context of data analysis examples provides motivation for these computational methods.
Download or read book Confronting Climate Uncertainty in Water Resources Planning and Project Design written by Patrick A. Ray and published by World Bank Publications. This book was released on 2015-08-20 with total page 149 pages. Available in PDF, EPUB and Kindle. Book excerpt: Confronting Climate Uncertainty in Water Resources Planning and Project Design describes an approach to facing two fundamental and unavoidable issues brought about by climate change uncertainty in water resources planning and project design. The first is a risk assessment problem. The second relates to risk management. This book provides background on the risks relevant in water systems planning, the different approaches to scenario definition in water system planning, and an introduction to the decision-scaling methodology upon which the decision tree is based. The decision tree is described as a scientifically defensible, repeatable, direct and clear method for demonstrating the robustness of a project to climate change. While applicable to all water resources projects, it allocates effort to projects in a way that is consistent with their potential sensitivity to climate risk. The process was designed to be hierarchical, with different stages or phases of analysis triggered based on the findings of the previous phase. An application example is provided followed by a descriptions of some of the tools available for decision making under uncertainty and methods available for climate risk management. The tool was designed for the World Bank but can be applicable in other scenarios where similar challenges arise.
