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The Future of Luxury Brands
The concepts of artification and sustainability are now both at the heart of luxury brand marketing strategies; artification as an ongoing process of transformation in the world of art and sustainability as an indispensable response to the issues of our times. The Future of Luxury Brands examines three interrelated luxury-marketing segments—the art world, fashion and fine wines including hospitality services—through the dual lenses of sustainability and artification. From safeguarding human and natural resources to upholding labor rights and protecting the environment, sustainability has taken center stage in consumer consciousness, embodying both moral authority and sound business pract...
Stochastic Networks
Two of the most exciting topics of current research in stochastic networks are the complementary subjects of stability and rare events - roughly, the former deals with the typical behavior of networks, and the latter with significant atypical behavior. Both are classical topics, of interest since the early days of queueing theory, that have experienced renewed interest mo tivated by new applications to emerging technologies. For example, new stability issues arise in the scheduling of multiple job classes in semiconduc tor manufacturing, the so-called "re-entrant lines;" and a prominent need for studying rare events is associated with the design of telecommunication systems using the new ATM...
Stochastic Epidemic Models and Their Statistical Analysis
The present lecture notes describe stochastic epidemic models and methods for their statistical analysis. Our aim is to present ideas for such models, and methods for their analysis; along the way we make practical use of several probabilistic and statistical techniques. This will be done without focusing on any specific disease, and instead rigorously analyzing rather simple models. The reader of these lecture notes could thus have a two-fold purpose in mind: to learn about epidemic models and their statistical analysis, and/or to learn and apply techniques in probability and statistics. The lecture notes require an early graduate level knowledge of probability and They introduce several te...
The Nature of the Japanese State
Brian J. McVeigh uses a unique anthropological approach to step outside flawed stereotypes of Japanese society and really engage in the current debate over the role of bureaucracy in Japanese politics. To many in the West, Japan appears as a paradox: a rational, high-tech economic superpower and yet at the same time a deeply ritualistic and ceremonial society. This adventurous new study demonstrates how these nominally conflicting impressions of Japan can be reconciled and a greater understanding of the state achieved.
Smoothness Priors Analysis of Time Series
Smoothness Priors Analysis of Time Series addresses some of the problems of modeling stationary and nonstationary time series primarily from a Bayesian stochastic regression "smoothness priors" state space point of view. Prior distributions on model coefficients are parametrized by hyperparameters. Maximizing the likelihood of a small number of hyperparameters permits the robust modeling of a time series with relatively complex structure and a very large number of implicitly inferred parameters. The critical statistical ideas in smoothness priors are the likelihood of the Bayesian model and the use of likelihood as a measure of the goodness of fit of the model. The emphasis is on a general state space approach in which the recursive conditional distributions for prediction, filtering, and smoothing are realized using a variety of nonstandard methods including numerical integration, a Gaussian mixture distribution-two filter smoothing formula, and a Monte Carlo "particle-path tracing" method in which the distributions are approximated by many realizations. The methods are applicable for modeling time series with complex structures.
Toward a Sociological Theory of Religion and Health
Driven by funding agencies, empirical research in the social scientific study of health and medicine has grown in quantity and developed in quality. When it became evident, in what is now a tradition of inquiry, that people’s religious activities had significant health consequences, a portion of that body of work began to focus more frequently on the relationship between health and religion. The field has reached a point where book-length summaries of empirical findings, especially those pertinent to older people, can identify independent, mediating, and dependent variables of interest. Every mediating variable, even if considered as a “control” variable, represents an explanation, a small theory of some kind. However, taken in granular form, as it were, the multiple theories do not comprise mid-level theory, let alone a general theoretical framework. This volume seeks to move toward more general theoretical development. Contributors include: Alex Bierman, Sherry Cummings, Christopher G. Ellison, Andrea K. Henderson, Barbara Kilbourne, Neal Krause, Jeff Levin, Robert S. Levine, Eric Liu, Michael K. Roemer, Scott Schieman, and Ephraim Shapiro.
Practical Nonparametric and Semiparametric Bayesian Statistics
A compilation of original articles by Bayesian experts, this volume presents perspectives on recent developments on nonparametric and semiparametric methods in Bayesian statistics. The articles discuss how to conceptualize and develop Bayesian models using rich classes of nonparametric and semiparametric methods, how to use modern computational tools to summarize inferences, and how to apply these methodologies through the analysis of case studies.
Bayesian Statistical Methods
Bayesian Statistical Methods provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis. This book focuses on Bayesian methods applied routinely in practice including multiple linear regression, mixed effects models and generalized linear models (GLM). The authors include many examples with complete R code and comparisons with analogous frequentist procedures. In addition to the basic concepts of Bayesian inferential methods, the book covers many general topics: Advice on selecting prior distributions Computational methods including Markov chain Monte Carlo (MCMC) Model-comparison and goodness-of-fit measures, including sensitivity to prior...
Nonparametric Statistics for Stochastic Processes
This book is devoted to the theory and applications of nonparametic functional estimation and prediction. Chapter 1 provides an overview of inequalities and limit theorems for strong mixing processes. Density and regression estimation in discrete time are studied in Chapter 2 and 3. The special rates of convergence which appear in continuous time are presented in Chapters 4 and 5. This second edition is extensively revised and it contains two new chapters. Chapter 6 discusses the surprising local time density estimator. Chapter 7 gives a detailed account of implementation of nonparametric method and practical examples in economics, finance and physics. Comarison with ARMA and ARCH methods sh...
Model-Oriented Design of Experiments
Here, the authors explain the basic ideas so as to generate interest in modern problems of experimental design. The topics discussed include designs for inference based on nonlinear models, designs for models with random parameters and stochastic processes, designs for model discrimination and incorrectly specified (contaminated) models, as well as examples of designs in functional spaces. Since the authors avoid technical details, the book assumes only a moderate background in calculus, matrix algebra, and statistics. However, at many places, hints are given as to how readers may enhance and adopt the basic ideas for advanced problems or applications. This allows the book to be used for courses at different levels, as well as serving as a useful reference for graduate students and researchers in statistics and engineering.
