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Focusing on Bayesian approaches and computations using analytic and
simulation-based methods for inference, Time Series: Modeling,
Computation, and Inference, Second Edition integrates mainstream
approaches for time series modeling with significant recent
developments in methodology and applications of time series
analysis. It encompasses a graduate-level account of Bayesian time
series modeling, analysis and forecasting, a broad range of
references to state-of-the-art approaches to univariate and
multivariate time series analysis, and contacts research frontiers
in multivariate time series modeling and forecasting. It presents
overviews of several classes of models and related methodology for
inference, statistical computation for model fitting and
assessment, and forecasting. It explores the connections between
time- and frequency-domain approaches and develop various models
and analyses using Bayesian formulations and computation, including
use of computations based on Markov chain Monte Carlo (MCMC) and
sequential Monte Carlo (SMC) methods. It illustrates the models and
methods with examples and case studies from a variety of fields,
including signal processing, biomedicine, environmental science,
and finance. Along with core models and methods, the book
represents state-of-the art approaches to analysis and forecasting
in challenging time series problems. It also demonstrates the
growth of time series analysis into new application areas in recent
years, and contacts recent and relevant modeling developments and
research challenges. New in the second edition: Expanded on aspects
of core model theory and methodology. Multiple new examples and
exercises. Detailed development of dynamic factor models. Updated
discussion and connections with recent and current research
frontiers.
Practical in its approach, Applied Bayesian Forecasting and Time
Series Analysis provides the theories, methods, and tools necessary
for forecasting and the analysis of time series. The authors unify
the concepts, model forms, and modeling requirements within the
framework of the dynamic linear mode (DLM). They include a complete
theoretical development of the DLM and illustrate each step with
analysis of time series data. Using real data sets the authors:
Explore diverse aspects of time series, including how to identify,
structure, explain observed behavior, model structures and
behaviors, and interpret analyses to make informed forecasts
Illustrate concepts such as component decomposition, fundamental
model forms including trends and cycles, and practical modeling
requirements for routine change and unusual events Conduct all
analyses in the BATS computer programs, furnishing online that
program and the more than 50 data sets used in the text The result
is a clear presentation of the Bayesian paradigm: quantified
subjective judgements derived from selected models applied to time
series observations. Accessible to undergraduates, this unique
volume also offers complete guidelines valuable to researchers,
practitioners, and advanced students in statistics, operations
research, and engineering.
Focusing on Bayesian approaches and computations using analytic and
simulation-based methods for inference, Time Series: Modeling,
Computation, and Inference, Second Edition integrates mainstream
approaches for time series modeling with significant recent
developments in methodology and applications of time series
analysis. It encompasses a graduate-level account of Bayesian time
series modeling, analysis and forecasting, a broad range of
references to state-of-the-art approaches to univariate and
multivariate time series analysis, and contacts research frontiers
in multivariate time series modeling and forecasting. It presents
overviews of several classes of models and related methodology for
inference, statistical computation for model fitting and
assessment, and forecasting. It explores the connections between
time- and frequency-domain approaches and develop various models
and analyses using Bayesian formulations and computation, including
use of computations based on Markov chain Monte Carlo (MCMC) and
sequential Monte Carlo (SMC) methods. It illustrates the models and
methods with examples and case studies from a variety of fields,
including signal processing, biomedicine, environmental science,
and finance. Along with core models and methods, the book
represents state-of-the art approaches to analysis and forecasting
in challenging time series problems. It also demonstrates the
growth of time series analysis into new application areas in recent
years, and contacts recent and relevant modeling developments and
research challenges. New in the second edition: Expanded on aspects
of core model theory and methodology. Multiple new examples and
exercises. Detailed development of dynamic factor models. Updated
discussion and connections with recent and current research
frontiers.
Practical in its approach, Applied Bayesian Forecasting and Time
Series Analysis provides the theories, methods, and tools necessary
for forecasting and the analysis of time series. The authors unify
the concepts, model forms, and modeling requirements within the
framework of the dynamic linear mode (DLM). They include a complete
theoretical development of the DLM and illustrate each step with
analysis of time series data. Using real data sets the authors:
Explore diverse aspects of time series, including how to identify,
structure, explain observed behavior, model structures and
behaviors, and interpret analyses to make informed forecasts
Illustrate concepts such as component decomposition, fundamental
model forms including trends and cycles, and practical modeling
requirements for routine change and unusual events Conduct all
analyses in the BATS computer programs, furnishing online that
program and the more than 50 data sets used in the text The result
is a clear presentation of the Bayesian paradigm: quantified
subjective judgements derived from selected models applied to time
series observations. Accessible to undergraduates, this unique
volume also offers complete guidelines valuable to researchers,
practitioners, and advanced students in statistics, operations
research, and engineering.
This text is concerned with Bayesian learning, inference and
forecasting in dynamic environments. We describe the structure and
theory of classes of dynamic models and their uses in forecasting
and time series analysis. The principles, models and methods of
Bayesian forecasting and time - ries analysis have been developed
extensively during the last thirty years.
Thisdevelopmenthasinvolvedthoroughinvestigationofmathematicaland
statistical aspects of forecasting models and related techniques.
With this has come experience with applications in a variety of
areas in commercial, industrial, scienti?c, and socio-economic
?elds. Much of the technical - velopment has been driven by the
needs of forecasting practitioners and applied researchers. As a
result, there now exists a relatively complete statistical and
mathematical framework, presented and illustrated here. In writing
and revising this book, our primary goals have been to present a
reasonably comprehensive view of Bayesian ideas and methods in m-
elling and forecasting, particularly to provide a solid reference
source for advanced university students and research workers.
The series of workshops Case Studies in Bayesian Statistics at Carnegie Mellon University is unique in devoting an entire meeting to extended presentation and discussion of scientific investigations in which statisticians play central roles within integrated, cross- disciplinary teams. The goal has been to elucidate the interplay between Bayesian theory and practice, and thereby identify successful methods and indicate important directions for future research. This volume contains the four invited case studies, with accompanying discussion, and nine contributed papers, from the 4th workshop, which was held September 27-28, 1997. While most of the case studies in this volume come from biomedical research, the reader will also find studies in environmental science and marketing research. Students and teachers of statistics, research statisticians, and investigators from other fields should find a wealth of ideas and methods in this series of case studies.
The second edition of this book includes revised, updated, and additional material on the structure, theory, and application of classes of dynamic models in Bayesian time series analysis and forecasting. In addition to wide ranging updates to central material, the second edition includes many more exercises and covers new topics at the research and application frontiers of Bayesian forecastings.
The 4th Workshop on Case Studies in Bayesian Statistics was held at
the Car negie Mellon University campus on September 27-28, 1997. As
in the past, the workshop featured both invited and contributed
case studies. The former were presented and discussed in detail
while the latter were presented in poster format. This volume
contains the four invited case studies with the accompanying discus
sion as well as nine contributed papers selected by a refereeing
process. While most of the case studies in the volume come from
biomedical research the reader will also find studies in
environmental science and marketing research. INVITED PAPERS In
Modeling Customer Survey Data, Linda A. Clark, William S.
Cleveland, Lorraine Denby, and Chuanhai LiD use hierarchical
modeling with time series components in for customer value analysis
(CVA) data from Lucent Technologies. The data were derived from
surveys of customers of the company and its competi tors, designed
to assess relative performance on a spectrum of issues including
product and service quality and pricing. The model provides a full
description of the CVA data, with random location and scale effects
for survey respondents and longitudinal company effects for each
attribute. In addition to assessing the performance of specific
companies, the model allows the empirical exploration of the
conceptual basis of consumer value analysis. The authors place
special em phasis on graphical displays for this complex,
multivariate set of data and include a wealth of such plots in the
paper."
Bayesian analysis has developed rapidly in applications in the last
two decades and research in Bayesian methods remains dynamic and
fast-growing. Dramatic advances in modelling concepts and
computational technologies now enable routine application of
Bayesian analysis using increasingly realistic stochastic models,
and this drives the adoption of Bayesian approaches in many areas
of science, technology, commerce, and industry.
This Handbook explores contemporary Bayesian analysis across a
variety of application areas. Chapters written by leading exponents
of applied Bayesian analysis showcase the scientific ease and
natural application of Bayesian modelling, and present solutions to
real, engaging, societally important and demanding problems. The
chapters are grouped into five general areas: Biomedical &
Health Sciences; Industry, Economics & Finance; Environment
& Ecology; Policy, Political & Social Sciences; and Natural
& Engineering Sciences, and Appendix material in each touches
on key concepts, models, and techniques of the chapter that are
also of broader pedagogic and applied interest.
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