Showing posts with label statistics. Show all posts
Showing posts with label statistics. Show all posts

1/02/2012

Kiev Review

Kiev
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I love to see Americans write European history. Not so much for the reason that we can't do it well as much for the reason that too often we refuse to do it well. In an age of American history scholarship dominated by revisionism, politically correct relativism, and otherwise trendy arcane trash, this brilliant analysis is like fine wine after years of Budweiser. Hamm chooses a national/ethnic context in which to tell the story of how these various peoples transformed Kiev from a forgotten backwater to the cosmopolitan capital of Ukraine. All of this took place in the matter of about 100 years--a blip on the radar screen of Kievan history. But what a century! Poles, Jews, Russians, Ukrainians, Greeks, the Decembrists, art, education, music, literature, commerce, war, pogroms, conflagration, disease, and revolution. It's all here, told in the perfect combination of lucidity and attention to detail as to both fascinate and instruct. Isn't every great work of history supposed to do that? I know I've come across something special when I feel like I've actually lived through a particular history after reading it. We all become residents of Kiev here. One thing that prospective readers should note: Hamm likes numbers. The book is full of statistics, but it never completely relies on them. The author always uses numbers to illustrate his point, but he never tells the story itself with numbers. Though the topic may seem to be a bit esoteric, Hamm's thesis suggests that we should consider understanding urban history as a history of people rather than of institutions and infrastructure. Wonderful stuff, even if you have no interest in Ukraine.

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Bayesian Forecasting and Dynamic Models (Springer Series in Statistics) Review

Bayesian Forecasting and Dynamic Models (Springer Series in Statistics)
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A Bayesian approach is a natural way to deal with time series data. You construct a model based on past data and prior information and use the model to predict future values in the series. When the new observations come in the model can be updated (model parameters reestimated) and forecasts can be updated. Most of the time series literature deals with the classical (frequentist) approach incluing the well-known book by Box and Jenkins on forecasting and control. This book provides a mathematically rigorous treament of time series modeling based on a Bayesian approach. Many common forecasting procedures including the Kalman filter are iterative algorithms that could be derived as solutions for forecasting based on a Bayesian model of the time series.
This is the best text available on this topic.

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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 in the first edition, the second edition includes many more exercises and covers new topics at the research and application frontiers of Bayesian forecastings.

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9/19/2011

Applied Bayesian Forecasting and Time Series Analysis (Chapman & Hall/CRC Texts in Statistical Science) Review

Applied Bayesian Forecasting and Time Series Analysis (Chapman and Hall/CRC Texts in Statistical Science)
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This is the second text authored by West and Harrison on the Bayesian approach to time series. I have read and reviewed both of them. The other text by these authors provides a rigorous development of time series analysis using Bayesian methods. While that text is very good at helping the reader understand the theory and the whys and hows of implementing it, this text is very much a book that shows applications and how the Bayesian time series approach is used to produce forecasts based on this methodology. The authors are expert researchers on this topic and aside from these two books texts on Bayesian approaches to time series are not easy to find.

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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 andthe 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.

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