A thorough review of the most current regression methods in time series analysis

Regression methods have been an integral part of time series analysis for over a century. Recently, new developments have made major strides in such areas as non-continuous data where a linear model is not appropriate. This book introduces the reader to newer developments and more diverse regression models and methods for time series analysis.

Accessible to anyone who is familiar with the basic modern concepts of statistical inference, Regression Models for Time Series Analysis provides a much-needed examination of recent statistical developments. Primary among them is the important class of models known as generalized linear models (GLM) which provides, under some conditions, a unified regression theory suitable for continuous, categorical, and count data.

The authors extend GLM methodology systematically to time series where the primary and covariate data are both random and stochastically dependent. They introduce readers to various regression models developed during the last thirty years or so and summarize classical and more recent results concerning state space models. To conclude, they present a Bayesian approach to prediction and interpolation in spatial data adapted to time series that may be short and/or observed irregularly. Real data applications and further results are presented throughout by means of chapter problems and complements.

Notably, the book covers:

• Important recent developments in Kalman filtering, dynamic GLMs, and state-space modeling
• Associated computational issues such as Markov chain, Monte Carlo, and the EM-algorithm
• Prediction and interpolation
• Stationary processes

The writing "bible" for financial professionals The Investment Writing Handbook provides practical, accessible guidance for crafting more effective investor communications. Written by an award-winning writer, editor, and speechwriter, this book explains the principles and conventions that help writing achieve its purpose; whether you need to inform, educate, persuade, or motivate, you'll become better-equipped to develop a broad range of communications and literature for investor consumption. Examples from real-world financial institutions illustrate expert execution, while explanations and advice targeted specifically toward investor relations gives you the help you need quickly. From white papers and investment commentary to RFPs, product literature, and beyond, this book is the financial writer's "bible" that you should keep within arm's reach. Investment writing is one of the primary influences on investors' attitudes. It educates, informs decisions, shapes opinions, and drives behavior so shouldn't it be expertly-crafted to achieve its intended goal? This book explains the "tricks of the trade" to help you get your message across. * Understand the principles of effective investor communication * Master the conventions of informative and persuasive writing * Examine well-written sample documents from real-world institutions * Improve research papers, presentations, investor letters, marketing literature, and more Virtually all firms with investors as clients need to communicate to them regularly, but few financial professionals receive formal training in investor communications. When investors' opinions, attitudes, and actions determine the health of your company, it is vitally important that these communications not be left to chance. The Investment Writing Handbook provides essential guidance and clear explanations to help you transform your communication strategy, execution, and results.

'The book provides a comprehensive review of the DRM approach to data fusion. It is well written and easy to follow, although the technical details are not trivial. The authors did an excellent job in making a concise introduction of the statistical techniques in data fusion. The book contains several real data ... Overall, I found that the book covers an important topic and the DRM is a promising tool in this area. Researchers on data fusion will surely find this book very helpful and I will use this book in studying with my PhD students.'Journal of the American Statistical AssociationThis book comes up with estimates or decisions based on multiple data sources as opposed to more narrowly defined estimates or decisions based on single data sources. And as the world is awash with data obtained from numerous and varied processes, there is a need for appropriate statistical methods which in general produce improved inference by multiple data sources.The book contains numerous examples useful to practitioners from genomics. Topics range from sensors (radars), to small area estimation of body mass, to the estimation of small tail probabilities, to predictive distributions in time series analysis.