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Hidden Markov Models for Time Series
Hidden Markov Models for Time Series: An Introduction Using R, Second Edition illustrates the great flexibility of hidden Markov models (HMMs) as general-purpose models for time series data. The book provides a broad understanding of the models and their uses. After presenting the basic model formulation, the book covers estimation, forecasting, decoding, prediction, model selection, and Bayesian inference for HMMs. Through examples and applications, the authors describe how to extend and generalize the basic model so that it can be applied in a rich variety of situations. The book demonstrates how HMMs can be applied to a wide range of types of time series: continuous-valued, circular, mult...
Zucchini
Zucchini is one of the gardens' most prolific plants, but its bounty often leaves gardeners wondering what to do with the fruit, other than hiding them in unsuspecting neighbours' cars and mailboxes. Master Chef John Butler presents 100 fresh ways to use zucchini, from appetizers to main dishes, breads and biscuits, sweet treats and more. Foreword by Lois Hole.
Dependence Modeling with Copulas
Dependence Modeling with Copulas covers the substantial advances that have taken place in the field during the last 15 years, including vine copula modeling of high-dimensional data. Vine copula models are constructed from a sequence of bivariate copulas. The book develops generalizations of vine copula models, including common and structured factor models that extend from the Gaussian assumption to copulas. It also discusses other multivariate constructions and parametric copula families that have different tail properties and presents extensive material on dependence and tail properties to assist in copula model selection. The author shows how numerical methods and algorithms for inference and simulation are important in high-dimensional copula applications. He presents the algorithms as pseudocode, illustrating their implementation for high-dimensional copula models. He also incorporates results to determine dependence and tail properties of multivariate distributions for future constructions of copula models.
Teachers' Professional Development
Within the central topics of the debate on teachers’ professionalism are the problems of research-based and evidence-based initial and lifelong teacher behavior. Although the statements on professional similarities of teacher actions with those of other (academic) professionals are very plausible, there remains a central task for teacher education programs: How to develop towards such expertise—which is equal to evidence convictions—effectively and efficiently. Which role do scientific research and its results play in this context? How can research results be converted into recommendations for teacher actions? The contributions to this book focus on central problems of the conversion p...
Extreme Value Methods with Applications to Finance
Extreme value theory (EVT) deals with extreme (rare) events, which are sometimes reported as outliers. Certain textbooks encourage readers to remove outliers—in other words, to correct reality if it does not fit the model. Recognizing that any model is only an approximation of reality, statisticians are eager to extract information about unknown distribution making as few assumptions as possible. Extreme Value Methods with Applications to Finance concentrates on modern topics in EVT, such as processes of exceedances, compound Poisson approximation, Poisson cluster approximation, and nonparametric estimation methods. These topics have not been fully focused on in other books on extremes. In...
Development as Freedom
Amartya Sen uses his 1999 work Development as Freedom to evaluate the processes and outcomes of economic development. Having come to the conclusion that development is best summed up as the expansion of freedom, Sen examines traditional definitions and understandings of the term. He says people tend to think of freedoms as economic (the freedom to enter into market exchanges) or political (the freedom to vote and be an active citizen), and tries to understand why the definition has been so narrow hitherto. He concludes that an evaluation of true freedom must necessarily include the freedom to access social services such as healthcare, sanitation and nutrition, just as much as it must acknowledge economic and political freedoms. Evaluating the relevance of the current thinking behind development, Sen’s concludes that the term ‘freedom’ cannot simply be about income. In many ways, measuring income does not account for various “unfreedoms” (manmade or natural bars to wellbeing) that hinder development. Sen’s evaluation is all the more powerful for its clarity: "The freedom-centered perspective has a generic similarity to the common concern with ‘quality of life."
Applications of State Space Models in Finance
State space models play a key role in the estimation of time-varying sensitivities in financial markets. The objective of this book is to analyze the relative merits of modern time series techniques, such as Markov regime switching and the Kalman filter, to model structural changes in the context of widely used concepts in finance. The presented material will be useful for financial economists and practitioners who are interested in taking time-variation in the relationship between financial assets and key economic factors explicitly into account. The empirical part illustrates the application of the various methods under consideration. As a distinctive feature, it includes a comprehensive analysis of the ability of time-varying coefficient models to estimate and predict the conditional nature of systematic risks for European industry portfolios.
Compstat
This COMPSTAT 2002 book contains the Keynote, Invited, and Full Contributed papers presented in Berlin, August 2002. A companion volume including Short Communications and Posters is published on CD. The COMPSTAT 2002 is the 15th conference in a serie of biannual conferences with the objective to present the latest developments in Computational Statistics and is taking place from August 24th to August 28th, 2002. Previous COMPSTATs were in Vienna (1974), Berlin (1976), Leiden (1978), Edinburgh (1980), Toulouse (1982), Pra~ue (1984), Rome (1986), Copenhagen (1988), Dubrovnik (1990), Neuchatel (1992), Vienna (1994), Barcelona (1996), Bris tol (1998) and Utrecht (2000). COMPSTAT 2002 is organise...
The Learning Potential of the Workplace
In our research programme “The Learning Potential of the Workplace” we set the task to analyse, describe and explain the conditions of the workplace as a tool for learning. Learning potential is for some experts an individual asset, others see the learning potential in the external conditions in work and work processes; again others see it in the reflection on action by peers, colleagues and experts. Some results are disappointing when the belief is that workplace learning might be the panacea for all life long learning problems; some results are hopeful for those who belief that the workplace is one of the potential places where people can learn specific competencies. The selection of chapters in this volume represent different opinions, visions and methodology to study workplace learning and the effects. The focus is on vocational education and human resource development, so workplace learning as a means to socialize youngsters in work organisations on their way to professionals and workplace learning as means to work, to innovate, to do maintenance work, and to create knowledge.
Machine Learning with R
Written as a tutorial to explore and understand the power of R for machine learning. This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning approach, each step in the process is detailed, from preparing the data for analysis to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks.Intended for those who want to learn how to use R's machine learning capabilities and gain insight from your data. Perhaps you already know a bit about machine learning, but have never used R; or perhaps you know a little R but are new to machine learning. In either case, this book will get you up and running quickly. It would be helpful to have a bit of familiarity with basic programming concepts, but no prior experience is required.
