Finite Mixture and Markov Switching Models

Author: Sylvia Frühwirth-Schnatter

Publisher: Springer Science & Business Media

ISBN: 0387357688

Category: Mathematics

Page: 494

View: 6930

The past decade has seen powerful new computational tools for modeling which combine a Bayesian approach with recent Monte simulation techniques based on Markov chains. This book is the first to offer a systematic presentation of the Bayesian perspective of finite mixture modelling. The book is designed to show finite mixture and Markov switching models are formulated, what structures they imply on the data, their potential uses, and how they are estimated. Presenting its concepts informally without sacrificing mathematical correctness, it will serve a wide readership including statisticians as well as biologists, economists, engineers, financial and market researchers.

Finite Mixture Multinomiales Probitmodell

Konzeption und Umsetzung

Author: Friederike Paetz

Publisher: Springer-Verlag

ISBN: 3658026626

Category: Business & Economics

Page: 172

View: 3418

​Neuere methodische Weiterentwicklungen der Conjoint-Analyse ermöglichen heute die simultane Segmentierung eines Gesamtmarktes von Konsumenten in homogene Teilmärkte und die Schätzung entsprechender segmentspezifischer Teilnutzenwertstrukturen. Auf diesem Wege soll der Heterogenität im Konsumentenverhalten Rechnung getragen werden. Das im Rahmen der simultanen Segmentierung derzeit meistgenutzte Conjoint Choice-Modell ist das Finite Mixture Logitmodell. Dieses unterstellt Unabhängigkeit der Gesamtnutzen aller Alternativen, die einem Konsumenten zur Auswahl gestellt werden, und postuliert somit, dass Auswahlentscheidungen unabhängig vom Kontext sind, in dem die Alternativen dem Konsumenten präsentiert werden. Diese Annahme erscheint in Bezug auf die Abbildung realen Kaufverhaltens jedoch fraglich. Friederike Paetz entwickelt ein Finite Mixture Multinomiales Probitmodell, welches explizit Abhängigkeiten zwischen (den Gesamtnutzen der) Alternativen berücksichtigen kann. Abhängigkeiten zwischen Alternativen können einerseits innerhalb einer Choice Task und andererseits durch die Erinnerung an Alternativen vorangegangener Auswahlsituationen entstehen. Das neu entwickelte Modell wird anschließend sowohl in einer Simulationsstudie als auch in einer empirischen Studie mit Modellen, die Unabhängigkeit unterstellen, verglichen.


Estimation and Applications

Author: Kerrie L. Mengersen,Christian Robert,Mike Titterington

Publisher: John Wiley & Sons

ISBN: 1119998441

Category: Mathematics

Page: 330

View: 2953

This book uses the EM (expectation maximization) algorithm to simultaneously estimate the missing data and unknown parameter(s) associated with a data set. The parameters describe the component distributions of the mixture; the distributions may be continuous or discrete. The editors provide a complete account of the applications, mathematical structure and statistical analysis of finite mixture distributions along with MCMC computational methods, together with a range of detailed discussions covering the applications of the methods and features chapters from the leading experts on the subject. The applications are drawn from scientific discipline, including biostatistics, computer science, ecology and finance. This area of statistics is important to a range of disciplines, and its methodology attracts interest from researchers in the fields in which it can be applied.

Nonlinear Financial Econometrics: Markov Switching Models, Persistence and Nonlinear Cointegration

Author: Greg N. Gregoriou,Razvan Pascalau

Publisher: Springer

ISBN: 0230295215

Category: Business & Economics

Page: 196

View: 2446

This book proposes new methods to value equity and model the Markowitz efficient frontier using Markov switching models and provide new evidence and solutions to capture the persistence observed in stock returns across developed and emerging markets.

Handbook of Mixture Analysis

Author: Sylvia Fruhwirth-Schnatter,Gilles Celeux,Christian P. Robert

Publisher: CRC Press

ISBN: 0429508867

Category: Computers

Page: 498

View: 3243

Mixture models have been around for over 150 years, and they are found in many branches of statistical modelling, as a versatile and multifaceted tool. They can be applied to a wide range of data: univariate or multivariate, continuous or categorical, cross-sectional, time series, networks, and much more. Mixture analysis is a very active research topic in statistics and machine learning, with new developments in methodology and applications taking place all the time. The Handbook of Mixture Analysis is a very timely publication, presenting a broad overview of the methods and applications of this important field of research. It covers a wide array of topics, including the EM algorithm, Bayesian mixture models, model-based clustering, high-dimensional data, hidden Markov models, and applications in finance, genomics, and astronomy. Features: Provides a comprehensive overview of the methods and applications of mixture modelling and analysis Divided into three parts: Foundations and Methods; Mixture Modelling and Extensions; and Selected Applications Contains many worked examples using real data, together with computational implementation, to illustrate the methods described Includes contributions from the leading researchers in the field The Handbook of Mixture Analysis is targeted at graduate students and young researchers new to the field. It will also be an important reference for anyone working in this field, whether they are developing new methodology, or applying the models to real scientific problems.

Time Series

Modeling, Computation, and Inference

Author: Raquel Prado,Mike West

Publisher: CRC Press

ISBN: 1439882754

Category: Mathematics

Page: 368

View: 2313

Focusing on Bayesian approaches and computations using simulation-based methods for inference, Time Series: Modeling, Computation, and Inference 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 and analysis, a broad range of references to state-of-the-art approaches to univariate and multivariate time series analysis, and emerging topics at research frontiers. The book presents overviews of several classes of models and related methodology for inference, statistical computation for model fitting and assessment, and forecasting. The authors also explore the connections between time- and frequency-domain approaches and develop various models and analyses using Bayesian tools, such as Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) methods. They illustrate the models and methods with examples and case studies from a variety of fields, including signal processing, biomedicine, and finance. Data sets, R and MATLAB® code, and other material are available on the authors’ websites. Along with core models and methods, this text offers sophisticated tools for analyzing challenging time series problems. It also demonstrates the growth of time series analysis into new application areas.

Handbook of Research Methods and Applications in Empirical Finance

Author: Adrian R. Bell,Chris Brooks,Marcel Prokopczuk

Publisher: Edward Elgar Publishing

ISBN: 0857936093

Category: Business & Economics

Page: 504

View: 9145

This impressive Handbook presents the quantitative techniques that are commonly employed in empirical finance research together with real-world, state-of-the-art research examples. Written by international experts in their field, the unique approach describes a question or issue in finance and then demonstrates the methodologies that may be used to solve it. All of the techniques described are used to address real problems rather than being presented for their own sake, and the areas of application have been carefully selected so that a broad range of methodological approaches can be covered. The Handbook is aimed primarily at doctoral researchers and academics who are engaged in conducting original empirical research in finance. In addition, the book will be useful to researchers in the financial markets and also advanced Masters-level students who are writing dissertations.

Handbook of Income Distribution

Author: Anthony B. Atkinson,Francois Bourguignon

Publisher: Elsevier

ISBN: 0444594760

Category: Social Science

Page: 2366

View: 8772

What new theories, evidence, explanations, and policies have shaped our studies of income distribution in the 21st century? Editors Tony Atkinson and Francois Bourguignon assemble the expertise of leading authorities in this survey of substantive issues. In two volumes they address subjects that were not covered in Volume 1 (2000), such as education, health and experimental economics; and subjects that were covered but where there have been substantial new developments, such as the historical study of income inequality and globalization. Some chapters discuss future growth areas, such as inheritance, the links between inequality and macro-economics and finance, and the distributional implications of climate change. They also update empirical advances and major changes in the policy environment. The volumes define and organize key areas of income distribution studies Contributors focus on identifying newly developing questions and opportunities for future research The authoritative articles emphasize the ways that income mobility and inequality studies have recently gained greater political significance

Amstat News

Author: N.A

Publisher: N.A


Category: Statistics

Page: N.A

View: 3508

Angewandte Statistik

Zweiter Teil Mehrdimensionale Probleme

Author: Kurt Stange

Publisher: Springer-Verlag

ISBN: 3642805965

Category: Technology & Engineering

Page: 506

View: 7989

Handbuch der sozialwissenschaftlichen Datenanalyse

Author: Christof Wolf,Henning Best

Publisher: Springer-Verlag

ISBN: 3531920383

Category: Political Science

Page: 1098

View: 8317

Das Handbuch der sozialwissenschaftlichen Datenanalyse bietet in über 40 Kapiteln eine umfassende Darstellung multivariater Analyseverfahren. Schwerpunkte des Handbuchs bilden Grundlagen der Datenanalyse, regressionsanalytische Verfahren für Quer- und Längsschnittsdaten sowie Skalierungsverfahren. Behandelt werden u. a. OLS-, logistische und robuste Regression, Strukturgleichungsmodelle, Mehrebenen-, Panel-, Ereignisdaten- und Zeitreihenanalyse, MDS und Rasch-Modelle. Darüber hinaus werden viele neuere Verfahren dargestellt, etwa multiple Imputation, Bootstrappen, Analyse latenter Klassen und propensity score matching. Jedes Kapitel beginnt mit einer allgemein verständlichen Einführung. Es folgt eine Darstellung der mathematisch-statistischen Grundlagen. Anschließend wird jedes Verfahren anhand eines sozialwissenschaftlichen Beispiels vorgestellt. Die Beiträge enden mit Hinweisen auf typische Anwendungsfehler und einer kommentierten Literaturempfehlung.


Author: Andrew C. Harvey

Publisher: De Gruyter Oldenbourg

ISBN: 9783486230062


Page: 379

View: 1575

Gegenstand des Werkes sind Analyse und Modellierung von Zeitreihen. Es wendet sich an Studierende und Praktiker aller Disziplinen, in denen Zeitreihenbeobachtungen wichtig sind.

Image Segmentation and Compression Using Hidden Markov Models

Author: Jia Li,Robert M. Gray

Publisher: Springer Science & Business Media

ISBN: 9780792378990

Category: Computers

Page: 141

View: 1064

In the current age of information technology, the issues of distributing and utilizing images efficiently and effectively are of substantial concern. Solutions to many of the problems arising from these issues are provided by techniques of image processing, among which segmentation and compression are topics of this book. Image segmentation is a process for dividing an image into its constituent parts. For block-based segmentation using statistical classification, an image is divided into blocks and a feature vector is formed for each block by grouping statistics of its pixel intensities. Conventional block-based segmentation algorithms classify each block separately, assuming independence of feature vectors. Image Segmentation and Compression Using Hidden Markov Models presents a new algorithm that models the statistical dependence among image blocks by two dimensional hidden Markov models (HMMs). Formulas for estimating the model according to the maximum likelihood criterion are derived from the EM algorithm. To segment an image, optimal classes are searched jointly for all the blocks by the maximum a posteriori (MAP) rule. The 2-D HMM is extended to multiresolution so that more context information is exploited in classification and fast progressive segmentation schemes can be formed naturally. The second issue addressed in the book is the design of joint compression and classification systems using the 2-D HMM and vector quantization. A classifier designed with the side goal of good compression often outperforms one aimed solely at classification because overfitting to training data is suppressed by vector quantization. Image Segmentation and Compression Using Hidden Markov Models is an essential reference source for researchers and engineers working in statistical signal processing or image processing, especially those who are interested in hidden Markov models. It is also of value to those working on statistical modeling.


Wie man die richtigen Entscheidungen trifft

Author: Gerd Gigerenzer

Publisher: C. Bertelsmann Verlag

ISBN: 3641119901

Category: Psychology

Page: 400

View: 947

Der neue Bestseller von Gerd Gigerenzer Erinnern wir uns an die weltweite Angst vor der Schweinegrippe, als Experten eine nie dagewesene Pandemie prognostizierten und Impfstoff für Millionen produziert wurde, der später still und heimlich entsorgt werden musste. Für Gerd Gigerenzer ist dies nur ein Beleg unseres irrationalen Umgangs mit Risiken. Und das gilt für Experten ebenso wie für Laien. An Beispielen aus Medizin, Rechtswesen und Finanzwelt erläutert er, wie die Psychologie des Risikos funktioniert, was sie mit unseren entwicklungsgeschichtlich alten Hirnstrukturen zu tun hat und welche Gefahren damit einhergehen. Dabei analysiert er die ungute Rolle von irreführenden Informationen, die von Medien und Fachleuten verbreitet werden. Doch Risiken und Ungewissheiten richtig einzuschätzen kann und sollte jeder lernen. Diese Risikoschulung erprobt Gigerenzer seit vielen Jahren mit verblüffenden Ergebnissen. Sein Fazit: Schon Kinder können lernen, mit Risiken realistisch umzugehen und sich gegen Panikmache wie Verharmlosung zu immunisieren.