05021nam 22006495 450 991043813880332120250505002859.01-4614-7789-110.1007/978-1-4614-7789-1(CKB)2670000000530503(EBL)1398475(OCoLC)858763670(SSID)ssj0000988287(PQKBManifestationID)11627772(PQKBTitleCode)TC0000988287(PQKBWorkID)10950212(PQKB)11619449(DE-He213)978-1-4614-7789-1(MiAaPQ)EBC1398475(PPN)17241993X(EXLCZ)99267000000053050320130813d2013 u| 0engur|n|---|||||txtccrState-Space Models Applications in Economics and Finance /edited by Yong Zeng, Shu Wu1st ed. 2013.New York, NY :Springer New York :Imprint: Springer,2013.1 online resource (358 p.)Statistics and Econometrics for Finance,2199-0948 ;1Description based upon print version of record.1-4614-7788-3 1-4899-9253-7 Includes bibliographical references and index.Particle Filtering and Parameter Learning in Nonlinear State-Space Models -- Linear State-Space Models in Macroeconomics and Finance -- Hidden Markov Models, Regime-Switching, and Mathematical Finance -- Nonlinear State-Space Models for High Frequency Financial Data -- Index.State-space models as an important mathematical tool has been widely used in many different fields. This edited collection explores recent theoretical developments of the models and their applications in economics and finance. The book includes nonlinear and non-Gaussian time series models, regime-switching and hidden Markov models, continuous- or discrete-time state processes, and models of equally-spaced or irregularly-spaced (discrete or continuous) observations. The contributed chapters are divided into four parts. The first part is on Particle Filtering and Parameter Learning in Nonlinear State-Space Models. The second part focuses on the application of Linear State-Space Models in Macroeconomics and Finance. The third part deals with Hidden Markov Models, Regime Switching and Mathematical Finance and the fourth part is on Nonlinear State-Space Models for High Frequency Financial Data.  The book will appeal to graduate students and researchers studying state-space modeling in economics, statistics, and mathematics, as well as to finance professionals. Yong Zeng is a professor in Department of Mathematics and Statistics at University of Missouri at Kansas City. His main research interest includes mathematical finance, financial econometrics, stochastic nonlinear filtering, and Bayesian statistical analysis. Notably, he developed the statistical analysis via filtering for financial ultra-high frequency data, where the model can be viewed as a random-arrival-time state space model. He has published in Mathematical Finance, International Journal of Theoretical and Applied Finance, Applied Mathematical Finance, IEEE Transactions on Automatic Control, Statistical Inference for Stochastic Processes, among others. He held visiting associate professor positions at Princeton University and the University of Tennessee.  He received his B.S. from Fudan University in 1990, M.S. from University of Georgia in 1994, and Ph.D. fromUniversity of Wisconsin at Madison in 1999. All degrees were in statistics. Shu Wu is an associate professor in Department of Economics at University of Kansas. His main research areas are empirical macroeconomics and finance. He has held visiting positions at Federal Reserve Bank at Kansas City, City University of Hong Kong. His publications have appeared in Journal of Monetary Economics, Journal of Money, Credit and Banking, Macroeconomic Dynamics, International Journal of Theoretical and Applied Finance, Journal of International Financial Markets, Institutions and Money, Handbook of Quantitative Finance and Risk Management, Hidden Markov Models in Finance among others. He received his Ph.D. in economics from Stanford University in 2000.Statistics and Econometrics for Finance,2199-0948 ;1StatisticsStatisticsStatistics in Business, Management, Economics, Finance, InsuranceStatistical Theory and MethodsStatistics.Statistics.Statistics in Business, Management, Economics, Finance, Insurance.Statistical Theory and Methods.511.8Zeng Yong1309854Wu Shu1058720MiAaPQMiAaPQMiAaPQBOOK9910438138803321State-space models4187850UNINA05332nam 22008293 450 991054827750332120250628110046.03-030-67024-4(CKB)5590000000896787(MiAaPQ)EBC6893332(Au-PeEL)EBL6893332(oapen)https://directory.doabooks.org/handle/20.500.12854/79344(PPN)260826111(ODN)ODN0010171413(oapen)doab79344(EXLCZ)99559000000089678720220321d2022 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierMetalearning Applications to Automated Machine Learning and Data Mining2nd ed.ChamSpringer Nature2022Cham :Springer International Publishing AG,2022.©2022.1 online resource (349 pages)Cognitive Technologies3-030-67023-6 This open access book as one of the fastest-growing areas of research in machine learning, metalearning studies principled methods to obtain efficient models and solutions by adapting machine learning and data mining processes. This adaptation usually exploits information from past experience on other tasks and the adaptive processes can involve machine learning approaches. As a related area to metalearning and a hot topic currently, automated machine learning (AutoML) is concerned with automating the machine learning processes. Metalearning and AutoML can help AI learn to control the application of different learning methods and acquire new solutions faster without unnecessary interventions from the user. This book offers a comprehensive and thorough introduction to almost all aspects of metalearning and AutoML, covering the basic concepts and architecture, evaluation, datasets, hyperparameter optimization, ensembles and workflows, and also how this knowledge can be used to select, combine, compose, adapt and configure both algorithms and models to yield faster and better solutions to data mining and data science problems. It can thus help developers to develop systems that can improve themselves through experience. This book is a substantial update of the first edition published in 2009. It includes 18 chapters, more than twice as much as the previous version. This enabled the authors to cover the most relevant topics in more depth and incorporate the overview of recent research in the respective area. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining, data science and artificial intelligence. ; Metalearning is the study of principled methods that exploit metaknowledge to obtain efficient models and solutions by adapting machine learning and data mining processes. While the variety of machine learning and data mining techniques now available can, in principle, provide good model solutions, a methodology is still needed to guide the search for the most appropriate model in an efficient way. Metalearning provides one such methodology that allows systems to become more effective through experience. This book discusses several approaches to obtaining knowledge concerning the performance of machine learning and data mining algorithms. It shows how this knowledge can be reused to select, combine, compose and adapt both algorithms and models to yield faster, more effective solutions to data mining problems. It can thus help developers improve their algorithms and also develop learning systems that can improve themselves. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining and artificial intelligence.Cognitive TechnologiesArtificial intelligencebicsscData miningbicsscMachine learningbicsscAprenentatge automàticthubMineria de dadesthubLlibres electrònicsthubMetalearningAutomating Machine Learning (AutoML)Machine LearningArtificial Intelligencealgorithm selectionalgorithm recommendationalgorithm configurationhyperparameter optimizationautomating the workflow/pipeline designmetalearning in ensemble constructionmetalearning in deep neural networkstransfer learningalgorithm recommendation for data streamsautomating data scienceOpen AccessArtificial intelligenceData miningMachine learningAprenentatge automàtic.Mineria de dades.006.31006.31COM004000COM021030bisacshBrazdil Pavel1214572van Rijn Jan N1214573Soares Carlos961096Vanschoren Joaquin1214574MiAaPQMiAaPQMiAaPQBOOK9910548277503321Metalearning2804517UNINA