Dynamic Semi-parametric Factor Model for Functional Expectiles
Author: Petra Burdejová
Publisher:
Published: 2017
Total Pages:
ISBN-13:
DOWNLOAD EBOOKRead and Download eBook Full
Author: Petra Burdejová
Publisher:
Published: 2017
Total Pages:
ISBN-13:
DOWNLOAD EBOOKAuthor: Siem Jan Koopman
Publisher: Emerald Group Publishing Limited
Published: 2016-01-08
Total Pages: 0
ISBN-13: 9781785603532
DOWNLOAD EBOOKThis volume explores dynamic factor model specification, asymptotic and finite-sample behavior of parameter estimators, identification, frequentist and Bayesian estimation of the corresponding state space models, and applications.
Author: Jushan Bai
Publisher: Now Publishers Inc
Published: 2008
Total Pages: 90
ISBN-13: 1601981449
DOWNLOAD EBOOKLarge Dimensional Factor Analysis provides a survey of the main theoretical results for large dimensional factor models, emphasizing results that have implications for empirical work. The authors focus on the development of the static factor models and on the use of estimated factors in subsequent estimation and inference. Large Dimensional Factor Analysis discusses how to determine the number of factors, how to conduct inference when estimated factors are used in regressions, how to assess the adequacy pf observed variables as proxies for latent factors, how to exploit the estimated factors to test unit root tests and common trends, and how to estimate panel cointegration models.
Author: George Kapetanios
Publisher:
Published: 2014
Total Pages: 0
ISBN-13:
DOWNLOAD EBOOKThe estimation of dynamic factor models for large sets of variables has attracted considerable attention recently, due to the increased availability of large datasets. In this paper we propose a new parametric methodology for estimating factors from large datasets based on state space models and discuss its theoretical properties. In particular, we show that it is possible to estimate consistently the factor space. We also develop a consistent information criterion for the determination of the number of factors to be included in the model. Finally, we conduct a set of simulation experiments that show that our approach compares well with existing alternatives.
Author: Wolfgang Karl Härdle
Publisher: Springer
Published: 2018-07-20
Total Pages: 532
ISBN-13: 3319182846
DOWNLOAD EBOOKAddressing a broad range of big data analytics in cross-disciplinary applications, this essential handbook focuses on the statistical prospects offered by recent developments in this field. To do so, it covers statistical methods for high-dimensional problems, algorithmic designs, computation tools, analysis flows and the software-hardware co-designs that are needed to support insightful discoveries from big data. The book is primarily intended for statisticians, computer experts, engineers and application developers interested in using big data analytics with statistics. Readers should have a solid background in statistics and computer science.
Author: Lajos Horváth
Publisher: Springer Science & Business Media
Published: 2012-05-08
Total Pages: 426
ISBN-13: 1461436559
DOWNLOAD EBOOKThis book presents recently developed statistical methods and theory required for the application of the tools of functional data analysis to problems arising in geosciences, finance, economics and biology. It is concerned with inference based on second order statistics, especially those related to the functional principal component analysis. While it covers inference for independent and identically distributed functional data, its distinguishing feature is an in depth coverage of dependent functional data structures, including functional time series and spatially indexed functions. Specific inferential problems studied include two sample inference, change point analysis, tests for dependence in data and model residuals and functional prediction. All procedures are described algorithmically, illustrated on simulated and real data sets, and supported by a complete asymptotic theory. The book can be read at two levels. Readers interested primarily in methodology will find detailed descriptions of the methods and examples of their application. Researchers interested also in mathematical foundations will find carefully developed theory. The organization of the chapters makes it easy for the reader to choose an appropriate focus. The book introduces the requisite, and frequently used, Hilbert space formalism in a systematic manner. This will be useful to graduate or advanced undergraduate students seeking a self-contained introduction to the subject. Advanced researchers will find novel asymptotic arguments.
Author: Elijah Gaioni
Publisher:
Published: 2009
Total Pages: 0
ISBN-13:
DOWNLOAD EBOOKAuthor: Jörg Breitung
Publisher:
Published: 2005
Total Pages: 29
ISBN-13: 9783865580979
DOWNLOAD EBOOKAuthor: Roger Koenker
Publisher: Cambridge University Press
Published: 2005-05-05
Total Pages: 367
ISBN-13: 1139444719
DOWNLOAD EBOOKQuantile regression is gradually emerging as a unified statistical methodology for estimating models of conditional quantile functions. By complementing the exclusive focus of classical least squares regression on the conditional mean, quantile regression offers a systematic strategy for examining how covariates influence the location, scale and shape of the entire response distribution. This monograph is the first comprehensive treatment of the subject, encompassing models that are linear and nonlinear, parametric and nonparametric. The author has devoted more than 25 years of research to this topic. The methods in the analysis are illustrated with a variety of applications from economics, biology, ecology and finance. The treatment will find its core audiences in econometrics, statistics, and applied mathematics in addition to the disciplines cited above.
Author: Trevor Hastie
Publisher: CRC Press
Published: 2015-05-07
Total Pages: 354
ISBN-13: 1498712177
DOWNLOAD EBOOKDiscover New Methods for Dealing with High-Dimensional DataA sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the underl