Estimation of Higher-order Two-phase Regression Models PDF Download
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Author: Hyunju Son
Publisher:
ISBN:
Category :
Languages : en
Pages : 42
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Book Description
Two-phase regression models are a class of nonlinear regression models that are known for their flexibility and interpretability. An important feature of two-phase regression models is the existence of a threshold at which the relationship between an outcome and a covariate of interest changes. A standard estimation method, such as that used for generalized linear models, cannot be applied to two-phase regression models since the likelihood function is not differentiable with respect to the threshold parameter. We resolve this difficulty by using a grid search method which reduces the problem to a set of well-behaved likelihood functions for given candidate threshold values. Previously, a fast grid search algorithm that dramatically improved computational efficiency over a brute-force grid search was developed for two-phase regression models with linear trends. Here we generalize this algorithm to higher-order two-phase regression models where two separate polynomial regressions, not limited to linear, are used to model each phase (i.e., before and after the threshold). Based on the proposed fast grid search algorithm, we perform Monte Carlo simulations to examine the behavior of the parameter estimates. A real data example is also presented to illustrate the practical use of two-phase regression models.
Author: Hyunju Son
Publisher:
ISBN:
Category :
Languages : en
Pages : 42
Get Book Here
Book Description
Two-phase regression models are a class of nonlinear regression models that are known for their flexibility and interpretability. An important feature of two-phase regression models is the existence of a threshold at which the relationship between an outcome and a covariate of interest changes. A standard estimation method, such as that used for generalized linear models, cannot be applied to two-phase regression models since the likelihood function is not differentiable with respect to the threshold parameter. We resolve this difficulty by using a grid search method which reduces the problem to a set of well-behaved likelihood functions for given candidate threshold values. Previously, a fast grid search algorithm that dramatically improved computational efficiency over a brute-force grid search was developed for two-phase regression models with linear trends. Here we generalize this algorithm to higher-order two-phase regression models where two separate polynomial regressions, not limited to linear, are used to model each phase (i.e., before and after the threshold). Based on the proposed fast grid search algorithm, we perform Monte Carlo simulations to examine the behavior of the parameter estimates. A real data example is also presented to illustrate the practical use of two-phase regression models.
Author:
Publisher:
ISBN:
Category : Forest biomass
Languages : en
Pages : 316
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Book Description
Proceedings of a workshop co-sponsored by the USDA Forest Service, the State University of New York, and the Society of American Foresters. Presented were papers on the methodology of sample tree selection, tree biomass measurement, construction of biomass tables and estimation of their error, and combining the error of biomass tables with that of the sample plots or points. Also presented were papers on various aspects of biomass research currently being conducted in the United States, Canada, and abroad.
Author: A. John Bailer
Publisher: Routledge
ISBN: 1351414143
Category : Mathematics
Languages : en
Pages : 596
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Book Description
Statistics for Environmental Biology and Toxicology presents and illustrates statistical methods appropriate for the analysis of environmental data obtained in biological or toxicological experiments. Beginning with basic probability and statistical inferences, this text progresses through non-linear and generalized linear models, trend testing, time-to-event data and analysis of cross-classified tabular and categorical data. For the more complex analyses, extensive examples including SAS and S-PLUS programming code are provided to assist the reader when implementing the methods in practice.
Author: John Fox
Publisher: SAGE Publications
ISBN: 1483321312
Category : Social Science
Languages : en
Pages : 612
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Book Description
Combining a modern, data-analytic perspective with a focus on applications in the social sciences, the Third Edition of Applied Regression Analysis and Generalized Linear Models provides in-depth coverage of regression analysis, generalized linear models, and closely related methods, such as bootstrapping and missing data. Updated throughout, this Third Edition includes new chapters on mixed-effects models for hierarchical and longitudinal data. Although the text is largely accessible to readers with a modest background in statistics and mathematics, author John Fox also presents more advanced material in optional sections and chapters throughout the book. Accompanying website resources containing all answers to the end-of-chapter exercises. Answers to odd-numbered questions, as well as datasets and other student resources are available on the author′s website. NEW! Bonus chapter on Bayesian Estimation of Regression Models also available at the author′s website.
Author: Richard Breen
Publisher: SAGE
ISBN: 9780803957107
Category : Mathematics
Languages : en
Pages : 92
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Book Description
This book provides an introduction to the regression models needed, where an outcome variable for a sample is not representative of the population from which a generalized result is sought.
Author: R. Carter Hill
Publisher: John Wiley & Sons
ISBN: 1118452275
Category : Business & Economics
Languages : en
Pages : 1808
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Book Description
Principles of Econometrics, Fifth Edition, is an introductory book for undergraduate students in economics and finance, as well as first-year graduate students in a variety of fields that include economics, finance, accounting, marketing, public policy, sociology, law, and political science. Students will gain a working knowledge of basic econometrics so they can apply modeling, estimation, inference, and forecasting techniques when working with real-world economic problems. Readers will also gain an understanding of econometrics that allows them to critically evaluate the results of others’ economic research and modeling, and that will serve as a foundation for further study of the field. This new edition of the highly-regarded econometrics text includes major revisions that both reorganize the content and present students with plentiful opportunities to practice what they have read in the form of chapter-end exercises.
Author: Richard J. Brook
Publisher: Routledge
ISBN: 1351465880
Category : Mathematics
Languages : en
Pages : 148
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Book Description
For a solid foundation of important statistical methods, the concise, single-source text unites linear regression with analysis of experiments and provides students with the practical understanding needed to apply theory in real data analysis problems. Stressing principles while keeping computational and theoretical details at a manageable level, Applied Regression Analysis and Experimental Design features an emphasis on vector geometry and least squares to unify and provide an intuitive basis for most topics covered... abundant examples and exercises using real-life data sets clearly illustrating practical of data analysis...essential exposure to MINITAB and GENSTAT computer packages , including computer printouts...and important background material such as vector and matrix properties and the distributional properties of quadratic forms. Designed to make theory work for students, this clearly written, easy-to-understand work serves as the ideal texts for courses Regression, Experimental Design, and Linear Models in a broad range of disciplines. Moreover, applied statisticians will find the book a useful reference for the general application of the linear model.
Author: Chang-Jin Kim
Publisher: Now Publishers Inc
ISBN: 1601983123
Category : Business & Economics
Languages : en
Pages : 116
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Book Description
The purpose of this monograph is to present a unified econometric framework for dealing with the issues of endogeneity in Markov-switching models and time-varying parameter models, as developed by Kim (2004, 2006, 2009), Kim and Nelson (2006), Kim et al. (2008), and Kim and Kim (2009). While Cogley and Sargent (2002), Primiceri (2005), Sims and Zha (2006), and Sims et al. (2008) consider estimation of simultaneous equations models with stochastic coefficients as a system, we deal with the LIML (limited information maximum likelihood) estimation of a single equation of interest out of a simultaneous equations model. Our main focus is on the two-step estimation procedures based on the control function approach, and we show how the problem of generated regressors can be addressed in second-step regressions.
Author: J.R. Raol
Publisher: IET
ISBN: 0863413633
Category : Mathematics
Languages : en
Pages : 405
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Book Description
This book presents a detailed examination of the estimation techniques and modeling problems. The theory is furnished with several illustrations and computer programs to promote better understanding of system modeling and parameter estimation.
Author: Andrew C. Harvey
Publisher: Cambridge University Press
ISBN: 1107717140
Category : Business & Economics
Languages : en
Pages : 578
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Book Description
In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.
