Quantile Regression in Heteroscedastic Varying Coefficient Models PDF Download
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Author: Mohammed Abdulkerim Ibrahim
Publisher:
ISBN:
Category :
Languages : en
Pages : 148
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Author: Mohammed Abdulkerim Ibrahim
Publisher:
ISBN:
Category :
Languages : en
Pages : 148
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Author: Mi-Ok Kim
Publisher:
ISBN:
Category :
Languages : en
Pages : 166
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Author: Marilena Furno
Publisher: John Wiley & Sons
ISBN: 111886364X
Category : Mathematics
Languages : en
Pages : 311
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Book Description
Contains an overview of several technical topics of Quantile Regression Volume two of Quantile Regression offers an important guide for applied researchers that draws on the same example-based approach adopted for the first volume. The text explores topics including robustness, expectiles, m-quantile, decomposition, time series, elemental sets and linear programming. Graphical representations are widely used to visually introduce several issues, and to illustrate each method. All the topics are treated theoretically and using real data examples. Designed as a practical resource, the book is thorough without getting too technical about the statistical background. The authors cover a wide range of QR models useful in several fields. The software commands in R and Stata are available in the appendixes and featured on the accompanying website. The text: Provides an overview of several technical topics such as robustness of quantile regressions, bootstrap and elemental sets, treatment effect estimators Compares quantile regression with alternative estimators like expectiles, M-estimators and M-quantiles Offers a general introduction to linear programming focusing on the simplex method as solving method for the quantile regression problem Considers time-series issues like non-stationarity, spurious regressions, cointegration, conditional heteroskedasticity via quantile regression Offers an analysis that is both theoretically and practical Presents real data examples and graphical representations to explain the technical issues Written for researchers and students in the fields of statistics, economics, econometrics, social and environmental science, this text offers guide to the theory and application of quantile regression models.
Author: Jorge M. Uribe
Publisher: Springer Nature
ISBN: 3030445046
Category : Business & Economics
Languages : en
Pages : 63
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Book Description
This brief addresses the estimation of quantile regression models from a practical perspective, which will support researchers who need to use conditional quantile regression to measure economic relationships among a set of variables. It will also benefit students using the methodology for the first time, and practitioners at private or public organizations who are interested in modeling different fragments of the conditional distribution of a given variable. The book pursues a practical approach with reference to energy markets, helping readers learn the main features of the technique more quickly. Emphasis is placed on the implementation details and the correct interpretation of the quantile regression coefficients rather than on the technicalities of the method, unlike the approach used in the majority of the literature. All applications are illustrated with R.
Author: Marie Davidian
Publisher: Routledge
ISBN: 1351428152
Category : Mathematics
Languages : en
Pages : 360
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Book Description
Nonlinear measurement data arise in a wide variety of biological and biomedical applications, such as longitudinal clinical trials, studies of drug kinetics and growth, and the analysis of assay and laboratory data. Nonlinear Models for Repeated Measurement Data provides the first unified development of methods and models for data of this type, with a detailed treatment of inference for the nonlinear mixed effects and its extensions. A particular strength of the book is the inclusion of several detailed case studies from the areas of population pharmacokinetics and pharmacodynamics, immunoassay and bioassay development and the analysis of growth curves.
Author: Lingxin Hao
Publisher: SAGE Publications
ISBN: 1483316904
Category : Social Science
Languages : en
Pages : 142
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Book Description
Quantile Regression, the first book of Hao and Naiman′s two-book series, establishes the seldom recognized link between inequality studies and quantile regression models. Though separate methodological literature exists for each subject, the authors seek to explore the natural connections between this increasingly sought-after tool and research topics in the social sciences. Quantile regression as a method does not rely on assumptions as restrictive as those for the classical linear regression; though more traditional models such as least squares linear regression are more widely utilized, Hao and Naiman show, in their application of quantile regression to empirical research, how this model yields a more complete understanding of inequality. Inequality is a perennial concern in the social sciences, and recently there has been much research in health inequality as well. Major software packages have also gradually implemented quantile regression. Quantile Regression will be of interest not only to the traditional social science market but other markets such as the health and public health related disciplines. Key Features: Establishes a natural link between quantile regression and inequality studies in the social sciences Contains clearly defined terms, simplified empirical equations, illustrative graphs, empirical tables and graphs from examples Includes computational codes using statistical software popular among social scientists Oriented to empirical research
Author: Zongwu Caia
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: Marilena Furno
Publisher: John Wiley & Sons
ISBN: 1118863593
Category : Mathematics
Languages : en
Pages : 307
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Book Description
Contains an overview of several technical topics of Quantile Regression Volume two of Quantile Regression offers an important guide for applied researchers that draws on the same example-based approach adopted for the first volume. The text explores topics including robustness, expectiles, m-quantile, decomposition, time series, elemental sets and linear programming. Graphical representations are widely used to visually introduce several issues, and to illustrate each method. All the topics are treated theoretically and using real data examples. Designed as a practical resource, the book is thorough without getting too technical about the statistical background. The authors cover a wide range of QR models useful in several fields. The software commands in R and Stata are available in the appendixes and featured on the accompanying website. The text: Provides an overview of several technical topics such as robustness of quantile regressions, bootstrap and elemental sets, treatment effect estimators Compares quantile regression with alternative estimators like expectiles, M-estimators and M-quantiles Offers a general introduction to linear programming focusing on the simplex method as solving method for the quantile regression problem Considers time-series issues like non-stationarity, spurious regressions, cointegration, conditional heteroskedasticity via quantile regression Offers an analysis that is both theoretically and practical Presents real data examples and graphical representations to explain the technical issues Written for researchers and students in the fields of statistics, economics, econometrics, social and environmental science, this text offers guide to the theory and application of quantile regression models.
Author: Cristina Davino
Publisher: John Wiley & Sons
ISBN: 111997528X
Category : Mathematics
Languages : en
Pages : 288
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Book Description
A guide to the implementation and interpretation of Quantile Regression models This book explores the theory and numerous applications of quantile regression, offering empirical data analysis as well as the software tools to implement the methods. The main focus of this book is to provide the reader with a comprehensive description of the main issues concerning quantile regression; these include basic modeling, geometrical interpretation, estimation and inference for quantile regression, as well as issues on validity of the model, diagnostic tools. Each methodological aspect is explored and followed by applications using real data. Quantile Regression: Presents a complete treatment of quantile regression methods, including, estimation, inference issues and application of methods. Delivers a balance between methodolgy and application Offers an overview of the recent developments in the quantile regression framework and why to use quantile regression in a variety of areas such as economics, finance and computing. Features a supporting website (www.wiley.com/go/quantile_regression) hosting datasets along with R, Stata and SAS software code. Researchers and PhD students in the field of statistics, economics, econometrics, social and environmental science and chemistry will benefit from this book.
Author: Zhanxiong Xu
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
The quantile regression method, first introduced by \citet{koenker1978regression}, provides a comprehensive toolkit of performing statistical inference for a class of statistical models and has become an important surrogate for the conventional least squares method. Specifically, quantile regression offers several versatile approaches to produce highly efficient estimates, regardless whether the error distribution is homoscedastic or not.This dissertation is concerned with developing some efficient estimation methods for both the regression parameter and the dispersion parameter under the parametric nonlinear heteroscedastic model. The proposed methods have their roots in quantile regression and rely heavily on large-sample properties of the estimates. In Chapter 2, we estimate the parameters by solving the ``double-weighted composite quantile regression (DWCQR)'' optimization problem. We establish central limit theorems for both estimates, based on which we recommend an objective way of choosing the optimal weights for both the quantile losses and the heteroscedasticity. It is shown by theoretical calculation that the resulting estimates are typically more efficient than those obtained from other methods, and their asymptotic variances converge to the Cram\'{e}r-Rao lower bounds as the number of quantile positions tends to infinity. An adaptive estimation procedure is reported at the end of this chapter.The computational aspects of the DWCQR problem are discussed in Chapter 3. Although the DWCQR problem, in general, does not admit numerical solutions that are guaranteed to converge, we attempted to provide an algorithm that combines the MM algorithm (\citet{hunter2000quantile}) and the linear programming. The proposed MMLP algorithm overall works well and successfully confirms the nice theoretical properties of the DWCQR estimates using the optimal weights. The Monte Carlo study demonstrates that the DWCQR method outperforms the conventional estimation methods for the models under investigation. In Chapter 4, for simplicity, we restrict the regression function to be linear and consider an alternative efficient estimation approach, which is based on a preliminary estimate $\hat{\alpha}_n$ of the dispersion parameter. We first derive the Bahadur representation of the regression quantile $\hat{\beta}(\tau)$ for fixed $\tau$. It is then interesting to note that the effect of the $\hat{\alpha}_n$ propagates in the asymptotic representation of $\hat{\beta}(\tau)$. Such asymptotic bias brought by $\hat{\alpha}_n$ can be eliminated by averaging regression quantiles across different quantile positions with a set of carefully chosen weights. In the meantime, it can be shown that these weights can besimultaneously adjusted so that the resulting estimate is also asymptotically efficient. The chapter is concluded by Monte Carlo studies.
