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Quantile regressionapplications on experimental and cross section data using EViews /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Quantile regressionI. Gusti Ngurah Agung.
其他題名:	applications on experimental and cross section data using EViews /
作者:	Agung, I Gusti Ngurah.
出版者:	Hoboken, NJ :John Wiley & Sons,2021.
面頁冊數:	1 online resource (499 p.)
標題:	Quantile regression.
電子資源:	https://onlinelibrary.wiley.com/doi/book/10.1002/9781119714958
ISBN:	9781119714958$q(electronic bk.)

Quantile regressionapplications on experimental and cross section data using EViews /
Agung, I Gusti Ngurah.

Quantile regressionapplications on experimental and cross section data using EViews /[electronic resource] :I. Gusti Ngurah Agung. - 1st ed. - Hoboken, NJ :John Wiley & Sons,2021. - 1 online resource (499 p.)

Includes bibliographical references and index.

"Quantile regression aims at estimating either the conditional median or other quantiles of the response variable. Essentially, quantile regression is the extension of linear regression and we use it when the conditions of linear regression are not applicable. LS-Regressions, Ordinary-Regressions or Mean-Regressions, the Quantile-Regressions (QRs) can be classified into three groups. The first group consists of the QRs with categorical variables, caller ANOVA QRs, where ordinal variables are treated as nominal variables and the numerical independent variables (IVs) are transformed to ordinal variables. The second group consists of the QRs with numerical variables, where the ordinal variables are treated as the numerical IVs. The third group consists of the various interaction QRs with numerical and categorical IV, where the ordinal variables can be treated as either numerical or nominal categorical IVs. Applications of Quantile Regression of Experimental and Cross Section Data using EViews presents examples of statistical results of various QRs in order to display their richer characteristics, based on the LS-Regression, Ordinary-Regressions, or Mean-Regressions. It offers instructions how to develop the best possible QRs and how to present more advanced analysis by using the Quantile Process, the Wald test, the Redundant Variables test, Omitted Variables Test, and forecasting, as well as to draw the best conclusions from results. A mathematical knowledge of quantile regression is not necessary so this book is applicable to students and lecturers in statistics, data analysis and engineering"--

ISBN: 9781119714958$q(electronic bk.)Subjects--Uniform Titles:

EViews (Computer file)
Subjects--Topical Terms:

709809
Quantile regression.

LC Class. No.: QA278.2 / .A386 2021

Dewey Class. No.: 519.536

Quantile regressionapplications on experimental and cross section data using EViews /
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520 $a "Quantile regression aims at estimating either the conditional median or other quantiles of the response variable. Essentially, quantile regression is the extension of linear regression and we use it when the conditions of linear regression are not applicable. LS-Regressions, Ordinary-Regressions or Mean-Regressions, the Quantile-Regressions (QRs) can be classified into three groups. The first group consists of the QRs with categorical variables, caller ANOVA QRs, where ordinal variables are treated as nominal variables and the numerical independent variables (IVs) are transformed to ordinal variables. The second group consists of the QRs with numerical variables, where the ordinal variables are treated as the numerical IVs. The third group consists of the various interaction QRs with numerical and categorical IV, where the ordinal variables can be treated as either numerical or nominal categorical IVs. Applications of Quantile Regression of Experimental and Cross Section Data using EViews presents examples of statistical results of various QRs in order to display their richer characteristics, based on the LS-Regression, Ordinary-Regressions, or Mean-Regressions. It offers instructions how to develop the best possible QRs and how to present more advanced analysis by using the Quantile Process, the Wald test, the Redundant Variables test, Omitted Variables Test, and forecasting, as well as to draw the best conclusions from results. A mathematical knowledge of quantile regression is not necessary so this book is applicable to students and lecturers in statistics, data analysis and engineering"-- $c Provided by publisher
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