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Quantile regression : applications on experimental and cross section data using EViews /

"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-Regressio...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Agung, I. Gusti Ngurah (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Hoboken : John Wiley & Sons, Inc., 2021.
Temas:
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)

MARC

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100 1 |a Agung, I. Gusti Ngurah,  |e author. 
245 1 0 |a Quantile regression :  |b applications on experimental and cross section data using EViews /  |c I Gusti Ngurah Agung. 
264 1 |a Hoboken :  |b John Wiley & Sons, Inc.,  |c 2021. 
264 4 |c ©2021 
300 |a 1 online resource (xviii, 473 pages) :  |b illustrations (some color) 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
504 |a Includes bibliographical references and index. 
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 
588 0 |a Online resource; title from digital title page (viewed on September 14, 2021). 
590 |a O'Reilly  |b O'Reilly Online Learning: Academic/Public Library Edition 
630 0 0 |a EViews (Computer file) 
630 0 7 |a EViews (Computer file)  |2 fast  |0 (OCoLC)fst01781707 
650 0 |a Quantile regression. 
650 0 |a Mathematical statistics. 
650 6 |a Régression quantile. 
650 7 |a Mathematical statistics  |2 fast  |0 (OCoLC)fst01012127 
650 7 |a Quantile regression  |2 fast  |0 (OCoLC)fst01896520 
776 0 8 |i Print version:  |a Agung, I Gusti Ngurah.  |t Quantile regression.  |b First edition.  |d Hoboken : Wiley, [2020]  |z 9781119715177  |w (DLC) 2020025365 
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