東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Quantile regression for cross-sectio...

Uribe, Jorge M.

FindBook

Google Book

Amazon

博客來

Quantile regression for cross-sectional and time series data = applications in energy markets using R /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Quantile regression for cross-sectional and time series data/ by Jorge M. Uribe, Montserrat Guillen.
其他題名:	applications in energy markets using R /
作者:	Uribe, Jorge M.
其他作者:	Guillen, Montserrat.
出版者:	Cham :Springer International Publishing : : 2020.,
面頁冊數:	x, 63 p. :ill., digital ;24 cm.
內容註:	Why and When Should Quantile Regression Be Used?- A Case of Study: Modelling Energy Markets by the Means of Quantile Regression -- Quantile Regression: A Methodological Overview -- Cross-Sectional Quantile Regression -- Time Series Quantile Regression -- Goodness of Fit in Quantile Regression Models -- Novel Approaches in Quantile Regression -- What Have We Learned from Quantile Regression? Implications for Economics and Finance -- Appendix: Programs for Quantile Regression and Implementation in R.
Contained By:	Springer eBooks
標題:	Quantile regression. -
電子資源:	https://doi.org/10.1007/978-3-030-44504-1
ISBN:	9783030445041

Quantile regression for cross-sectional and time series data = applications in energy markets using R /
Uribe, Jorge M.

Quantile regression for cross-sectional and time series dataapplications in energy markets using R /[electronic resource] :by Jorge M. Uribe, Montserrat Guillen. - Cham :Springer International Publishing :2020. - x, 63 p. :ill., digital ;24 cm. - SpringerBriefs in finance,2193-1720. - SpringerBriefs in finance..

Why and When Should Quantile Regression Be Used?- A Case of Study: Modelling Energy Markets by the Means of Quantile Regression -- Quantile Regression: A Methodological Overview -- Cross-Sectional Quantile Regression -- Time Series Quantile Regression -- Goodness of Fit in Quantile Regression Models -- Novel Approaches in Quantile Regression -- What Have We Learned from Quantile Regression? Implications for Economics and Finance -- Appendix: Programs for Quantile Regression and Implementation in R.

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.

ISBN: 9783030445041

Standard No.: 10.1007/978-3-030-44504-1doiSubjects--Topical Terms:

2148155
Quantile regression.

LC Class. No.: QA278.2 / .U753 2020

Dewey Class. No.: 519.536

Quantile regression for cross-sectional and time series data = applications in energy markets using R /
LDR:02465nmm a2200337 a 4500 001 2216974
003 DE-He213
005 20200730113650.0
006 m d
007 cr nn 008maaau
008 201120s2020 sz s 0 eng d
020 $a 9783030445041 $q (electronic bk.)
020 $a 9783030445034 $q (paper)
024 7 $a 10.1007/978-3-030-44504-1 $2 doi
035 $a 978-3-030-44504-1
040 $a GP $c GP
041 0 $a eng
050 4 $a QA278.2 $b .U753 2020
072 7 $a KCH $2 bicssc
072 7 $a BUS021000 $2 bisacsh
072 7 $a KCH $2 thema
082 0 4 $a 519.536 $2 23
090 $a QA278.2 $b .U76 2020
100 1 $a Uribe, Jorge M. $3 3449818
245 1 0 $a Quantile regression for cross-sectional and time series data $h [electronic resource] : $b applications in energy markets using R / $c by Jorge M. Uribe, Montserrat Guillen.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a x, 63 p. : $b ill., digital ; $c 24 cm.
490 1 $a SpringerBriefs in finance, $x 2193-1720
505 0 $a Why and When Should Quantile Regression Be Used?- A Case of Study: Modelling Energy Markets by the Means of Quantile Regression -- Quantile Regression: A Methodological Overview -- Cross-Sectional Quantile Regression -- Time Series Quantile Regression -- Goodness of Fit in Quantile Regression Models -- Novel Approaches in Quantile Regression -- What Have We Learned from Quantile Regression? Implications for Economics and Finance -- Appendix: Programs for Quantile Regression and Implementation in R.
520 $a 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.
650 0 $a Quantile regression. $3 2148155
650 0 $a Time-series analysis. $3 532530
650 0 $a R (Computer program language) $3 784593
650 1 4 $a Econometrics. $3 542934
650 2 4 $a Quantitative Finance. $3 891090
650 2 4 $a Statistics for Business, Management, Economics, Finance, Insurance. $3 3382132
650 2 4 $a Statistics and Computing/Statistics Programs. $3 894293
700 1 $a Guillen, Montserrat. $3 3449819
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a SpringerBriefs in finance. $3 1568901
856 4 0 $u https://doi.org/10.1007/978-3-030-44504-1
950 $a Economics and Finance (Springer-41170)