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Predicting Music Revenue: A hierarch...

Whitworth, Alex Phillip.

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Predicting Music Revenue: A hierarchical linear modeling approach with sensitivity analyses.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Predicting Music Revenue: A hierarchical linear modeling approach with sensitivity analyses./
Author:	Whitworth, Alex Phillip.
Description:	83 p.
Notes:	Source: Masters Abstracts International, Volume: 54-05.
Contained By:	Masters Abstracts International54-05(E).
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1590334
ISBN:	9781321795325

Predicting Music Revenue: A hierarchical linear modeling approach with sensitivity analyses.
Whitworth, Alex Phillip.

Predicting Music Revenue: A hierarchical linear modeling approach with sensitivity analyses. - 83 p.

Source: Masters Abstracts International, Volume: 54-05.

Thesis (M.S.)--University of California, Los Angeles, 2015.

The music industry has undergone enormous change since the introduction of of Napster in 1999. In 1999, 100% of industry revenue was from physical sales; in 2014, United States music industry revenue was 32% physical, 37% digital downloads, 27% streaming, and 4\% other minor categories. In this thesis, I present the first models in the music industry that predict monthly revenue at the album level across both revenue stream and geography within the music industry, which are based on a hierarhical linear modeling framework. In addition to the predictive models, I present several sensitivity analyses to examine interesting properties of the data. Specifically, the sensitivity analyses address the effects of data missingness, design imbalance, and the impact of outliers on the predictive results.

ISBN: 9781321795325Subjects--Topical Terms:

517247
Statistics.

Predicting Music Revenue: A hierarchical linear modeling approach with sensitivity analyses.
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100 1 $a Whitworth, Alex Phillip. $3 3186215
245 1 0 $a Predicting Music Revenue: A hierarchical linear modeling approach with sensitivity analyses.
300 $a 83 p.
500 $a Source: Masters Abstracts International, Volume: 54-05.
500 $a Adviser: Ying Nian Wu.
502 $a Thesis (M.S.)--University of California, Los Angeles, 2015.
520 $a The music industry has undergone enormous change since the introduction of of Napster in 1999. In 1999, 100% of industry revenue was from physical sales; in 2014, United States music industry revenue was 32% physical, 37% digital downloads, 27% streaming, and 4\% other minor categories. In this thesis, I present the first models in the music industry that predict monthly revenue at the album level across both revenue stream and geography within the music industry, which are based on a hierarhical linear modeling framework. In addition to the predictive models, I present several sensitivity analyses to examine interesting properties of the data. Specifically, the sensitivity analyses address the effects of data missingness, design imbalance, and the impact of outliers on the predictive results.
590 $a School code: 0031.
650 4 $a Statistics. $3 517247
650 4 $a Arts management. $3 3168382
650 4 $a Music. $3 516178
690 $a 0463
690 $a 0424
690 $a 0413
710 2 $a University of California, Los Angeles. $b Statistics 0891. $3 2095317
773 0 $t Masters Abstracts International $g 54-05(E).
790 $a 0031
791 $a M.S.
792 $a 2015
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1590334