DocumentCode
3256859
Title
Quantile regression for workforce analytics
Author
Ramamurthy, K.N. ; Varshney, Kush R. ; Singh, Monika
Author_Institution
Bus. Analytics & Math. Sci. Dept., IBM Thomas J. Watson Res. Center, Yortktown Heights, NY, USA
fYear
2013
fDate
3-5 Dec. 2013
Firstpage
1134
Lastpage
1134
Abstract
Understanding the behavior of a constantly changing workforce is key to making business decisions in modern organizations. In this paper, we develop frameworks based on quantile regression to estimate the productivity and attrition profiles of employees from revenue, headcount, and incentive data. Results show the advantages of quantile-specific profiles compared to those obtained with other regression schemes.
Keywords
personnel; productivity; regression analysis; employee attrition profiles; employee productivity estimation; headcount data; incentive data; quantile regression; revenue data; workforce analytics; Customer satisfaction; Data models; Indexes; Linear regression; Organizations; Productivity; attrition profile; productivity profile; quantile regression; workforce behavior;
fLanguage
English
Publisher
ieee
Conference_Titel
Global Conference on Signal and Information Processing (GlobalSIP), 2013 IEEE
Conference_Location
Austin, TX
Type
conf
DOI
10.1109/GlobalSIP.2013.6737097
Filename
6737097
Link To Document