Activity Number:
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531
- SPEED: Statistical Computing: Methods, Implementation, and Application, Part 2
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Type:
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Contributed
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Date/Time:
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Wednesday, July 31, 2019 : 11:35 AM to 12:20 PM
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Sponsor:
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Section on Statistical Computing
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Abstract #307950
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Title:
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Spatial Location-Based Trajectory Modeling: Predicting the Success of an Crowdfunding Campaign
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Author(s):
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Han Yu*
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Companies:
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University of Northern Colorado
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Keywords:
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Joint models;
INLA;
Gaussian Markov Random Fields;
Crowdfunding
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Abstract:
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Crowdfunding has emerged as a major trend in financing various entrepreneurs including startup capital. The wide spread daily use of the internet in most parts of people’s lives has led to crowdfunding being a primarily online endeavor. Many online platforms offering different services are used for crowdfunding. Though there are different platforms, Kickstarter is currently the most popular and makes for a good representation of the online crowdfunding phenomenon. What makes for a truly successful drive to obtain donors is unknown to everybody who uses a crowdfunding service. For understanding the behavioral performance of an online crowdfunding campaign, a probabilistic representation of spatial location-based developmental trajectories for the backer count and the Dollar pledged was developed with the large, complex, and heterogeneous spatio-temporal crowdfunding data of over ninety-nine thousand observations to elaborate the effects of the spatio-temporal components on a crowdfunding campaign on the Kickstarter as well as the effects of key factors of interest on the prediction of successful Kickstarter campaigns using a joint modeling strategy.
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Authors who are presenting talks have a * after their name.
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