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Activity Number: 465 - SPEED: Statistical Computing: Methods, Implementation, and Application, Part 1
Type: Contributed
Date/Time: Wednesday, July 31, 2019 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Computing
Abstract #306586
Title: Spatial Location-Based Trajectory Modeling: Predicting the Success of an Crowdfunding Campaign
Author(s): Han Yu*
Companies: University of Northern Colorado
Keywords: Joint models; INLA; Gaussian Markov Random Fields; Crowdfunding

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.

Authors who are presenting talks have a * after their name.

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