TY  - JOUR
T1  - A Statistical Method for Big Data with Excessive Zero-Inflated Problem
AU - Jun, Sunghae 
JO  - Journal of Engineering and Applied Sciences
VL  - 14
IS  - 8
SP  - 2465
EP  - 2469
PY  - 2019
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2019.2465.2469
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2019.2465.2469
KW  - Statistical model
KW  -big data
KW  -zero-inflated problem
KW  -count data analysis
KW  -patent big data analysis
KW  -validity
KW  -statistical modeling
KW  -data analysis
AB  - In many cases, we meet the zero-inflated problem in big data analysis. This is because the value of
zero is too much in the data table structured through preprocessing from collected big data. If the big data is
analyzed as it is the performances of estimation and prediction of statistical models will deteriorate. To build
valid models for big data analysis, we have to solve the zero-inflated problem of big data. So, we propose a
statistical modeling to overcome the zero-inflated problem in big data analysis. In this study, we combine the
method of data division with count data models such as Poisson, hurdle, negative binomial regressions. In
order to verify the validity of the proposed approach, we carry out case study using simulated and patent big
data.
ER  -