TY - JOUR

T1 - Local linear negative binomial nonparametric regression for predicting the number of speed violations on toll road

T2 - A theoretical discussion

AU - Chamidah, Nur

AU - Widyanti, Ari

AU - Trapsilawati, Fitri

AU - Syafitri, Utami Dyah

N1 - Funding Information:
Authors wish to thank to Rector of Airlangga University for funding this research through the Indonesian Collaboration Research - World Class University (Riset Kolaborasi Indonesia/RKI - WCU) Grant in the fiscal year 2020 with a Contract No. 253/UN3.14/PT/2020.
Funding Information:
Authors wish to thank to Rector of Airlangga University for funding this research through the Indonesian Collaboration Research – World Class University (Riset Kolaborasi Indonesia/RKI – WCU) Grant in the fiscal year 2020 with a Contract No. 253/UN3.14/PT/2020.
Publisher Copyright:
© 2021 the author(s).

PY - 2021

Y1 - 2021

N2 - In this paper, we describe a theoretical discussion about local linear negative binomial regression for predicting the number of speed violations on toll road. Data on the number of speed violations on toll roads is a count data. Count data is a non-negative integer data generated from continuous calculation process. We usually use Poisson regression to analyze count data of a response variable. But, one of infractions on Poisson regression assumption is over-dispersion. To overcome that over-dispersion we should use negative binomial nonparametric regression model approach. The negative binomial nonparametric regression model is a development of the negative binomial parametric regression model. In this research, we theoretically discuss estimation of negative binomial nonparametric regression model based on local linear estimator which is applied to data of the number of speed violations on toll roads. The estimation results of the negative binomial nonparametric regression model that we have obtained then can be used to predict the number of speed violations on toll roads so that the Ministry of Transportation together with the police can use it to take preventive measures.

AB - In this paper, we describe a theoretical discussion about local linear negative binomial regression for predicting the number of speed violations on toll road. Data on the number of speed violations on toll roads is a count data. Count data is a non-negative integer data generated from continuous calculation process. We usually use Poisson regression to analyze count data of a response variable. But, one of infractions on Poisson regression assumption is over-dispersion. To overcome that over-dispersion we should use negative binomial nonparametric regression model approach. The negative binomial nonparametric regression model is a development of the negative binomial parametric regression model. In this research, we theoretically discuss estimation of negative binomial nonparametric regression model based on local linear estimator which is applied to data of the number of speed violations on toll roads. The estimation results of the negative binomial nonparametric regression model that we have obtained then can be used to predict the number of speed violations on toll roads so that the Ministry of Transportation together with the police can use it to take preventive measures.

KW - Local linear estimator

KW - Negative binomial regression

KW - Nonparametric regression

KW - The number of speed violations

UR - http://www.scopus.com/inward/record.url?scp=85101594277&partnerID=8YFLogxK

U2 - 10.28919/cmbn/5282

DO - 10.28919/cmbn/5282

M3 - Article

AN - SCOPUS:85101594277

SN - 2052-2541

VL - 2021

SP - 1

EP - 11

JO - Communications in Mathematical Biology and Neuroscience

JF - Communications in Mathematical Biology and Neuroscience

M1 - 10

ER -