Confidence interval of the parameter on multipredictor biresponse longitudinal data analysis using local linear estimator for modeling of case increase and case fatality rates COVID-19 in Indonesia: A theoretical discussion

Abstract

In this paper, we describe a theoretical discussion about confidence intervals for longitudinal data based on local linear estimator. The confidence interval represents the range of possible values in the estimating process. The confidence intervals for the parameter in nonparametric regression can be used to determine the predictor variables that have a significant effect on the response variable. In this research, we theoretically discuss estimation of the confidence interval of the parameter on multipredictor biresponse nonparametric regression model for longitudinal data based on local linear estimator which is applied to data of the case increase and case fatality rates COVID-19 in Indonesia. The estimation result can be used to determine the predictor variable, e.g. temperature which has a significant effect on the case increase and case fatality rates COVID-19 in Indonesia so that it can be advised to the ministry of health to control the case increase and case fatality rates COVID-19 in Indonesia.