Estimating the confidence interval of the regression coefficient of the blood sugar model through a multivariable linear spline with known variance

Islamiyati Anna (), Raupong (), Kalondeng Anisa () and Sari Ummi ()
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Islamiyati Anna: Department of Statistics, Faculty of Mathematics and Natural Sciences, Hasanuddin University, Indonesia .
Raupong: Department of Statistics, Faculty of Mathematics and Natural Sciences, Hasanuddin University, Indonesia .
Kalondeng Anisa: Department of Statistics, Faculty of Mathematics and Natural Sciences, Hasanuddin University, Indonesia .
Sari Ummi: Hasanuddin University Teaching Hospital, Indonesia

Statistics in Transition New Series, 2022, vol. 23, issue 1, 201-212

Abstract: Estimates from confidence intervals are more powerful than point estimates, because there are intervals for parameter values used to estimate populations. In relation to global conditions, involving issues such as type 2 diabetes mellitus, it is very difficult to make estimations limited to one point only. Therefore, in this article, we estimate confidence intervals in a truncated spline model for type 2 diabetes data. We use a non-parametric regression model through a multi-variable spline linear estimator. The use of the model results from the irregularity of the data, so it does not form a parametric pattern. Subsequently, we obtained the interval from beta parameter values for each predictor. Body mass index, HDL cholesterol, LDL cholesterol and triglycerides all have two regression coefficients at different intervals as the number of the found optimal knot points is one. This value is the interval for multivariable spline regression coefficients that can occur in a population of type 2 diabetes patients.

Keywords: confidence interval; diabetes; known variance; spline (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:stintr:v:23:y:2022:i:1:p:201-212:n:4

DOI: 10.2478/stattrans-2022-0012

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