A Useful Pivotal Quantity

Toulis, Panagiotis

doi:10.1080/00031305.2016.1237894

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Estimates Of Confidence Intervals With Variance Are Known1

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2022

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“…We use the pivotal quantity approach (Toulis, 2017) in estimating the confidence interval for the multi-variable spline regression coefficient. First, we determine the expected value of α , namely:…”

Section: Estimates Of Confidence Intervals With Variance Are Knownmentioning

confidence: 99%

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

Islamiyati

Raupong

Kalondeng

et al. 2022

Statistics in Transition New Series

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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.

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Section: Estimates Of Confidence Intervals With Variance Are Knownmentioning

confidence: 99%