Modeling of blood pressures based on stress score using least square spline estimator in bi-response non-parametric regression

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17 Citations (Scopus)

Abstract

The basic idea of non-parametric regression is to let the data decide which regression function fits best without imposing any specific form on it. Consequently, non-parametric regression methods are in general more flexible. They can uncover structure in the data that might otherwise be missed. Bi-response non-parametric regression model provides powerful tools for modeling the regression function which represents association between blood pressures and stress score. Spline estimator has powerful and flexible properties for estimating the regression function. In this paper we discuss methods to estimate blood pressure affected by a stress score using least squared spline estimator. The results show that the estimated regression function is linear in observation and biased estimator. Also, we obtain the minimum GCV value of 389.9907, and optimal smoothing parameter values of 0.5255788 and 2.544688.

Original languageEnglish
Pages (from-to)1200-1216
Number of pages17
JournalInternational Journal of Innovation, Creativity and Change
Volume5
Issue number3
Publication statusPublished - Aug 2019

Keywords

  • Blood pressure and stress score
  • GCV
  • Smoothing parameter
  • Smoothing spline estimator

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