Robust High-Dimensional Semiparametric Regression using Optimized Differencing Method Applied to Vitamin B2 Production Data

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ROOZBEH MAHDI; Maanavi Monireh; BABAIE KAFAKI SAMAN

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Journal Paper

Paper Information

Journal: Iranian Journal of Health Sciences Year:2020 | Volume:8 | Issue:2 Page(s): 9-22

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Title

Robust High-Dimensional Semiparametric Regression using Optimized Differencing Method Applied to Vitamin B2 Production Data

Author(s)

ROOZBEH MAHDI | Maanavi Monireh | BABAIE KAFAKI SAMAN | Issue Writer Certificate

Keywords

High-Dimensional Data Set

Ordinary Least Square Method

Outliers

Robust Regression

Abstract

Background and Purpose: By evolving science, knowledge, and technology, we deal with highdimensional data in which the number of predictors may considerably exceed the sample size. The main problems with high-dimensional data are the estimation of the coefficients and interpretation. For highdimension problems, classical methods are not reliable because of a large number of predictor variables. In addition, classical methods are affected by the presence of Outliers and collinearity. Methods: Nowadays, many real-world data sets carry structures of high-dimensional and Outliers problems. In the regression concept, an outlier is a point that fails to follow the main linear pattern of the data. The ordinary least-squares estimator is potentially sensitive to the Outliers; this fact provided necessary motivations to investigate robust estimations. To handle these problems, we combined the least absolute shrinkage and selection operator (LASSO) with the least trimmed squares (LTS) estimation. Results: Due to the flexibility and applicability of the semiparametric model in medical data, a penalized optimization approach for semiparametric regression models to simultaneously combat high-dimension and Outliers in the data set. Based on the numerical study, it was deduced that the proposed model is quite efficient in the sense that it has a significant value of goodness of fit (MSE=1. 3807). Conclusion: We have proposed an optimization approach for semiparametric models to combat Outliers in the data set. Especially, based on a penalization LASSO scheme, we have suggested a nonlinear integer programming problem as the semiparametric model which can be effectively solved by any evolutionary algorithm. We have also studied a real-world application related to the riboflavin production.

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APA: Copy

ROOZBEH, MAHDI, Maanavi, Monireh, & BABAIE KAFAKI, SAMAN. (2020). Robust High-Dimensional Semiparametric Regression using Optimized Differencing Method Applied to Vitamin B2 Production Data. IRANIAN JOURNAL OF HEALTH SCIENCES, 8(2), 9-22. SID. https://sid.ir/paper/769660/en

Vancouver: Copy

ROOZBEH MAHDI, Maanavi Monireh, BABAIE KAFAKI SAMAN. Robust High-Dimensional Semiparametric Regression using Optimized Differencing Method Applied to Vitamin B2 Production Data. IRANIAN JOURNAL OF HEALTH SCIENCES[Internet]. 2020;8(2):9-22. Available from: https://sid.ir/paper/769660/en

IEEE: Copy

MAHDI ROOZBEH, Monireh Maanavi, and SAMAN BABAIE KAFAKI, “Robust High-Dimensional Semiparametric Regression using Optimized Differencing Method Applied to Vitamin B2 Production Data,” IRANIAN JOURNAL OF HEALTH SCIENCES, vol. 8, no. 2, pp. 9–22, 2020, [Online]. Available: https://sid.ir/paper/769660/en

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