Nonparametric Regression Estimates Based on Imputation Techniques for Right-Censored Data

This study aims to introduce four modified linear estimators for the right-censored high-dimensional data. Obviously, data of interest involves two important problems to be solved that are censorship and high dimensionality. This paper can be distinguished from other studies in the literature with that it achieves to handle these two problems simultaneously. The main contribution of the paper is merging weightedridge method with the imputation techniques to obtain more efficient estimators than its alternatives. To solve the censorship problem, four imputation techniques are considered based on machine learning algorithms kNN, sliding-windows, regression and support vector machines. The high-dimensionality problem is handled by the weighted ridge approach which provides estimator with less risk than its alternatives because it detects the covariates with a weak contribution via the post-selection procedure. To show the empirical performance of the introduced estimators, a simulation study is made and comparative results are presented. Results show that kNN and regression imputation basis WR esitmators show satisfying performances on estimation of the high-dimensional right-censored model.

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“…In this paper, kNN imputation summarized and provides an algorithm. All the details about the method can be seen in (Ahmed et al, 2019).…”

Section: Knn Imputationmentioning

confidence: 99%

“…where m E (x, z) represents the function of distance measure. To obtained the imputed values of the right-censored data points in the response variable y i , this paper considers the algorithm proposed by (Ahmed et al, 2019)…”

Section: Knn Imputationmentioning

confidence: 99%

Regression with right-censored high-dimensional data: An application with different imputation techniques

Yılmaz

Aydın

Ahmed

2022

KJS publishes peer-review articles in Mathematics, Computer Sci

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“…An algorithm is developed to perform the kNN imputation for right-censored observations and is given in Algorithm 1. This algorithm is also used in Ahmed et al (2020).…”

Section: The Knn Imputation Techniquementioning

confidence: 99%

“…This paper solves the multi-collinearity problem by using the kernel-type ridge estimator for partially linear models studied by Yüzbaşı et al (2017). To overcome censorship, we use two methods: the synthetic data transformation method described in Koul et al (1981) and the kNN imputation method studied by Batista and Monard (2002) and Ahmed et al (2020). Note also that kernel ridge regression under censored data has been previously studied by Shim (2005) in a nonparametric setting.…”

Section: Introductionmentioning

confidence: 99%

Kernel Ridge Estimator for the Partially Linear Model under Right-Censored Data

Ahmed

Aydın

Yılmaz

2021

JIRSS

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Objective: This paper aims to introduce a modified kernel-type ridge estimator for partially linear models under randomly-right censored data. Such models include two main issues that need to be solved: multi-collinearity and censorship. To address these issues, we improved the kernel estimator based on synthetic data transformation and kNN imputation techniques. The key idea of this paper is to obtain a satisfactory estimate of the partially linear model with multi-collinear and right-censored using a modified ridge estimator. Results: To determine the performance of the method, a detailed simulation study is carried out and a kernel-type ridge estimator for PLM is investigated for two censorship solution techniques. The results are compared and presented with tables and figures. Necessary derivations for the modified semiparametric estimator are given in appendices.

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“…Examples of these studies include [1][2][3][4][5][6][7][8]. In the context of the imputation, there are several notable studies such as [9][10][11][12][13]. The cited references examined different types of attribution techniques for both missing data and censorship data.…”

Section: Introductionmentioning

confidence: 99%

Kernel Smoothing as an Imputation Technique for Right-Censored Data

Aydın

Yılmaz

2020

Eskişehir Technical University Journal of Science and Technology a - Applied Sciences and Engineering

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Imputation of right-censored observations is an important problem in statistics and other applied sciences. Since rightcensored data sets are common in medical studies and survival analysis, researchers should be careful about data quality. In this sense, imputation techniques are used to correctly estimate and complete censored data points. This study introduces the kernel smoothing method as an imputation method that takes into account the structure of the data and the individual effects of the accessible data points with kernel weights. The basic idea is to obtain a nonparametric model from the missing data set and consider sample predictions to estimate the censored ones. A simulation study is conducted to show the benefits of the method, and it is also compared with Ordinary Least Squares (OLS) based imputation, which is one of the widely used imputation methods and works similar to the proposed method.

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Nonparametric Regression Estimates Based on Imputation Techniques for Right-Censored Data

Cited by 9 publications

References 11 publications

Regression with right-censored high-dimensional data: An application with different imputation techniques

Regression with right-censored high-dimensional data: An application with different imputation techniques

Kernel Ridge Estimator for the Partially Linear Model under Right-Censored Data

Kernel Smoothing as an Imputation Technique for Right-Censored Data

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