Kernel and regression spline smoothing techniques to estimate coefficient in rates model and its application in psoriasis

Osmani, Freshteh; Hajizadeh, Ebrahim; Mansouri, Parvin

doi:10.47176/mjiri.33.90

Cited by 8 publications

(13 citation statements)

References 14 publications

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“…Based on equation (11) and a theorem in [2] about Riesz representation, we can then state that there exists a representer of such that: ; where is notation for mathematical inner product. Based on equation (10) and by considering the inner product properties, we obtain: (12) Next, by using equation 12for we get: (13) Hence, based on equation 12for we have:…”

Section: B Estimation Of Regression Functionmentioning

confidence: 99%

“…In addition, [11] discussed optimal smoothing parameter selection for MNRM by using both the kernel estimator and smoothing spline estimator. Furthermore, [12] discussed techniques of smoothing, namely spline and kernel applied to psoriasis data, demonstrating that spline is better than kernel. However, the local linear [3], [4], [5], [6], local polynomial [7], [8], and kernel estimators [10], [11], [12] are highly dependent on the neighborhood of the target point, called the bandwidth, so that if used for the model estimation of fluctuating data, a small bandwidth is required and this will result in an estimation curve that is too rough.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, [15] has studied estimating regression function of the MNRM using the smoothing spline estimator and investigated the asymptotic properties of the regression function. It should be noted that the local linear [3], [4], [5], [6], local polynomial [7], [8], and kernel estimators [10], [11], [12] are highly dependent on the neighborhood of the target point, called the bandwidth, so that if used for model estimation from fluctuating data, a small bandwidth is required and this will result in an estimation curve that is too rough. This is the same as the penalized weighted least-squares estimator proposed by [9], which depends on knots.…”

Section: Introductionmentioning

confidence: 99%

“…Although those researchers discussed several estimators that can be used for estimating the regression functions of either the NRM or the SRM regression models, they discussed the estimators for uniresponse NRMs [1], [2], [3], [4], [5], [6], [7] and MNRMs [8], [9], [10], [11], [12], [13], [14], [15] only and uniresponse SRMs [16], [17], [18] only. In other words, they did not discuss estimating the regression function of MSRM using a smoothing spline estimator.…”

Section: Introductionmentioning

confidence: 99%

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Estimating Regression Function of Multi-response Semiparametric Regression Model Using Smoothing Spline

Lestari¹,

Chamidah²

2020

Journal of Southwest Jiaotong University

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The article describes a new estimation method of regression functions in a multi-response semiparametric regression model based on smoothing spline. The multi-response semiparametric regression model is a combined model between a parametric regression model and a nonparametric regression model, where there is a correlation between responses. The proposed estimation method enhances the flexibility of the multi-response semiparametric regression model by combining a goodness of fit function and a penalty function to calculate an estimation which not only considers the goodness of fitting of the model, but also the smoothness of the estimation model curve. The optimal trade-off between goodness and smoothness can be achieved by selecting the optimal smoothing parameters. The article discusses a theoretically proposed method for estimating this multi-response semiparametric regression model regression function of parametric and nonparametric components. We use the weighted least squares method to estimate the parametric component parameters, we determine the goodness of fit and penalty functions using the reproducing kernel Hilbert space method, and then take the result of penalized weighted least squares optimization to obtain an estimate of the nonparametric component. The new research results are a weighted least squares estimator of parameters of parametric components, and a weighted partial smoothing spline estimator of the nonparametric component. The result shows that the estimated multi-response semiparametric regression model is linear to the observation, and is a combination of the estimations of the parametric and nonparametric components. The research results of the estimation of this model can be applied to medical fields for predictive purposes.

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Section: B Estimation Of Regression Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Estimating Regression Function of Multi-response Semiparametric Regression Model Using Smoothing Spline

Lestari¹,

Chamidah²

2020

Journal of Southwest Jiaotong University

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“…The solution destroys the bacterial cell wall, damages proteins, and destroys bacterial DNA; thus, it is considered a suitable antiseptic to use in vascular access of hemodialysis patients. Rapid onset and 96 h durability are among other advantages of this antiseptic (21)(22)(23).…”

Section: Introductionmentioning

confidence: 99%

A Comparative Inquiry into the Effects of Alcohol Used After Betadine and the Combination of Alcohol and Betadine on Vascular Access Inflammation Severity in Hemodialysis Patients

2019

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Background: Looking after patients undergoing hemodialysis is very critical, because of the disease's complications. In the context of hemodialysis, the essential duty of nurses is reducing the inflammation resulting from the vascular access.Methods: This clinical trial was conducted during one month on 101 hemodialysis patients in Montaserie and Imam Reza hospitals, Mashhad, Iran. Sampling was accomplished using Non-probability Convenience method. Using random allocation, the patients were categorized into three groups including those who received a combination of alcohol and Betadine to disinfect the area (n = 37; henceforth, group (1), those who underwent a concurrent application of alcohol after Betadine (n = 33; henceforth, group (2), and the control group (n = 31)). In group 1, the vascular access site was disinfected with Betadine solution, and alcohol was mixed with a ratio of 2:1 before the dialysis initiated. In group 2, alcohol and Betadine were separately used for disinfection. In the control group, the routine method of disinfection was performed using Betadine, while antiseptic precautions were taken into account. During 12 sessions (4 weeks), the vascular access site of patients was evaluated,in terms of the inflammation criteria postulated by the Iranian Nurses Association. Data were analyzed using SPSS16 software (Sig P < 0.05) Results: Mean inflammation severity was lower in group 1 in comparison with group 2 (P = 0.039). No significant difference was observed between group 2 and the control group (P = 0.079). Conclusions:This combination can be used, if the study is going to be repeated in other hospitals, due to the fact that the combination of alcohol and Betadine is a simple, inexpensive, and most importantly is an effective way for reducing the inflammation of the vascular access site in hemodialysis patients.

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Consistency and Asymptotic Normality of Estimator for Parameters in Multiresponse Multipredictor Semiparametric Regression Model

et al. 2022

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A multiresponse multipredictor semiparametric regression (MMSR) model is a combination of parametric and nonparametric regressions models with more than one predictor and response variables where there is correlation between responses. Due to this correlation we need to construct a symmetric weight matrix. This is one of the things that distinguishes it from the classical method, which uses a parametric regression approach. In this study, we theoretically developed a method of determining a confidence interval for parameters in a MMSR model based on a truncated spline, and investigating asymptotic properties of estimator for parameters in a MMSR model, especially consistency and asymptotic normality. The weighted least squares method was used to estimate the MMSR model. Next, we applied a pivotal quantity method, a Cramer–Wold theorem, and a Slutsky theorem to determine the confidence interval, investigate consistency, and asymptotic normality properties of estimator for parameters in a MMSR model. The obtained results were that the estimated regression function is linear to observation. We also obtained a 100(1 − α)% confidence interval for parameters in the MMSR model, and the estimator for parameters in MMSR model was consistent and asymptotically normally distributed. In the future, these obtained results can be used as a theoretical basis in designing a standard toddlers growth chart to assess nutritional status.

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Kernel and regression spline smoothing techniques to estimate coefficient in rates model and its application in psoriasis

Cited by 8 publications

References 14 publications

Estimating Regression Function of Multi-response Semiparametric Regression Model Using Smoothing Spline

Estimating Regression Function of Multi-response Semiparametric Regression Model Using Smoothing Spline

A Comparative Inquiry into the Effects of Alcohol Used After Betadine and the Combination of Alcohol and Betadine on Vascular Access Inflammation Severity in Hemodialysis Patients

Consistency and Asymptotic Normality of Estimator for Parameters in Multiresponse Multipredictor Semiparametric Regression Model

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