Estimation of Regression Function in Multi-Response Nonparametric Regression Model Using Smoothing Spline and Kernel Estimators

Lestari, Budi; Fatmawati, Fatmawati; Budiantara, I Nyoman; Chamidah, Nur

doi:10.1088/1742-6596/1097/1/012091

Cited by 20 publications

(10 citation statements)

References 25 publications

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“…In the real cases, we are often faced to the problem in which response variable is observed at several values of the predictor variables. Spline estimator has been studied by [1][2][3][4][5][6]. Designing growth charts of children up to five years old based on local polynomial modeling, kernel and local linear, respectively have been done by [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Improving of classification accuracy of cyst and tumor using local polynomial estimator

et al. 2019

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Cyst and tumor in oral cavity are seriously noticed by health experts along with increasing death cases of oral cancer in developing country. Early detection of cyst and tumor using dental panoramic image is needed since its initial growth does not cause any complaints. Image processing is done by mean for distinguishing the classification of cyst and tumor. The results in previous studies about classification of cyst and tumor were done by using a mathematical computation approach namely supports vector machine method that have still not satisfied and have not been validated. Therefore, in this study we propose a method, i.e., nonparametric regression model based on local polynomial estimator that can be improve the classification accuracy of cyst and tumor on human dental panoramic image. By using the proposed method, we get the classification accuracy of cyst and tumor, i.e., 90.91% which is greater than those by using the support vector machine method, i.e., 76.67%. Also, in validation process we obtain that the nonparametric regression model approach gives a significant Press's Q statistical testing value. So, we conclude that the nonparametric regression model approach improves the classification accuracy and gives better outcome to classify cyst and tumor using dental panoramic image than the support vector machine method.

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Section: Introductionmentioning

confidence: 99%

Improving of classification accuracy of cyst and tumor using local polynomial estimator

et al. 2019

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“…Estimator Least Square Spline (LS-Spline) dipilih karena mampu mengikuti pola perilaku data yang berfluktuasi dengan menempatkan titik knot. Pemilihan model terbaik dilakukan dengan membandingkan nilai Generalized Cross Validation (GCV), dimana nilai GCV terendah menunjukkan model yang optimal [11][12]20]. Pendekatan ini telah banyak dipelajari dan diterapkan oleh berbagai peneliti [13].…”

Section: Pendahuluanunclassified

Analyzing The Effect of BI 7-Days Repo Rate on The Jakarta Composite Index Using Nonparametric Regression Approaches Based on Least Square Spline Estimator

Andreas

Harianto²,

Safitri³

et al. 2021

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During the Covid-19 pandemic, the Indonesia stock market was under great pressure, so that the value of the Jakarta Composite Index (JCI) fluctuated greatly. To maintain economic stability, Bank Indonesia has regulated monetary policy such as setting the BI 7-Days Repo Rate. Analysis of this effect is important to formulate the right policy. This study aims to design the best model in describing the relationship between JCI value and BI 7-Days Repo Rate. The analysis was carried out by using parametric regression approach based on the ordinary least square method and nonparametric regression approach based on least square spline estimator. The results showed that the parametric regression models failed to meet the classical assumptions. Meanwhile, nonparametric regression can produce an optimal model with high accurate prediction, with an overall mean absolute percentage error value of 3.16%. Furthermore, mean square error, coefficient of determination, and mean absolute deviation also show good results. Thus, the effect of the BI 7-Days Repo Rate on the JCI value forms a quadratic pattern, in which a positive relationship is formed when the BI 7-Days Repo Rate is set at more than 4.25% and vice versa for a negative relationship.

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“…Regresi nonparametrik sudah banyak dikembangkan oleh peneliti. Grajeda, Laura, Ivanescu dan Checkley (2016) Oktober-2019, Vol.7, No.2, hal.97-112 estimator smoothing spline 14 . Artikel ini mengusulkan penggunaan estimator spline polynomial truncated karena memiliki sifat yang lebih fleksibel dan mudah diinterpretasikan secara visual 15…”

Section: Pendahuluanunclassified

Regresi Spline Polynomial Truncated Biprediktor untuk Identifikasi Perubahan Jumlah Trombosit Pasien Demam Berdarah Dengue

Islamiyati

2019

JPMIPA

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Abstract:This paper is a longitudinal study using a nonparametric regression model to identify changes in platelet count from dengue fever. Changes in platelet counts were analyzed based on treatment time and hematocrit count factors. The estimator method proposed is spline polynomial truncated bipredictor. Based on the results of the simultaneous model estimation, we obtained GCV = 714.72 and R2 = 95.9%, it means the model is feasible to explain and identify changes in platelet count based on the time of treatment and the number of hematocrit from DBD patients. Based on the data, there are four patterns of platelet change based on time of treatment and three patterns of platelet change based on hematocrit that are different from each other.Abstrak:Paper ini merupakan studi longitudinal dengan menggunakan model regresi nonparametrik untuk mengidentifikasi perubahan jumlah trombosit demam berdarah. Perubahan jumlah trombosit dianalisis berdasarkan faktor waktu perawatan dan jumlah hematokrit. Metode estimator yang diusulkan adalah spline polynomial truncated bi prediktor. Berdasarkan hasil taksiran model simultan diperoleh GCV = 714,72 dan R2 = 95,9%, artinya model layak untuk menjelaskan dan mengidentifikasi perubahan jumlah trombosit berdasarkan waktu perawatan dan jumlah hematokrit pasien DBD. Berdasarkan data, terdapat empat pola perubahan trombosit berdasarkan waktu perawatan dan tiga pola perubahan trombosit berdasarkan hematokrit yang berbeda satu sama lain.

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Estimation of Regression Function in Multi-Response Nonparametric Regression Model Using Smoothing Spline and Kernel Estimators

Cited by 20 publications

References 25 publications

Improving of classification accuracy of cyst and tumor using local polynomial estimator

Improving of classification accuracy of cyst and tumor using local polynomial estimator

Analyzing The Effect of BI 7-Days Repo Rate on The Jakarta Composite Index Using Nonparametric Regression Approaches Based on Least Square Spline Estimator

Regresi Spline Polynomial Truncated Biprediktor untuk Identifikasi Perubahan Jumlah Trombosit Pasien Demam Berdarah Dengue

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