scholarly journals Nonparametric Regression Mixed Estimators of Truncated Spline and Gaussian Kernel based on Cross-Validation (CV), Generalized Cross-Validation (GCV), and Unbiased Risk (UBR) Methods

2021 ◽  
Vol 11 (6) ◽  
pp. 2400
Author(s):  
Vita Ratnasari ◽  
I Nyoman Budiantara ◽  
Andrea Tri Rian Dani
Keyword(s):  
Nonparametric Regression ◽  
Cross Validation ◽  
Gaussian Kernel ◽  
Generalized Cross Validation ◽  
Unbiased Risk

scholarly journals Model Regresi Nonparametrik dengan Pendekatan Spline (Studi Kasus: Berat Badan Lahir Rendah di Rumah Sakit Ibu dan Anak Siti Fatimah Makassar)

2020 ◽  
Vol 2 (1) ◽  
pp. 70
Author(s):  
Wahidah Sanusi ◽  
Rahmat Syam ◽  
Rabiatul Adawiyah
Keyword(s):  
Birth Weight ◽  
Low Birth Weight ◽  
Nonparametric Regression ◽  
Gestational Age ◽  
Cross Validation ◽  
Predictor Variable ◽  
Coefficient Of Determination ◽  
Generalized Cross Validation ◽  
Child Hospital ◽  
The Relationship

Pendekatan nonparametrik merupakan suatu pendekatan yang digunakan apabila bentuk hubungan antara variabel respon dan variabel prediktornya tidak diketahui atau tidak adanya informasi mengenai bentuk fungsi regresinya. Spline merupakan suatu teknik yang dilakukan untuk mengestimasi parameter dalam regresi nonparametrik. Penelitian ini bertujuan untuk mengetahui model hubungan antara berat badan lahir rendah dan faktor-faktor yang mempengaruhi berdasarkan model spline. Faktor-faktor tersebut adalah usia ibu, usia kehamilan, dan jarak kehamilan. Data tersebut diperoleh dari rumah sakit ibu dan anak siti Fatimah Makassar tahun 2017. Dimana untuk mendapatkan model spline terbaik langkah awal yang dilakukan adalah menentukan knot dengan nilai Generalized Cross Validation (GCV) yang minimum. Berdasarkan penelitian yang telah dilakukan, dua variabel dinyatakan berpengaruh terhadap berat badan lahir rendah yaitu usia ibu, dan usia kehamilan. Model regresi nonparametrik dengan pendekatan Spline yang terbentuk memiliki koefisien determinasi sebesar 78,19%, serta nilai GCV dengan tiga titik knot yaitu 0.0117.Kata kunci: Regresi Nonparametrik, Spline, Berat Badan Lahir Rendah, Generalized Cross Validation The non-parametric approach is an approach that is used if the form of the relationship between the response variable and the predictor variable is unknown or the absence of information about the shapes of regression functions. The Spline is a technique performed to estimate the parameters in the nonparametric regression. This study aims to determine the model of the relationship between low birth weight and the factors that affect the based on the spline model. Such factors are maternal age, gestational age, and pregnancy distance. The Data is obtained from the mother and child hospital siti Fatimah Makassar 2017. Where to get a spline model best the initial step is to determine the knots with the value of the Generalized Cross Validation (GCV) which is a minimum. Based on the research that has been done, the two variables stated effect against low birth weight, namely age of mother, and gestational age. Nonparametric regression Model with the approach of the Spline that is formed has a coefficient of determination of 78.19 to%, as well as the value of the GCV with a three-point knot that is 0.0117.Keyword : Nonparametric Regression, Spline, Low Birth Weight, Generalized Cross Validation

scholarly journals PEMODELAN RATA-RATA LAMA SEKOLAH MENGGUNAKAN PENDEKATAN REGRESI NONPARAMETRIK SPLINE MULTIVARIABLE

2021 ◽  
Vol 10 (2) ◽  
pp. 53
Author(s):  
NI LUH GEDE SINTA ARYATI ◽  
I KOMANG GDE SUKARSA ◽  
I GUSTI AYU MADE SRINADI
Keyword(s):  
Human Development ◽  
Nonparametric Regression ◽  
Human Development Index ◽  
Cross Validation ◽  
Development Index ◽  
Generalized Cross Validation ◽  
Minimum Value

Mean years school (MYS) is one of the indicators used in calculating the human development index (HDI). The value of MYS Indonesia in 2019 is 8,75 which is still low. Therefore it still needs to be improved. In this research, MYS modeling will be carried out using six factors that are thought to influence MYS. This research uses multivariable spline nonparametric regression to modeling MYS Indonesia in 2019. The best model is selected based on the minimum value of Generalized Cross Validation (GCV). Based on this research, the best model obtained is a linear orde (orde 2) spline model with four knots. The model has    value of  99,91%.

scholarly journals Penerapan Generalized Cross Validation dalam Model Regresi Smoothing Spline pada Produksi Ubi Jalar di Jawa Tengah

2019 ◽  
Vol 1 (2) ◽  
pp. 117
Author(s):  
Trionika Dian Wahyuningsih ◽  
Sri Sulistijowati Handajani ◽  
Diari Indriati
Keyword(s):  
Sweet Potato ◽  
Nonparametric Regression ◽  
Cross Validation ◽  
Animal Feed ◽  
Organic Fertilizer ◽  
Specific Pattern ◽  
Production Model ◽  
Smoothing Spline ◽  
Urea Fertilizer ◽  
Generalized Cross Validation

<p>Sweet Potato is a useful plant as a source carbohydrates, proteins, and is used as an animal feed and ingredient industry. Based on data from the Badan Pusat Statistik (BPS), the production fluctuations of the sweet potato in Central Java from year to year are caused by many factor. The production of sweet potato and the factors that affected it if they are described into a pattern of relationships then they do not have a specific pattern and do not follow a particular distribution, such as harvest area, the allocation of subsidized urea fertilizer, and the allocation of subsidized organic fertilizer. Therefore, the production model of sweet potato could be applied into nonparametric regression model. The approach used for nonparametric regression in this study is smoothing spline regression. The method used in regression smoothing spline is generalized cross validation (GCV). The value of the smoothing parameter (λ) is chosen from the minimum GCV value. The results of the study show that the optimum λ value for the factors of harvest area, urea fertilizer and organic fertilizer are 5.57905e-14, 2.51426e-06, and 3.227217e-13 that they result a minimum GCV i.e 2.29272e-21, 1.38391e-16, and 3.46813e-24.</p><p> </p><p><strong>Keywords</strong>: Sweet potato; nonparametric; smoothing spline; generalized cross validation.</p>

A note on generalized cross-validation with replicates

1992 ◽  
Vol 14 (4) ◽  
pp. 283-287 ◽  
Author(s):  
Chong Gu ◽  
Nancy Heckman ◽  
Grace Wahba
Keyword(s):  
Cross Validation ◽  
Generalized Cross Validation

Automatic blind deconvolution with Toeplitz-structured sparse total least squares

Geophysics ◽  
2018 ◽  
Vol 83 (6) ◽  
pp. V345-V357 ◽  
Author(s):  
Nasser Kazemi
Keyword(s):  
Least Squares ◽  
Cross Validation ◽  
Blind Deconvolution ◽  
Real Data ◽  
Total Least Squares ◽  
Data Matrix ◽  
Seismic Survey ◽  
Structural Constraints ◽  
Generalized Cross Validation ◽  
Free Data

Given the noise-corrupted seismic recordings, blind deconvolution simultaneously solves for the reflectivity series and the wavelet. Blind deconvolution can be formulated as a fully perturbed linear regression model and solved by the total least-squares (TLS) algorithm. However, this algorithm performs poorly when the data matrix is a structured matrix and ill-conditioned. In blind deconvolution, the data matrix has a Toeplitz structure and is ill-conditioned. Accordingly, we develop a fully automatic single-channel blind-deconvolution algorithm to improve the performance of the TLS method. The proposed algorithm, called Toeplitz-structured sparse TLS, has no assumptions about the phase of the wavelet. However, it assumes that the reflectivity series is sparse. In addition, to reduce the model space and the number of unknowns, the algorithm benefits from the structural constraints on the data matrix. Our algorithm is an alternating minimization method and uses a generalized cross validation function to define the optimum regularization parameter automatically. Because the generalized cross validation function does not require any prior information about the noise level of the data, our approach is suitable for real-world applications. We validate the proposed technique using synthetic examples. In noise-free data, we achieve a near-optimal recovery of the wavelet and the reflectivity series. For noise-corrupted data with a moderate signal-to-noise ratio (S/N), we found that the algorithm successfully accounts for the noise in its model, resulting in a satisfactory performance. However, the results deteriorate as the S/N and the sparsity level of the data are decreased. We also successfully apply the algorithm to real data. The real-data examples come from 2D and 3D data sets of the Teapot Dome seismic survey.

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