Optimal knot selection in spline regression using unbiased risk and generalized cross validation methods

Utami, Tiani Wahyu; Haris, Muhammad; Prahutama, Alan; Purnomo, Eko Andy

doi:10.1088/1742-6596/1446/1/012049

Cited by 5 publications

(4 citation statements)

References 4 publications

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“…Untuk melakukan pemodelan regresi B-splines dalam penelitian agar diperoleh regresi smoothing B-splines adalah dengan menentukan jumlah knot optimal (𝑡 𝑜𝑝𝑡 ) dan smoothing parameter optimal (𝜆 𝑜𝑝𝑡 ) berdasarkan kriteria Generalized Cross Validation (GCV) (Devi et al, 2019;Utami et al, 2020):…”

Section: Penentuan Knot Optimal Pada Smoothing B-splinesunclassified

Penerapan Smoothing B-Splines Pada Hubungan Antara Pertumbuhan Ekonomi Dan Tingkat Kebahagiaan

Fajar

Fajariyanto

2023

J.Gauss

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This study aimed to model the smoothing of B-splines on the relationship between happiness and economic growth. The method used in this research is smoothing B-splines, which were in the process of determining knots and smoothing parameters (λ) based on the minimum GCV. The data used in the study came from Badan Pusat Statistik-Statistics Indonesia. The results of this study concluded that the smoothing of B-splines is quite good at modeling the relationship between the level of happiness (response variable) and economic growth (predictor variable). The smoothing B-splines model can explain the variation in the level of happiness by 71.583 percent.

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Section: Penentuan Knot Optimal Pada Smoothing B-splinesunclassified

Penerapan Smoothing B-Splines Pada Hubungan Antara Pertumbuhan Ekonomi Dan Tingkat Kebahagiaan

Fajar

Fajariyanto

2023

J.Gauss

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“…There is a method to select optimal smoothing parameters in the spline model, namely Generalized Cross Validation (GCV). The best spline estimator is obtained when the knot point is optimal leading to parsimony or simplicity of the model (Devi, Pratama et al, 2019;Utami et al, 2020).…”

Section: A Introductionmentioning

confidence: 99%

The Application of Truncated Spline Semiparametric Path Analysis on Determining Factors Influencing Cashless Society Development

Pramaningrum,

Fernandes,

Iriany

et al. 2024

JTAM

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Semiparametric path analysis is a combination of parametric and nonparametric path analysis. Semiparametric path analysis is used when there are partially nonlinear and unknown patterns of relationships. One approach to semiparametric pathways is truncated spline. Truncated spline approach tends to search for their own estimation of regression functions according to the data. This is because in the truncated spline there are knot points, which are intersection points that indicate changes in data behavior patterns. Truncated spline semiparametric path analysis will be applied to this study to determine the variables that have a significant effect on the development of the Cashless Society so that the result can be used as a reference for banks and the government in maximizing non-cash-based community development. The data used is the result of a questionnaire with 100 respondents of mobile banking users in Jakarta and will be analyzed using R Studio. Based on the results, it was found that the optimal knot point in the truncated spline function is 3 with many knots is 1, thus dividing the condition of digitizing electronic money into 2 regimes. It was concluded that the product and digitalization of electronic money had a significant effect on the development of cashless society where the modeling obtained could explain 83.87548% of the data. However, when electronic digitalization increases through the value of knot points, the development of cashless society tends to stagnate. This could be due to people who are not ready when the condition of digitizing electronic money is increasingly sophisticated because the available electronic money features are increasingly complex. Therefore, it is important for banks to pay attention to the sophistication of electronic money features provided to customers and adjust the target market so that customers are more accustomed and comfortable to use electronic money in the future.

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“…The nonparametric regression method is used in this study because the data used is nonlinear data, the approach used is a fourier series and a local polynomial. Both of these methods have been studied a lot including research on nonparametric regression including: [13], [14], [15] and [16]. This study will conclude the results of climate change projections in Indonesia, through the Statistical Downscaling method which then reduces bias correction with Nonparametric Regression.…”

Section: Introductionmentioning

confidence: 99%

Statistical Downscaling Using Regression Nonparametric of Fourier Series-Polynomial Local of Climate Change

Utami,

Fauzi,

Yuliyanto

2023

BAREKENG: J. Math. & App.

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Indonesia is a tropical country that is vulnerable to the impacts of climate change. Climate change causes an effect on the level of comfort (heat stress) that can affect the level of human immunity, one of the indices to calculate the level of human comfort (heat stress) is the Thermal Humidity Index (THI). Climate change scenarios modeled in Earth System Models (ESMs). ESM has a coarse resolution and is subject to considerable bias. This research is using secondary data. The data source used in this study comes from the Coupled Model Intercomparison Project (CMIP5). This research will focus on projected heat stress which is calculated based on THI with the temperature and humidity variables. Therefore, in this research to reduce the bias correction method used Statistical Downscaling (SD) and nonparametric regression. The results of the bias correction using the Statistical Downscaling (SD) method and Nonparametric Regression Fourier-Polynomial Local Series in this study the R-square value for Relative Humidity yields 95% and for Temperature yields 94%. The projection of climate change based on the value of the Temperature Humidity Index (THI) in Indonesia in the category of 50% of the population of Indonesians feeling comfortable conditions occurred in 2006-2059. Then the population of citizens in Indonesia felt uncomfortable conditions occurred in 2060 to 2100 with a THI value of 27.0730°C - 27.7800°C.

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Optimal knot selection in spline regression using unbiased risk and generalized cross validation methods

Cited by 5 publications

References 4 publications

Penerapan Smoothing B-Splines Pada Hubungan Antara Pertumbuhan Ekonomi Dan Tingkat Kebahagiaan

Penerapan Smoothing B-Splines Pada Hubungan Antara Pertumbuhan Ekonomi Dan Tingkat Kebahagiaan

The Application of Truncated Spline Semiparametric Path Analysis on Determining Factors Influencing Cashless Society Development

Statistical Downscaling Using Regression Nonparametric of Fourier Series-Polynomial Local of Climate Change

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