Simulation Study and Development of Semiparametric Multiresponse Multigroup Truncated Spline Regression for Rice Pest Control
Rice pest control is a critical challenge in the agricultural sector that requires a deep understanding of rice pest management. Regression analysis is a statistical method capable of describing and predicting cause-and-effect relationships between individuals. In real-life applications, not all rel...
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| Language: | English |
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Mathematics Department UIN Maulana Malik Ibrahim Malang
2025-03-01
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| Series: | Cauchy: Jurnal Matematika Murni dan Aplikasi |
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| Online Access: | https://ejournal.uin-malang.ac.id/index.php/Math/article/view/29773 |
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| author | Laila Nur Azizah Adji Achmad Rinaldo Fernandes Ni Wayan Surya Wardhani |
| author_facet | Laila Nur Azizah Adji Achmad Rinaldo Fernandes Ni Wayan Surya Wardhani |
| author_sort | Laila Nur Azizah |
| collection | DOAJ |
| description | Rice pest control is a critical challenge in the agricultural sector that requires a deep understanding of rice pest management. Regression analysis is a statistical method capable of describing and predicting cause-and-effect relationships between individuals. In real-life applications, not all relationships exhibit a known curve pattern, and non-identifiable curve forms are often observed. Additionally, a single cause may affect more than one outcome, and the outcomes themselves can have interrelationships. Such relationships can be approached through a multi-response semiparametric regression using a truncated spline multi-group model. This study aims to develop a multi-response semiparametric multi-group regression model using the truncated spline approach to understand the variables influencing rice pest control under light and dark conditions. This model is applied to secondary and simulated data with various scenarios to determine the best model. The study results indicate that the optimal model for secondary data is a semiparametric regression model with a linear order and a single knot point, achieving a determination coefficient of 89.17%. Simulation results show that the scenario 1 model (linear with a single knot point) produces a high determination coefficient. This multi-response regression model proves more optimal when error variance and multicollinearity levels are kept low to moderate. |
| format | Article |
| id | doaj-art-b2fc56bc0d6b4a998270c7c659482da1 |
| institution | Kabale University |
| issn | 2086-0382 2477-3344 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Mathematics Department UIN Maulana Malik Ibrahim Malang |
| record_format | Article |
| series | Cauchy: Jurnal Matematika Murni dan Aplikasi |
| spelling | doaj-art-b2fc56bc0d6b4a998270c7c659482da12025-08-20T03:48:30ZengMathematics Department UIN Maulana Malik Ibrahim MalangCauchy: Jurnal Matematika Murni dan Aplikasi2086-03822477-33442025-03-01101365210.18860/cauchy.v10i1.297738637Simulation Study and Development of Semiparametric Multiresponse Multigroup Truncated Spline Regression for Rice Pest ControlLaila Nur Azizah0Adji Achmad Rinaldo Fernandes1Ni Wayan Surya Wardhani2Brawijaya UniversityBrawiajaya UniversityBrawijaya UniversityRice pest control is a critical challenge in the agricultural sector that requires a deep understanding of rice pest management. Regression analysis is a statistical method capable of describing and predicting cause-and-effect relationships between individuals. In real-life applications, not all relationships exhibit a known curve pattern, and non-identifiable curve forms are often observed. Additionally, a single cause may affect more than one outcome, and the outcomes themselves can have interrelationships. Such relationships can be approached through a multi-response semiparametric regression using a truncated spline multi-group model. This study aims to develop a multi-response semiparametric multi-group regression model using the truncated spline approach to understand the variables influencing rice pest control under light and dark conditions. This model is applied to secondary and simulated data with various scenarios to determine the best model. The study results indicate that the optimal model for secondary data is a semiparametric regression model with a linear order and a single knot point, achieving a determination coefficient of 89.17%. Simulation results show that the scenario 1 model (linear with a single knot point) produces a high determination coefficient. This multi-response regression model proves more optimal when error variance and multicollinearity levels are kept low to moderate.https://ejournal.uin-malang.ac.id/index.php/Math/article/view/29773multi-groupmulti-responses semiparametric regressionrice pesttruncated splineweighted least square |
| spellingShingle | Laila Nur Azizah Adji Achmad Rinaldo Fernandes Ni Wayan Surya Wardhani Simulation Study and Development of Semiparametric Multiresponse Multigroup Truncated Spline Regression for Rice Pest Control Cauchy: Jurnal Matematika Murni dan Aplikasi multi-group multi-responses semiparametric regression rice pest truncated spline weighted least square |
| title | Simulation Study and Development of Semiparametric Multiresponse Multigroup Truncated Spline Regression for Rice Pest Control |
| title_full | Simulation Study and Development of Semiparametric Multiresponse Multigroup Truncated Spline Regression for Rice Pest Control |
| title_fullStr | Simulation Study and Development of Semiparametric Multiresponse Multigroup Truncated Spline Regression for Rice Pest Control |
| title_full_unstemmed | Simulation Study and Development of Semiparametric Multiresponse Multigroup Truncated Spline Regression for Rice Pest Control |
| title_short | Simulation Study and Development of Semiparametric Multiresponse Multigroup Truncated Spline Regression for Rice Pest Control |
| title_sort | simulation study and development of semiparametric multiresponse multigroup truncated spline regression for rice pest control |
| topic | multi-group multi-responses semiparametric regression rice pest truncated spline weighted least square |
| url | https://ejournal.uin-malang.ac.id/index.php/Math/article/view/29773 |
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