Modeling of Maternal Mortality and Infant Mortality Cases in East Kalimantan using Poisson Regression Approach Based on Local Linear Estimator

Darnah,; Utoyo, Mohammad Imam; Chamidah, Nur

doi:10.1088/1755-1315/243/1/012023

Cited by 6 publications

(2 citation statements)

References 3 publications

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“…is the Kernel function and h is bandwidth (Darnah et al, 2019). g. Choose the polynomial order (p) and the optimal bandwidth (h) value that minimizes Generalized cross-validation (GCV) (Aydın et al, 2016):…”

Section: B Methodsmentioning

confidence: 99%

Platelet Modeling in DHF Patients Using Local Polynomial Semiparametric Regression on Longitudinal Data

Utami,

Chamidah,

Saifudin

2024

JTAM

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Regression analysis is one of the statistical methods used to model the relationship between response variables and predictor variables. Semiparametric regression is a combination of parametric and nonparametric regression. The estimator used in estimating the semiparametric regression model in this research is the Local Polynomial. Longitudinal data can be found in the health sector, including dengue hemorrhagic fever (DHF) data. The laboratory criteria for indication of DHF is thrombocytopenia. This research aims to obtain platelets model for DHF patients that can be used for forecasting so that it is hoped that it can provide information to the medical team in treating DHF patients. The estimated model used is Local Polynomial semiparametric regression on longitudinal data. The response variables in this research were platelets of DHF patients, which were influenced by hemoglobin as parametric predictor variable and examination time while hospitalized as nonparametric predictor variable. In the local polynomial regression model, it is necessary to select the optimal bandwidth and polynomial order method, GCV. The optimum bandwidth selection based on the GCV method obtained is 1.5 and polynomial order of 2, then applied to DHF patient platelet data, producing an estimated local polynomial semiparametric regression model that follows the actual data pattern. Modeling the platelets of DHF patients obtained using a local polynomial estimator resulted in an R2 value of 84.25% and MAPE of 4.5%, indicating highly accurate forecasting, so it can be concluded that the resulting model is better at predicting.

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Section: B Methodsmentioning

confidence: 99%

Platelet Modeling in DHF Patients Using Local Polynomial Semiparametric Regression on Longitudinal Data

Utami,

Chamidah,

Saifudin

2024

JTAM

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“…Regresi nonparametrik memiliki fleksibilitas yang tinggi, karena data diharapkan mencari sendiri bentuk estimasi kurva regresinya tanpa dipengaruhi oleh faktor subyektifitas peneliti [6]. Pada pendekatan menggunakan model regresi nonparametrik yang tidak memiliki spesifikasi pola fungsi regresi tertentu, dapat digunakan estimator-estimator untuk memperkirakan fungsi regresi dalam model, estimator-estimator yang biasa digunakan tersebut misalnya spline, kernel, linier lokal, dan lain sebagainya [12].…”

Section: Pendahuluanunclassified

Nonparametric Regression Modeling Based on Spline Truncated Estimator on Simulation Data

Nurhuda

Wasono

Nohe

2022

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Regression analysis is one of the statistical analysis used to estimate the pattern of the relationship between predictor variables and response variables . In general, the approach to estimating the regression function is the parametric regression, the nonparametric regression and the semiparametric regression. The approach with parametric regression is used if the shape of the regression curve is assumed to follow a certain pattern such as linear, quadratic, cubic and so on, but in fact there is an unknown pattern of relationship between predictor variables and response variables, so nonparametric regression is used. Then the combination of parametric and nonparametric regression is semiparametric regression. One of the well-known nonparametric regression estimators is the spline truncated. This study was conducted by simulating the relationship pattern of the response variable and the predictor variable that not have specific pattern by following a trigonometric function that formed a regression curve with a standard deviation of 0,05 and 0,25 which formed a different distribution of data, then will be approached with parametric regression (linear, quadratic, cubic) and nonparametric regression (spline truncated linear). Based on the coefficient of determination of each standard deviation, it will shows that the nonparametric regression approach has high flexibility so that it is able to adjust the form of regression curve estimation by itself

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Estimating model of the number of HIV and AIDS cases in East Java using bi-response negative binomial regression based on local linear estimator

Tohari

Chamidah

Fatmawati

2020

Symposium on Biomathematics 2019 (Symomath 2019)

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Modeling of Maternal Mortality and Infant Mortality Cases in East Kalimantan using Poisson Regression Approach Based on Local Linear Estimator

Cited by 6 publications

References 3 publications

Platelet Modeling in DHF Patients Using Local Polynomial Semiparametric Regression on Longitudinal Data

Platelet Modeling in DHF Patients Using Local Polynomial Semiparametric Regression on Longitudinal Data

Nonparametric Regression Modeling Based on Spline Truncated Estimator on Simulation Data

Estimating model of the number of HIV and AIDS cases in East Java using bi-response negative binomial regression based on local linear estimator

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