The Estimation Function Approach Smoothing Spline Regression Analysis for Longitudinal Data

Risnawati, .; Fernandes, Adji Achmad Rinaldo; Nurjannah, .

doi:10.1088/1757-899x/546/5/052064

Cited by 3 publications

(2 citation statements)

References 2 publications

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“…Various problems can occur in estimating model parameters, one of which is if the assumption of normality isn't fulfilled, one of which is due to outliers in the data (Fernandes et al, 2019). According to (Draper & Smith, 1992), data coming out of the regression line is an outlier.…”

Section: Introductionmentioning

confidence: 99%

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Robust Regression Analysis Study for Data with Outliers at Some Significance Levels

Nugroho¹,

Wardhani²,

Fernandes³

et al. 2020

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Robust regression analysis is an analysis that is used if there is an outlier in a regression model. Outliers cause data to be abnormal. The most commonly used parameter estimation method is Ordinary Least Squares (OLS). However, outliers in models cause the estimator of the least-squares in the model to be biased, so handling of outliers is required. One of the regressions used for outliers is robust regression. Robust regression method that can be used is M-Estimation. By using Tukey's Bisquare weighted function, a robust M-estimation method can estimate parameters in a model, for example in malnutrition data in East Java Province 2017 to 2012. This study aims to compare the robust method of M-estimation and OLS method on data with several different levels of significance, which is 1%, 5%, and 10%. The predictor variables used in this study were the percentage of poor society, population density, and some health facilities. R 2 is used to compare the OLS method and the robust method of M-estimation. The results obtained that robust regression is the best method to handle the model if there are outliers in the data. It was supported by almost all results of the value of 2 on each data that M-estimation has a higher value than the OLS method.

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Section: Introductionmentioning

confidence: 99%

“…The robust method is used when a data contains an outlier and has an abnormal distribution that will affect the parameter estimator (Risnawati et al, 2019). The purpose of the robust method is to detect outliers in the data and give strong results to the effect of outliers (Rousseeuw & Leroy, 1987).…”

Section: Introductionmentioning

confidence: 99%

Robust Regression Analysis Study for Data with Outliers at Some Significance Levels

Nugroho¹,

Wardhani²,

Fernandes³

et al. 2020

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The handling of people smuggling involving foreign nationals as efforts to safeguard Indonesian territories

Hadiyantina

2020

IJDIPE

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This study aimed to investigate the responsibility of Indonesia as a transit country against foreigners who are involved in people smuggling crime in the territory of Indonesia. The approach method used in this research was the Juridical Sociological approach, which means that this study examined the problem by researching from the aspect of law science. In handling the crime of people smuggling involving foreigners, Indonesia has its own rules in the handling of smuggled foreigners. Therefore, there needs to be coordination with other related agencies. There is a difference in the handling of this people smuggling case. This research is one of the few studies that examine people smuggling, given Indonesia's geographic position that lies between two continents and two oceans make Indonesia a very strategic area, not only bringing positive impacts for the benefit of access to transnational commerce but also the international maritime path.

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Creditor Classification Logistic Regression Ensemble Boosting And Logistic Regression In Creditor Classification With Binary Response

Chairunissa

Solimun

Fernandes

2021

Wseas Transactions on Systems and Control

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Credit risk is the risk that has the greatest opportunity to occur in banking. The number of bad loans will also affect bank performance. The banking sector needs to know whether a prospective creditor is classified as a risky person or not. The purpose of this study is to classify creditors and compare the classification results through logistic regression with the maximum likelihood model and the Boosting algorithm, especially the AdaBoost algorithm, and to select a model with the Boosting algorithm Credit Scoring aims to classify prospective creditor into two classes, namely good prospective creditor (Performing Loan) and bad prospective creditor (Non Performing Loan) based on certain characteristics. The method often used for classifying creditor is logistic regression, but this method is less robust and less accurate than data mining. Thus, there is a need for methods that provide greater accuracy. Among the methods that have been proposed is a method called Boosting, which operates sequentially by applying a classification algorithm to the reweighted version of the training data set. This study uses 5 datasets. The first dataset is secondary data originating from data on non-subsidized homeownership creditors of Bank X Malang City. While the other datasets are simulation data with many samples of 10, 500, and 1000. The results of this study indicate that ensemble boosting logistic regression is more suitable for describing binary response problems, especially creditor classification because it provides more accurate information. For high-dimensional data, which is represented by a sample size of 10, ensemble logistic regression is proven to be able to produce fairly accurate predictions with an accuracy rate of up to 80%, whereas in the logistic regression analysis the model raises N.A because many samples < many independent variables. The use of boosting is preferred because it focuses on problems that are misclassified and have a tendency to increase to higher accuracy.

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The Estimation Function Approach Smoothing Spline Regression Analysis for Longitudinal Data

Cited by 3 publications

References 2 publications

Robust Regression Analysis Study for Data with Outliers at Some Significance Levels

Robust Regression Analysis Study for Data with Outliers at Some Significance Levels

The handling of people smuggling involving foreign nationals as efforts to safeguard Indonesian territories

Creditor Classification Logistic Regression Ensemble Boosting And Logistic Regression In Creditor Classification With Binary Response

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