Multivariate Adaptive Regression Splines Modeling for Household Food Security in Central Borneo Province 2017

This study aims to make a model household food security with social, economic and demographic variables in Central Borneo Province using nonparametric regression, Multivariate Adaptive Regression Splines (MARS). The source of data was 2017 Socioeconomic Survey (SUSENAS) from Central Bureau of Statistics. The model shows that all variables have significant to affect household food security with importance level: area type (100%), age (99.47%), the amount of people in a household (88.57%), access to credit (81.01%), head of household education (19.74%), and gender (11.08%). In addition, there are four basic functions with no variable interaction, two basic functions with two variable interactions and two basic functions with three variable interactions on the MARS model specification

Multivariate adaptive regression splines for estimating riverine constituent concentrations

Regression-based methods are commonly used for riverine constituent concentration/flux estimation, which is essential for guiding water quality protection practices and environmental decision making. This paper developed a multivariate adaptive regression splines model for estimating riverine constituent concentrations (MARS-EC). The process, interpretability and flexibility of the MARS-EC modelling approach, was demonstrated for total nitrogen in the Patuxent River, a major river input to Chesapeake Bay. Model accuracy and uncertainty of the MARS-EC approach was further analysed using nitrate plus nitrite datasets from eight tributary rivers to Chesapeake Bay. Results showed that the MARS-EC approach integrated the advantages of both parametric and nonparametric regression methods, and model accuracy was demonstrated to be superior to the traditionally used ESTIMATOR model. MARS-EC is flexible and allows consideration of auxiliary variables; the variables and interactions can be selected automatically. MARS-EC does not constrain concentration-predictor curves to be constant but rather is able to identify shifts in these curves from mathematical expressions and visual graphics. The MARS-EC approach provides an effective and complementary tool along with existing approaches for estimating riverine constituent concentrations

Analisis Survival dengan Pendekatan Multivariate Adaptive Regression Splines pada Kasus Demam Berdarah Dengue (DBD)

Indonesia merupakan negara beriklim tropis de-ngan jumlah penduduk padat yang disertai dengan tingkat pen-didikan rata-rata yang masih rendah, mengakibatkan rendahnya pula pengetahuan tentang sanitasi yang baik sehingga dapat memunculkan penyakit yang memicu Kejadian Luar Biasa (KLB) di Indonesia, salah satunya adalah Demam Berdarah Dengue (DBD). Indonesia merupakan Negara dengan jumlah kejadian DBD tertinggi di Asia Tenggara, sehingga DBD menjadi salah satu obyek penelitian yang menarik untuk dikaji. Penelitian ini membahas tentang analisis survival dan faktor-faktor yang mempengaruhi laju kesembuhan pasien DBD dengan pendekatan Multivariate Adaptive Regression Splines (MARS) berdasarkan data rekam medis pasien rawat inap DBD di Kabupaten Gresik. Hasil penelitian menunjukkan bahwa proporsi kejadian DBD pada laki-laki lebih tinggi daripada perempuan, dan faktor-faktor yang mempengaruhi laju ke-sembuhan pasien DBD adalah umur, kadar hematokrit, kejadian perbesaran hati, dan jumlah trombosit. Interaksi antar variabel yang mempengaruhi laju kesembuhan pasien antara lain adalah adalah interaksi antara kadar hematokrit dengan kejadian perbesaran hati, interkasi antara umur, kejadian perbesaran hati, dan kadar hematokrit, interaksi antara kadar hematokrit dengan jumlah trombosit, serta interaksi antara umur, kadar hematokrit, dan kejadian perbesaran hati

Multivariate adaptive regression splines and bootstrap aggregating multivariate adaptive regression splines of poverty in Central Java

Poverty population is one of the serious problems in Indonesia. The percentage of population poverty used as a means for a statistical instrument to be guidelines to create standard policies and evaluations to reduce poverty. The aims of the research are to determine model population poverty using Multivariate Adaptive Regression Spline and Bagging MARS then to understand the most influence variable population poverty of Central Java Province in 2018. The result of this research is the Bagging MARS model showed better accuracy than the MARS model. Since, GCV in the Bagging MARS model is 0,009798721 and GCV in the MARS model is 6,985571. The most influence variable population poverty of Central Java Province in 2018 based on MARS model is the percentage of the old school expectation rate. Then, the most influentce variable based on Bagging MARS model is the number of diarrhea diseas

Multivariate adaptive regression splines models for vehicular emission prediction

© 2015, Oduro et al. Background: Rate models for predicting vehicular emissions of nitrogen oxides (NO X) are insensitive to the vehicle modes of operation, such as cruise, acceleration, deceleration and idle, because these models are usually based on the average trip speed. This study demonstrates the feasibility of using other variables such as vehicle speed, acceleration, load, power and ambient temperature to predict (NO X) emissions to ensure that the emission inventory is accurate and hence the air quality modelling and management plans are designed and implemented appropriately. Methods: We propose to use the non-parametric Boosting-Multivariate Adaptive Regression Splines (B-MARS) algorithm to improve the accuracy of the Multivariate Adaptive Regression Splines (MARS) modelling to effectively predict NO X emissions of vehicles in accordance with on-board measurements and the chassis dynamometer testing. The B-MARS methodology is then applied to the NO X emission estimation. Results: The model approach provides more reliable results of the estimation and offers better predictions of NO X emissions. Conclusion: The results therefore suggest that the B-MARS methodology is a useful and fairly accurate tool for predicting NO X emissions and it may be adopted by regulatory agencies

Analysis of earthquake hazards prediction with multivariate adaptive regression splines

Earthquake research has not yielded promising results, either in the form of causes or revealing the timing of their future events. Many methods have been developed, one of which is related to data mining, such as the use of hybrid neural networks, support vector regressor, fuzzy modeling, clustering, and others. Earthquake research has uncertain parameters and to obtain optimal results an appropriate method is needed. In general, several predictive data mining methods are grouped into two categories, namely parametric and non-parametric. This study uses a non-parametric method with multivariate adaptive regression spline (MARS) and conic multivariate adaptive regression spline (CMARS) as the backward stage of the MARS algorithm. The results of this study after parameter testing and analysis obtained a mathematical model with 16 basis functions (BF) and 12 basis functions contributing to the model and 4 basis functions not contributing to the model. Based on the level of variable contribution, it can be written that the epicenter distance is 100 percent, the magnitude is 31.1 percent, the location temperature is 5.5 percent, and the depth is 3.5 percent. It can be concluded that the results of the prediction analysis of areas in Lombok with the highest earthquake hazard level are Malaka, Genggelang, Pemenang, Tanjung, Tegal Maja, Senggigi, Mangsit. Meninting, and Malimbu

Intrusion Detection Systems Using Adaptive Regression Splines

Past few years have witnessed a growing recognition of intelligent techniques for the construction of efficient and reliable intrusion detection systems. Due to increasing incidents of cyber attacks, building effective intrusion detection systems (IDS) are essential for protecting information systems security, and yet it remains an elusive goal and a great challenge. In this paper, we report a performance analysis between Multivariate Adaptive Regression Splines (MARS), neural networks and support vector machines. The MARS procedure builds flexible regression models by fitting separate splines to distinct intervals of the predictor variables. A brief comparison of different neural network learning algorithms is also given

Data mining in real estate appraisal: a model tree and multivariate adaptive regression spline approach

In this paper we adopt two exploratory modelling techniques: Model Trees and Multivariate Adaptive Regression Splines. The objective is the building of two sale price prediction models in order to highlight possible market segments not detectable a priori. We show how these novel procedures can help to understand complex patterns and interactions among predictors in real estate appraisal