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Journal articles on the topic 'Multivariate Adaptive Regression Splines'

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Published: 4 June 2021
Last updated: 1 February 2022

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1

Friedman, Jerome H. "Multivariate Adaptive Regression Splines." Annals of Statistics 19, no. 1 (March 1991): 1–67. http://dx.doi.org/10.1214/aos/1176347963.

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2

Barron, Andrew R., and Xiangyu Xiao. "Discussion: Multivariate Adaptive Regression Splines." Annals of Statistics 19, no. 1 (March 1991): 67–82. http://dx.doi.org/10.1214/aos/1176347964.

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3

Breiman, Leo. "Discussion: Multivariate Adaptive Regression Splines." Annals of Statistics 19, no. 1 (March 1991): 82–91. http://dx.doi.org/10.1214/aos/1176347965.

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4

Golubev, George K., and Rafael Z. Hasminskii. "Discussion: Multivariate Adaptive Regression Splines." Annals of Statistics 19, no. 1 (March 1991): 91–92. http://dx.doi.org/10.1214/aos/1176347966.

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5

Buja, Andreas, Diane Duffy, Trevor Hastie, and Robert Tibshirani. "Discussion: Multivariate Adaptive Regression Splines." Annals of Statistics 19, no. 1 (March 1991): 93–99. http://dx.doi.org/10.1214/aos/1176347967.

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6

O'Sullivan, Finbarr. "Discussion: Multivariate Adaptive Regression Splines." Annals of Statistics 19, no. 1 (March 1991): 99–102. http://dx.doi.org/10.1214/aos/1176347968.

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7

Owen, Art. "Discussion: Multivariate Adaptive Regression Splines." Annals of Statistics 19, no. 1 (March 1991): 102–12. http://dx.doi.org/10.1214/aos/1176347969.

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8

Schumaker, Larry L. "Discussion: Multivariate Adaptive Regression Splines." Annals of Statistics 19, no. 1 (March 1991): 112–13. http://dx.doi.org/10.1214/aos/1176347970.

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9

Stone, Charles J. "Discussion: Multivariate Adaptive Regression Splines." Annals of Statistics 19, no. 1 (March 1991): 113–15. http://dx.doi.org/10.1214/aos/1176347971.

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10

Gu, Chong, and Grace Wahba. "Discussion: Multivariate Adaptive Regression Splines." Annals of Statistics 19, no. 1 (March 1991): 115–23. http://dx.doi.org/10.1214/aos/1176347972.

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11

Friedman, Jerome H. "Rejoinder: Multivariate Adaptive Regression Splines." Annals of Statistics 19, no. 1 (March 1991): 123–41. http://dx.doi.org/10.1214/aos/1176347973.

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12

Karisma, Ria Dhea Layla Nur, Juhari Juhari, and Ramadani A Rosa. "Poverty in Central Java using Multivariate Adaptive Regression Splines and Bootstrap Aggregating Multivariate Adaptive Regression Splines." CAUCHY 6, no. 4 (May 30, 2021): 238–45. http://dx.doi.org/10.18860/ca.v6i4.10871.

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Population poverty 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 MARS 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 value in the Bagging MARS model is 0,009798721 and GCV value in the MARS model is 6,985571. The most influential variable poverty population of Central Java Province in 2018 in the MARS model is the percentage of the old school expectation rate (X9). Then, the most influential variable in the Bagging MARS model is the number of diarrhea (X1).
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13

Friedman, Jerome H., and Charles B. Roosen. "An introduction to multivariate adaptive regression splines." Statistical Methods in Medical Research 4, no. 3 (September 1995): 197–217. http://dx.doi.org/10.1177/096228029500400303.

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14

Crino, Scott, and Donald E. Brown. "Global Optimization With Multivariate Adaptive Regression Splines." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 37, no. 2 (April 2007): 333–40. http://dx.doi.org/10.1109/tsmcb.2006.883430.

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15

PUTRI, ARNEZDA, DODI DEVIANTO, and MAIYASTRI MAIYASTRI. "ANALISIS KINERJA KARYAWAN MENGGUNAKAN MULTIVARIATE ADAPTIVE REGRESSION SPLINES." Jurnal Matematika UNAND 9, no. 2 (June 29, 2020): 184. http://dx.doi.org/10.25077/jmu.9.2.184-191.2020.

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Kinerja karyawan merupakan indikator keberhasilan seseorang secara keseluruhan selama periode tertentu dalam melaksanakan tugas yang diberikan dibandingkan dengan standar hasil kerja yang telah ditentukan dan disepakati bersama. Apabila kinerja karyawan baik maka akan meningkatkan kualitas operasional suatu perusahaan, sebaliknya jika kinerja karyawan kurang baik maka suatu kegiatan operasional perusahaan tidak berjalan dengan maksimal. Oleh sebab itu, perlu dikaji pembentukan model analisis kinerja karyawan terbaik menggunakan metode Multivariate Adaptive Regression Spline (MARS). Metode MARS dapat digunakan pada data berdimensi tinggi dan dapat mengklasifikasikan permasalahan dengan respon biner yang sesuai dengan permasalahan analisa kinerja karyawan yaitu kinerja baik dan kinerja kurang baik. Berdasarkan hasil penelitian diperoleh model MARS terbaik dengan seluruh variabel yang digunakan yaitu kompensasi, budaya organisasi dan motivasi kerja berpengaruh dalam penentuan klasifikasi kinerja karyawan. Ketepatan klasifikasi model yaitu sebesar 98,67 % dan model yang diperoleh telah konsisten secara statistik. Kata Kunci: Kinerja, Multivariate Adaptive Regression Spline, Klasifikasi
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16

Zhang, Wengang, and A. T. C. Goh. "Nonlinear structural modeling using multivariate adaptive regression splines." Computers and Concrete 16, no. 4 (October 25, 2015): 569–85. http://dx.doi.org/10.12989/cac.2015.16.4.569.

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17

Durmaz, Murat, Mahmut Onur Karslioglu, and Metin Nohutcu. "Regional VTEC modeling with multivariate adaptive regression splines." Advances in Space Research 46, no. 2 (July 2010): 180–89. http://dx.doi.org/10.1016/j.asr.2010.02.030.

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18

Martinez, Diana L., Dachuan T. Shih, Victoria C. P. Chen, and Seoung Bum Kim. "A convex version of multivariate adaptive regression splines." Computational Statistics & Data Analysis 81 (January 2015): 89–106. http://dx.doi.org/10.1016/j.csda.2014.07.015.

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19

Mukhopadhyay, Tanmoy. "A multivariate adaptive regression splines based damage identification methodology for web core composite bridges including the effect of noise." Journal of Sandwich Structures & Materials 20, no. 7 (April 28, 2017): 885–903. http://dx.doi.org/10.1177/1099636216682533.

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A novel computationally efficient damage identification methodology for web core fiber-reinforced polymer composite bridges has been developed in this article based on multivariate adaptive regression splines in conjunction with a multi-objective goal-attainment optimization algorithm. The proposed damage identification methodology has been validated for several single and multiple damage cases. The performance of the efficient multivariate adaptive regression splines-based approach for the inverse system identification process is found to be quite satisfactory. An iterative scheme in conjunction with the multi-objective optimization algorithm coupled with multivariate adaptive regression splines is proposed to increase damage identification accuracy. The effect of noise on the proposed damage identification algorithm has also been addressed subsequently using a probabilistic framework. The multivariate adaptive regression splines-based damage identification algorithm is general in nature; therefore, in future it can be implemented to other structures.
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20

Motogaito, Hiroki, Tomoyuki Sugimoto, and Masashi Goto. "Multivariate Adaptive Regression Splines with Non-negative Garrote Estimator." Japanese journal of applied statistics 36, no. 2/3 (2007): 99–118. http://dx.doi.org/10.5023/jappstat.36.99.

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21

Mao, Hu Ping, Yi Zhong Wu, and Li Ping Chen. "Data Driven Multivariate Adaptive Regression Splines Based Simulation Optimization." Applied Mechanics and Materials 44-47 (December 2010): 3800–3806. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.3800.

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This paper proposes a data driven based optimization approach which combines augmented Lagrangian method, MARS with effective data processing. In the approach, an expensive simulation run is required if and only if a nearby data point does not exist in the cumulatively growing database. Over time the database matures and is enriched as more and more optimizations have been performed. MARS is a self-adaptive regression process, which fits in with the multidimensional problems, and uses a modified recursive partitioning strategy to simplify high-dimensional problems into smaller yet highly accurate models. Combining the local response surface of MARS and augmented Lagrangian method improve sequential approximation optimization and reduce simulation times by effective data processing, yet maintain a low computational cost. The approach is applied to a six dimensional test function, a ten dimensional engineering problem and a two dimensional global test functions to demonstrate its feasibility and convergence, and yet some limiting properties.
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22

Huang, Hong, Xiaoliang Ji, Fang Xia, Shuhui Huang, Xu Shang, Han Chen, Minghua Zhang, Randy A. Dahlgren, and Kun Mei. "Multivariate adaptive regression splines for estimating riverine constituent concentrations." Hydrological Processes 34, no. 5 (December 26, 2019): 1213–27. http://dx.doi.org/10.1002/hyp.13669.

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23

Sahraei, Mohammad Ali, Hakan Duman, Muhammed Yasin Çodur, and Ecevit Eyduran. "Prediction of transportation energy demand: Multivariate Adaptive Regression Splines." Energy 224 (June 2021): 120090. http://dx.doi.org/10.1016/j.energy.2021.120090.

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24

Zhang, Wengang, Anthony T. C. Goh, and Yanmei Zhang. "Multivariate Adaptive Regression Splines Application for Multivariate Geotechnical Problems with Big Data." Geotechnical and Geological Engineering 34, no. 1 (October 23, 2015): 193–204. http://dx.doi.org/10.1007/s10706-015-9938-9.

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25

Zhang, Wengang, and Anthony T. C. Goh. "Evaluating seismic liquefaction potential using multivariate adaptive regression splines and logistic regression." Geomechanics and Engineering 10, no. 3 (March 25, 2016): 269–84. http://dx.doi.org/10.12989/gae.2016.10.3.269.

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26

Kilinc, Betul Kan, Semra Malkoc, A. Savas Koparal, and Berna Yazici. "Using multivariate adaptive regression splines to estimate pollution in soil." International Journal of ADVANCED AND APPLIED SCIENCES 4, no. 2 (February 2017): 10–16. http://dx.doi.org/10.21833/ijaas.2017.02.002.

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27

Zhou, Yuming, and Hareton Leung. "Predicting object-oriented software maintainability using multivariate adaptive regression splines." Journal of Systems and Software 80, no. 8 (August 2007): 1349–61. http://dx.doi.org/10.1016/j.jss.2006.10.049.

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28

Deconinck, E., Q. S. Xu, R. Put, D. Coomans, D. L. Massart, and Y. Vander Heyden. "Prediction of gastro-intestinal absorption using multivariate adaptive regression splines." Journal of Pharmaceutical and Biomedical Analysis 39, no. 5 (October 2005): 1021–30. http://dx.doi.org/10.1016/j.jpba.2005.05.034.

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29

Chang, Li-yen, Hsing-chung Chu, Da-jie Lin, and Pei Lui. "Analysis of Freeway Accident Frequency using Multivariate Adaptive Regression Splines." Procedia Engineering 45 (2012): 824–29. http://dx.doi.org/10.1016/j.proeng.2012.08.245.

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30

Stoklosa, Jakub, and David I. Warton. "A Generalized Estimating Equation Approach to Multivariate Adaptive Regression Splines." Journal of Computational and Graphical Statistics 27, no. 1 (January 2, 2018): 245–53. http://dx.doi.org/10.1080/10618600.2017.1360780.

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31

Graczyk-Kucharska, Magdalena, Ayse Özmen, Maciej Szafrański, Gerhard Wilhelm Weber, Marek Golińśki, and Małgorzata Spychała. "Knowledge accelerator by transversal competences and multivariate adaptive regression splines." Central European Journal of Operations Research 28, no. 2 (July 23, 2019): 645–69. http://dx.doi.org/10.1007/s10100-019-00636-x.

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32

Zhang, W. G., and A. T. C. Goh. "Multivariate adaptive regression splines for analysis of geotechnical engineering systems." Computers and Geotechnics 48 (March 2013): 82–95. http://dx.doi.org/10.1016/j.compgeo.2012.09.016.

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33

Xu, Q. S., M. Daszykowski, B. Walczak, F. Daeyaert, M. R. de Jonge, J. Heeres, L. M. H. Koymans, et al. "Multivariate adaptive regression splines—studies of HIV reverse transcriptase inhibitors." Chemometrics and Intelligent Laboratory Systems 72, no. 1 (June 2004): 27–34. http://dx.doi.org/10.1016/j.chemolab.2004.02.007.

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34

HAGHIABI, AMIR HAMZEH. "Prediction of longitudinal dispersion coefficient using multivariate adaptive regression splines." Journal of Earth System Science 125, no. 5 (July 2016): 985–95. http://dx.doi.org/10.1007/s12040-016-0708-8.

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35

CHENG, Min-Yuan, and Minh-Tu CAO. "ESTIMATING STRENGTH OF RUBBERIZED CONCRETE USING EVOLUTIONARY MULTIVARIATE ADAPTIVE REGRESSION SPLINES." JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 22, no. 5 (May 17, 2016): 711–20. http://dx.doi.org/10.3846/13923730.2014.897989.

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This study proposes an artificial intelligence (AI) model to predict the compressive strength and splitting tensile strength of rubberized concrete. This Evolutionary Multivariate Adaptive Regression Splines (EMARS) model is a hybrid of the Multivariate Adaptive Regression Splines (MARS) and Artificial Bee Colony (ABC) within which MARS addresses learning and curve fitting and ABC implements optimization to determine the fittest parameter settings with minimal prediction error. K-fold cross validation was utilized to compare EMARS performance against four other benchmark data mining techniques including MARS, Back-propagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), and Genetic Programming (GP). Comparison results showed EMARS to be the best model for predicting rubberized concrete strength and study results demonstrated EMARS as a reliable tool for civil engineers in the concrete construction industry.
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36

Widagdo, Nurhaerunisa, Muhammad Kasim Aidid, and S. Sudarmin. "Multivariate Adaptive Regression Splines pada Kasus Inflasi di Indonesia Tahun 2005-2018." VARIANSI: Journal of Statistics and Its application on Teaching and Research 2, no. 3 (August 7, 2020): 110. http://dx.doi.org/10.35580/variansiunm14639.

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Abstrak. Kegiatan perekonomian suatu negara dipengaruhi oleh inflasi yang terjadi pada negara tersebut. Tingkat inflasi Indonesia yang fluktuatif, cenderung tidak stabil, mempengaruhi kehidupan sosial dan ekonomi masyarakat. Sehingga penting untuk mengetahui faktor-faktor yang berpengaruh terhadap inflasi serta pemodelan faktor-faktor berpengaruh tersebut dan hubungannya terhadap inflasi. Mengidentifikasi hubungan inflasi dan faktor penyebabnya dilakukan menggunakan pemodelan Multivariate Adaptive Regression Splines (MARS). MARS merupakan jenis regeresi nonparametrik yang menggabungkan prinsip Recursive Partitioning Regression (RPR) dan spline, fleksibel dalam memodelkan data sehingga memberikan hasil pemodelan data yang cukup akurat serta dapat menangani data berdimensi tinggi, yaitu data dengan jumlah peubah prediktor 3 ≤ x ≤ 20 dan ukuran data sampel 50 ≤ n ≤ 1000. Model MARS diperoleh berdasarkan kombinasi nilai BF, MI, dan MO yang memiliki nilai Generalized Cross Validation (GCV) terkecil. Pada penelitian ini digunakan enam peubah prediktor sebagai faktor yang mempengaruhi inflasi dengan data sampel sebesar 168 sampel. Hasil penelitian menunjukkan bahwa peubah Indeks Harga Perdagangan Besar (IHPB), BI Rate, Nilai Tukar IDR-USD, dan Uang Beredar adalah faktor-faktor yang berpengaruh terhadap inflasi berdasarkan model terbaik MARS dengan BF=24, MI=3, MO=1, GCV=0,772, MSE=0,391, dan R2=0,968.Kata kunci: Inflasi, MARS, RPR, BF, MI, MO, GCV.
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37

Guzmán, Dani, Francisco Javier de Cos Juez, Fernando Sánchez Lasheras, Richard Myers, and Laura Young. "Deformable mirror model for open-loop adaptive optics using multivariate adaptive regression splines." Optics Express 18, no. 7 (March 15, 2010): 6492. http://dx.doi.org/10.1364/oe.18.006492.

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38

Li, Zhuokai, Hai Liu, and Wanzhu Tu. "Model selection in multivariate semiparametric regression." Statistical Methods in Medical Research 27, no. 10 (February 6, 2017): 3026–38. http://dx.doi.org/10.1177/0962280217690769.

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Variable selection in semiparametric mixed models for longitudinal data remains a challenge, especially in the presence of multiple correlated outcomes. In this paper, we propose a model selection procedure that simultaneously selects fixed and random effects using a maximum penalized likelihood method with the adaptive least absolute shrinkage and selection operator penalty. Through random effects selection, we determine the correlation structure among multiple outcomes and therefore address whether a joint model is necessary. Additionally, we include a bivariate nonparametric component, as approximated by tensor product splines, to accommodate the joint nonlinear effects of two independent variables. We use an adaptive group least absolute shrinkage and selection operator to determine whether the bivariate nonparametric component can be reduced to additive components. To implement the selection and estimation method, we develop a two-stage expectation-maximization procedure. The operating characteristics of the proposed method are assessed through simulation studies. Finally, the method is illustrated in a clinical study of blood pressure development in children.
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39

Safer, Alan M. "Multivariate adaptive regression splines and insider trading data for stock prediction." Journal of Interdisciplinary Mathematics 7, no. 1 (January 2004): 79–93. http://dx.doi.org/10.1080/09720502.2004.10700360.

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40

C.-C. Yang, S. O. Prasher, R. Lacroix, and S. H. Kim. "APPLICATION OF MULTIVARIATE ADAPTIVE REGRESSION SPLINES (MARS) TO SIMULATE SOIL TEMPERATURE." Transactions of the ASAE 47, no. 3 (2004): 881–87. http://dx.doi.org/10.13031/2013.16085.

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41

Haghiabi, Amir Hamzeh. "Prediction of River Pipeline Scour Depth Using Multivariate Adaptive Regression Splines." Journal of Pipeline Systems Engineering and Practice 8, no. 1 (February 2017): 04016015. http://dx.doi.org/10.1061/(asce)ps.1949-1204.0000248.

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42

Mohamed Amin, Muhalim, Anazida Zainal, Nurulhuda Firdaus Mohd. Azmi, and Nor Azizah Ali. "Feature Selection Using Multivariate Adaptive Regression Splines in Telecommunication Fraud Detection." IOP Conference Series: Materials Science and Engineering 864 (July 10, 2020): 012059. http://dx.doi.org/10.1088/1757-899x/864/1/012059.

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43

Lewis, P. A. W., and J. G. Stevens. "Nonlinear Modeling of Time Series Using Multivariate Adaptive Regression Splines (MARS)." Journal of the American Statistical Association 86, no. 416 (December 1991): 864–77. http://dx.doi.org/10.1080/01621459.1991.10475126.

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44

Msilini, A., P. Masselot, and T. B. M. J. Ouarda. "Regional Frequency Analysis at Ungauged Sites with Multivariate Adaptive Regression Splines." Journal of Hydrometeorology 21, no. 12 (December 2020): 2777–92. http://dx.doi.org/10.1175/jhm-d-19-0213.1.

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AbstractHydrological systems are naturally complex and nonlinear. A large number of variables, many of which not yet well considered in regional frequency analysis (RFA), have a significant impact on hydrological dynamics and consequently on flood quantile estimates. Despite the increasing number of statistical tools used to estimate flood quantiles at ungauged sites, little attention has been dedicated to the development of new regional estimation (RE) models accounting for both nonlinear links and interactions between hydrological and physio-meteorological variables. The aim of this paper is to simultaneously take into account nonlinearity and interactions between variables by introducing the multivariate adaptive regression splines (MARS) approach in RFA. The predictive performances of MARS are compared with those obtained by one of the most robust RE models: the generalized additive model (GAM). Both approaches are applied to two datasets covering 151 hydrometric stations in the province of Quebec (Canada): a standard dataset (STA) containing commonly used variables and an extended dataset (EXTD) combining STA with additional variables dealing with drainage network characteristics. Results indicate that RE models using MARS with the EXTD outperform slightly RE models using GAM. Thus, MARS seems to allow for a better representation of the hydrological process and an increased predictive power in RFA.
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45

Mao, Hu-ping, Yi-zhong Wu, and Li-ping Chen. "Multivariate adaptive regression splines based simulation optimization using move-limit strategy." Journal of Shanghai University (English Edition) 15, no. 6 (December 2011): 542–47. http://dx.doi.org/10.1007/s11741-011-0783-2.

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46

Nguyen-Cong, V., G. Van Dang, and BM Rode. "Using multivariate adaptive regression splines to QSAR studies of dihydroartemisinin derivatives." European Journal of Medicinal Chemistry 31, no. 10 (January 1996): 797–803. http://dx.doi.org/10.1016/0223-5234(96)83973-0.

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47

Durmaz, Murat, and Mahmut Onur Karslioğlu. "Non-parametric regional VTEC modeling with Multivariate Adaptive Regression B-Splines." Advances in Space Research 48, no. 9 (November 2011): 1523–30. http://dx.doi.org/10.1016/j.asr.2011.06.031.

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48

Cheng, Min-Yuan, and Minh-Tu Cao. "Accurately predicting building energy performance using evolutionary multivariate adaptive regression splines." Applied Soft Computing 22 (September 2014): 178–88. http://dx.doi.org/10.1016/j.asoc.2014.05.015.

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49

Rahmatian, Matin, Yu Christine Chen, Atefeh Palizban, Ali Moshref, and William G. Dunford. "Transient stability assessment via decision trees and multivariate adaptive regression splines." Electric Power Systems Research 142 (January 2017): 320–28. http://dx.doi.org/10.1016/j.epsr.2016.09.030.

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50

Miguéis, V. L., Ana Camanho, and João Falcão e Cunha. "Customer attrition in retailing: An application of Multivariate Adaptive Regression Splines." Expert Systems with Applications 40, no. 16 (November 2013): 6225–32. http://dx.doi.org/10.1016/j.eswa.2013.05.069.

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