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Journal articles on the topic 'Regresión de Ridge'

Author: Grafiati

Published: 17 September 2021

Last updated: 18 February 2022

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1

Pérez-Planells, Ll, J. Delegido, J. P. Rivera-Caicedo, and J. Verrelst. "Análisis de métodos de validación cruzada para la obtención robusta de parámetros biofísicos." Revista de Teledetección, no. 44 (December 22, 2015): 55. http://dx.doi.org/10.4995/raet.2015.4153.

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<p class="Bodytext">Los métodos de regresión no paramétricos son una gran herramienta estadística para obtener parámetros biofísicos a partir de medidas realizadas mediante teledetección. Pero los resultados obtenidos se pueden ver afectados por los datos utilizados en la fase de entrenamiento del modelo. Para asegurarse de que los modelos son robustos, se hace uso de varias técnicas de validación cruzada. Estas técnicas permiten evaluar el modelo con subconjuntos de la base de datos de campo. Aquí, se evalúan dos tipos de validación cruzada en el desarrollo de modelos de regresión no paramétricos: hold-out y k-fold. Los métodos de regresión lineal seleccionados fueron: Linear Regression (LR) y Partial Least Squares Regression (PLSR). Y los métodos no lineales: Kernel Ridge Regression (KRR) y Gaussian Process Regression (GPR). Los resultados de la validación cruzada mostraron que LR ofrece los resultados más inestables, mientras KRR y GPR llevan a resultados más robustos. Este trabajo recomienda utilizar algoritmos de regresión no lineales (como KRR o GPR) combinando con la validación cruzada k-fold con un valor de k igual a 10 para hacer la estimación de una manera robusta.</p>

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2

McDonald, Gary C. "Ridge regression." Wiley Interdisciplinary Reviews: Computational Statistics 1, no. 1 (July 2009): 93–100. http://dx.doi.org/10.1002/wics.14.

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3

Fearn, Tom. "Ridge Regression." NIR news 24, no. 3 (May 2013): 18–19. http://dx.doi.org/10.1255/nirn.1365.

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4

Rashid, Khasraw A., and Hanaw A. Amin. "Ridge Estimates of Regression Coefficients for SoilMoisture Retention of IraqiSoils." Journal of Zankoy Sulaimani - Part A 18, no. 3 (February 25, 2016): 85–98. http://dx.doi.org/10.17656/jzs.10537.

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5

Dorugade, A. V. "New ridge parameters for ridge regression." Journal of the Association of Arab Universities for Basic and Applied Sciences 15, no. 1 (April 2014): 94–99. http://dx.doi.org/10.1016/j.jaubas.2013.03.005.

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6

Munroe, Jeffrey S. "Ground Penetrating Radar Investigation of Late Pleistocene Shorelines of Pluvial Lake Clover, Elko County, Nevada, USA." Quaternary 3, no. 1 (March 20, 2020): 9. http://dx.doi.org/10.3390/quat3010009.

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Beach ridges constructed by pluvial Lake Clover in Elko County, Nevada during the Late Pleistocene were investigated with ground-penetrating radar (GPR). The primary objective was to document the internal architecture of these shorelines and to evaluate whether they were constructed during lake rise or fall. GPR data were collected with a ground-coupled 400-Mhz antenna and SIR-3000 controller. To constrain the morphology of the ridges, detailed topographic surveys were collected with a Topcon GTS-235W total station referenced to a second class 0 vertical survey point. GPR transects crossed the beach ridge built by Lake Clover at its highstand of 1725 m, along with seven other ridges down to the lowest beach at 1712 m. An average dielectric permittivity of 5.0, typical for dry sand and gravel, was calculated from GPR surveys in the vicinity of hand-excavations that encountered prominent stratigraphic discontinuities at known depths. Assuming this value, consistent radar signals were returned to a depth of ~3 m. Beach ridges are resolvable as ~90 to 150-cm thick stratified packages of gravelly sand overlying a prominent lakeward-dipping reflector, interpreted as the pre-lake land surface. Many ridges contain a package of sediment resembling a buried berm at their core, typically offset in a landward direction from the geomorphic crest of the beach ridge. Sequences of lakeward-dipping reflectors are resolvable beneath the beach face of all ridges. No evidence was observed to indicate that beach ridges were submerged by higher water levels after their formation. Instead, the GPR data are consistent with a model of sequential ridge formation during a monotonic lake regression.

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7

de Boer, Paul M. C., and Christian M. Hafner. "Ridge regression revisited." Statistica Neerlandica 59, no. 4 (October 13, 2005): 498–505. http://dx.doi.org/10.1111/j.1467-9574.2005.00304.x.

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8

Hertz, D. "Sequential ridge regression." IEEE Transactions on Aerospace and Electronic Systems 27, no. 3 (May 1991): 571–74. http://dx.doi.org/10.1109/7.81440.

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9

Tutz, Gerhard, and Harald Binder. "Boosting ridge regression." Computational Statistics & Data Analysis 51, no. 12 (August 2007): 6044–59. http://dx.doi.org/10.1016/j.csda.2006.11.041.

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10

Sundberg, Rolf. "Continuum Regression and Ridge Regression." Journal of the Royal Statistical Society: Series B (Methodological) 55, no. 3 (July 1993): 653–59. http://dx.doi.org/10.1111/j.2517-6161.1993.tb01930.x.

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11

Vigneau, E., M. F. Devaux, E. M. Qannari, and P. Robert. "Principal component regression, ridge regression and ridge principal component regression in spectroscopy calibration." Journal of Chemometrics 11, no. 3 (May 1997): 239–49. http://dx.doi.org/10.1002/(sici)1099-128x(199705)11:3<239::aid-cem470>3.0.co;2-a.

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12

Yin, Xiao Cui. "Comparison between Several Regression Models." Applied Mechanics and Materials 530-531 (February 2014): 601–4. http://dx.doi.org/10.4028/www.scientific.net/amm.530-531.601.

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This paper study ordinary linear regression and ridge regression, ridge regression includes ordinary ridge regression (ORR) and generalized ridge regression (GRR). Comparison between these methods are made by an example, the results show that ridge regression has smaller standard deviation and MSE than OLS, and among all the methods, GRR is better than others.

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13

YAVUZ, Arzu Altın. "A New Modification of Ridge Parameter for Regression Problems: A Monte Carlo Simulation Study." Turkiye Klinikleri Journal of Biostatistics 11, no. 3 (2019): 173–88. http://dx.doi.org/10.5336/biostatic.2019-66024.

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14

Dube, Madhulika, and Isha Isha. "A Review on the Biasing Parameters of Ridge Regression Estimator in LRM." MATHEMATICAL JOURNAL OF INTERDISCIPLINARY SCIENCES 3, no. 1 (September 1, 2014): 73–82. http://dx.doi.org/10.15415/mjis.2014.31007.

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15

Sari, Fitri Mudia, Khairil Anwar Notodiputro, and Bagus Sartono. "ANALISIS TINGKAT KEMISKINAN DI PROVINSI SUMATERA BARAT MELALUI PENDEKATAN REGRESI TERKENDALA (RIDGE REGRESSION, LASSO, DAN ELASTIC NET)." STATISTIKA Journal of Theoretical Statistics and Its Applications 21, no. 1 (June 28, 2021): 29–36. http://dx.doi.org/10.29313/jstat.v21i1.7836.

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Pandemi Covid-19 yang mulai menyerang Indonesia semenjak Maret 2020 menyebabkan krisis ekonomi dan sosial di Indonesia, termasuk Sumatera Barat. Data BPS Sumatera Barat menyebutkan bahwa jumlah penduduk miskin bertambah sebanyak 20.056, dari 344.023 orang pada Maret 2020, menjadi 364.079 pada September 2020. Masalah kemiskinan merujuk pada konsep high dimensional data yang melibatkan banyak peubah sehingga digunakan Regresi Ridge, LASSO, dan Elastic Net yang dapat mengatasi masalah multikolinieritas. Penelitian ini bertujuan untuk melihat peubah yang memiliki pengaruh yang penting terhadap tingkat kemiskinan di Sumatera Barat menggunakan model terbaik yang terpilih dari Regresi Ridge, LASSO, dan Elastic Net. Hasil penelitian menunjukkan bahwa tingkat buta huruf merupakan peubah penting yang mempengaruhi tingkat kemiskinan di Sumatera Barat dengan model terbaik yaitu Regresi Ridge.

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16

Türkan, Semra, and Öniz Toktamış. "Detection of influential observations in ridge regression and modified ridge regression." Model Assisted Statistics and Applications 7, no. 2 (April 9, 2012): 91–97. http://dx.doi.org/10.3233/mas-2011-0215.

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17

Özkale, M. Revan, Stanley Lemeshow, and Rodney Sturdivant. "Logistic regression diagnostics in ridge regression." Computational Statistics 33, no. 2 (July 29, 2017): 563–93. http://dx.doi.org/10.1007/s00180-017-0755-x.

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18

Draper, Norman R., and Agnes M. Herzberg. "A Ridge-Regression Sidelight." American Statistician 41, no. 4 (November 1987): 282. http://dx.doi.org/10.2307/2684750.

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19

Aldrin, Magne. "Length modified ridge regression." Computational Statistics & Data Analysis 25, no. 4 (September 1997): 377–98. http://dx.doi.org/10.1016/s0167-9473(97)00015-7.

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20

Draper, Norman R., and Agnes M. Herzberg. "A Ridge-Regression Sidelight." American Statistician 41, no. 4 (November 1987): 282–83. http://dx.doi.org/10.1080/00031305.1987.10475504.

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21

Cawley, Gavin C., Nicola L. C. Talbot, Robert J. Foxall, Stephen R. Dorling, and Danilo P. Mandic. "Heteroscedastic kernel ridge regression." Neurocomputing 57 (March 2004): 105–24. http://dx.doi.org/10.1016/j.neucom.2004.01.005.

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22

McDonald, Gary C. "Tracing ridge regression coefficients." Wiley Interdisciplinary Reviews: Computational Statistics 2, no. 6 (September 29, 2010): 695–703. http://dx.doi.org/10.1002/wics.126.

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23

HAITOVSKY, YOEL. "On multivariate ridge regression." Biometrika 74, no. 3 (1987): 563–70. http://dx.doi.org/10.1093/biomet/74.3.563.

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24

Khalaf, G., Kristofer Månsson, and Ghazi Shukur. "Modified Ridge Regression Estimators." Communications in Statistics - Theory and Methods 42, no. 8 (April 15, 2013): 1476–87. http://dx.doi.org/10.1080/03610926.2011.593285.

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25

Choi, Seung Hoe, Hye-Young Jung, and Hyoshin Kim. "Ridge Fuzzy Regression Model." International Journal of Fuzzy Systems 21, no. 7 (July 17, 2019): 2077–90. http://dx.doi.org/10.1007/s40815-019-00692-0.

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26

Khurshid, Shadan H. "Prediction of Crack Porosity from Other Easily Soil Properties Using Ridge Regression Analysis." Journal of Zankoy Sulaimani - Part A 22, no. 1 (January 30, 2020): 159–68. http://dx.doi.org/10.17656/jzs.10782.

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27

Liu, Xu-Qing, and Feng Gao. "Linearized Ridge Regression Estimator in Linear Regression." Communications in Statistics - Theory and Methods 40, no. 12 (April 8, 2011): 2182–92. http://dx.doi.org/10.1080/03610921003746693.

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28

Dorugade, Ashok V. "On Comparison of Some Ridge Parameters in Ridge Regression." Sri Lankan Journal of Applied Statistics 15, no. 1 (April 5, 2014): 31. http://dx.doi.org/10.4038/sljastats.v15i1.6792.

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29

Arashi, M., M. Roozbeh, N. A. Hamzah, and M. Gasparini. "Ridge regression and its applications in genetic studies." PLOS ONE 16, no. 4 (April 8, 2021): e0245376. http://dx.doi.org/10.1371/journal.pone.0245376.

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With the advancement of technology, analysis of large-scale data of gene expression is feasible and has become very popular in the era of machine learning. This paper develops an improved ridge approach for the genome regression modeling. When multicollinearity exists in the data set with outliers, we consider a robust ridge estimator, namely the rank ridge regression estimator, for parameter estimation and prediction. On the other hand, the efficiency of the rank ridge regression estimator is highly dependent on the ridge parameter. In general, it is difficult to provide a satisfactory answer about the selection for the ridge parameter. Because of the good properties of generalized cross validation (GCV) and its simplicity, we use it to choose the optimum value of the ridge parameter. The GCV function creates a balance between the precision of the estimators and the bias caused by the ridge estimation. It behaves like an improved estimator of risk and can be used when the number of explanatory variables is larger than the sample size in high-dimensional problems. Finally, some numerical illustrations are given to support our findings.

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30

Xin, Seng Jia, and Kamil Khalid. "Modelling House Price Using Ridge Regression and Lasso Regression." International Journal of Engineering & Technology 7, no. 4.30 (November 30, 2018): 498. http://dx.doi.org/10.14419/ijet.v7i4.30.22378.

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House price prediction is important for the government, finance company, real estate sector and also the house owner. The data of the house price at Ames, Iowa in United State which from the year 2006 to 2010 is used for multivariate analysis. However, multicollinearity is commonly occurred in the multivariate analysis and gives a serious effect to the model. Therefore, in this study investigates the performance of the Ridge regression model and Lasso regression model as both regressions can deal with multicollinearity. Ridge regression model and Lasso regression model are constructed and compared. The root mean square error (RMSE) and adjusted R-squared are used to evaluate the performance of the models. This comparative study found that the Lasso regression model is performing better compared to the Ridge regression model. Based on this analysis, the selected variables includes the aspect of house size, age of house, condition of house and also the location of the house.

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31

Грицюк, Вера Ильинична. "Improved robust ridge regression estimates." Eastern-European Journal of Enterprise Technologies 1, no. 9(73) (February 25, 2015): 53. http://dx.doi.org/10.15587/1729-4061.2015.37316.

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32

Toker, Selma, Gülesen Üstündağ Şiray, and Selahattin Kaçıranlar. "Inequality constrained ridge regression estimator." Statistics & Probability Letters 83, no. 10 (October 2013): 2391–98. http://dx.doi.org/10.1016/j.spl.2013.06.023.

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33

Shi, Lei, and Xueren Wang. "Local influence in ridge regression." Computational Statistics & Data Analysis 31, no. 3 (September 1999): 341–53. http://dx.doi.org/10.1016/s0167-9473(99)00019-5.

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34

Lee, A. H., and M. J. Silvapulle. "Ridge estimation in logistic regression." Communications in Statistics - Simulation and Computation 17, no. 4 (January 1988): 1231–57. http://dx.doi.org/10.1080/03610918808812723.

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35

Jensen, D. R., and D. E. Ramirez. "Concentration reversals in ridge regression." Statistics & Probability Letters 79, no. 21 (November 2009): 2237–41. http://dx.doi.org/10.1016/j.spl.2009.07.022.

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36

Morris, John D. "Calculating a Stepwise Ridge Regression." Educational and Psychological Measurement 46, no. 1 (March 1986): 151–55. http://dx.doi.org/10.1177/0013164486461014.

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37

Walker, Esteban, and Jeffrey B. Birch. "Influence Measures in Ridge Regression." Technometrics 30, no. 2 (May 1988): 221–27. http://dx.doi.org/10.1080/00401706.1988.10488370.

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38

Hoerl, Roger W. "Ridge Regression: A Historical Context." Technometrics 62, no. 4 (October 1, 2020): 420–25. http://dx.doi.org/10.1080/00401706.2020.1742207.

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39

Khalaf, Ghadban, and Mohamed Iguernane. "Ridge Regression and Ill-Conditioning." Journal of Modern Applied Statistical Methods 13, no. 2 (November 1, 2014): 355–63. http://dx.doi.org/10.22237/jmasm/1414815420.

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40

Cessie, S. Le, and J. C. Van Houwelingen. "Ridge Estimators in Logistic Regression." Applied Statistics 41, no. 1 (1992): 191. http://dx.doi.org/10.2307/2347628.

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41

Guerard, John B. "Composite forecasting using ridge regression." Communications in Statistics - Theory and Methods 16, no. 4 (January 1987): 937–52. http://dx.doi.org/10.1080/03610928708829414.

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42

Jensen, Donald R., and Donald E. Ramirez. "Anomalies in Ridge Regression: Rejoinder." International Statistical Review 78, no. 2 (June 18, 2010): 215–17. http://dx.doi.org/10.1111/j.1751-5823.2010.00116.x.

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43

Clark, A. E., and C. G. Troskie. "Ridge Regression – A Simulation Study." Communications in Statistics - Simulation and Computation 35, no. 3 (September 2006): 605–19. http://dx.doi.org/10.1080/03610910600716811.

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44

Kizilaslan, Busenur, Erol Egrioglu, and Atif Ahmet Evren. "Intuitionistic fuzzy ridge regression functions." Communications in Statistics - Simulation and Computation 49, no. 3 (June 13, 2019): 699–708. http://dx.doi.org/10.1080/03610918.2019.1626887.

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45

Zinodiny, S. "Bayes minimax ridge regression estimators." Communications in Statistics - Theory and Methods 47, no. 22 (March 7, 2018): 5519–33. http://dx.doi.org/10.1080/03610926.2017.1397167.

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46

Chandrasekhar, C. K., H. Bagyalakshmi, M. R. Srinivasan, and M. Gallo. "Partial ridge regression under multicollinearity." Journal of Applied Statistics 43, no. 13 (May 8, 2016): 2462–73. http://dx.doi.org/10.1080/02664763.2016.1181726.

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47

Pagel, Mark D., and Clifford E. Lunneborg. "Empirical evaluation of ridge regression." Psychological Bulletin 97, no. 2 (1985): 342–55. http://dx.doi.org/10.1037/0033-2909.97.2.342.

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48

Khalaf, G., K. Månsson, P. Sjölander, and G. Shukur. "A Tobit Ridge Regression Estimator." Communications in Statistics - Theory and Methods 43, no. 1 (November 25, 2013): 131–40. http://dx.doi.org/10.1080/03610926.2012.655881.

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49

Chawla, J. S. "A note on ridge regression." Statistics & Probability Letters 9, no. 4 (April 1990): 343–45. http://dx.doi.org/10.1016/0167-7152(90)90144-v.

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50

Månsson, Kristofer, and Ghazi Shukur. "A Poisson ridge regression estimator." Economic Modelling 28, no. 4 (July 2011): 1475–81. http://dx.doi.org/10.1016/j.econmod.2011.02.030.

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