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Journal articles on the topic 'Linear regression'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 June 2024

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1

Raghuvanshi, Monika. "Knowledge and Awareness: Linear Regression." Educational Process: International Journal 5, no. 4 (December 1, 2016): 279–92. http://dx.doi.org/10.22521/edupij.2016.54.2.

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2

Lam, Kim Fung. "A Unified Linear Regression Approach." International Journal of Applied Physics and Mathematics 4, no. 4 (2014): 223–26. http://dx.doi.org/10.7763/ijapm.2014.v4.287.

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3

Samaniego, Angel. "CAPM-alpha estimation with robust regression vs. linear regression." Análisis Económico 38, no. 97 (January 20, 2023): 27–37. http://dx.doi.org/10.24275/uam/azc/dcsh/ae/2022v38n97/samaniego.

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4

Genç, S., and M. Mendeş. "Multiple Linear Regression versus Automatic Linear Modelling." Arquivo Brasileiro de Medicina Veterinária e Zootecnia 76, no. 1 (2024): 131–36. http://dx.doi.org/10.1590/1678-4162-13071.

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Abstract:

ABSTRACT In this study, performances of Multiple Linear Regression and Automatic Linear Modelling are compared for different sample sizes and number of predictors. A comprehensive Monte Carlo simulation study was carried out for this purpose. Random numbers generated from multivariate normal distribution by using RNMVN function of IMSL library of Microsoft FORTRAN Developer Studio composed the material of this study. Results of the simulation study showed that the sample size and the number of predictors are the main factors that lead to produce different results. Although both methods gave very similar results especially when studied with large sample sizes (n≥100), the Automatic linear modelling is preferred for analyzing data sets due to its simplicity in analyzing data and interpreting the results, ability to present results visually and providing more detailed information especially studying large complex data sets. It will be beneficial to use the Automatic linear modelling especially in analyzing massive and complex data sets for the purposes of investigating the relationships between one continuous dependent and 10 or more predictors and determine the factors that affect the response or target variable. At the same time, it will also be possible to evaluate the effect of each predictor with a more detailed response.

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5

Hersh, A., and T. B. Newman. "Linear Regression." AAP Grand Rounds 25, no. 6 (June 1, 2011): 68. http://dx.doi.org/10.1542/gr.25-6-68-a.

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6

Pandis, Nikolaos. "Linear regression." American Journal of Orthodontics and Dentofacial Orthopedics 149, no. 3 (March 2016): 431–34. http://dx.doi.org/10.1016/j.ajodo.2015.11.019.

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7

Dombrowsky, Thomas. "Linear regression." Nursing 53, no. 9 (September 2023): 56–60. http://dx.doi.org/10.1097/01.nurse.0000946844.96157.68.

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8

Su, Xiaogang, Xin Yan, and Chih-Ling Tsai. "Linear regression." Wiley Interdisciplinary Reviews: Computational Statistics 4, no. 3 (February 10, 2012): 275–94. http://dx.doi.org/10.1002/wics.1198.

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9

Marill, Keith A. "Advanced Statistics: Linear Regression,Part I: Simple Linear Regression." Academic Emergency Medicine 11, no. 1 (January 2004): 87–93. http://dx.doi.org/10.1111/j.1553-2712.2004.tb01378.x.

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10

Marill, Keith A. "Advanced Statistics: Linear Regression, Part II: Multiple Linear Regression." Academic Emergency Medicine 11, no. 1 (January 2004): 94–102. http://dx.doi.org/10.1111/j.1553-2712.2004.tb01379.x.

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11

MARILL, K. "Advanced statistics: linear regression,*1Part I: simple linear regression." Academic Emergency Medicine 11, no. 1 (January 2004): 87–93. http://dx.doi.org/10.1016/s1069-6563(03)00600-6.

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12

Kilic, Selim. "Linear regression analysis." Journal of Mood Disorders 3, no. 2 (2013): 90. http://dx.doi.org/10.5455/jmood.20130624120840.

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13

Altman, Naomi, and Martin Krzywinski. "Simple linear regression." Nature Methods 12, no. 11 (October 29, 2015): 999–1000. http://dx.doi.org/10.1038/nmeth.3627.

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14

Krzywinski, Martin, and Naomi Altman. "Multiple linear regression." Nature Methods 12, no. 12 (December 2015): 1103–4. http://dx.doi.org/10.1038/nmeth.3665.

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15

Olive, David J. "Linear Regression Analysis." Technometrics 45, no. 4 (November 2003): 362–63. http://dx.doi.org/10.1198/tech.2003.s163.

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16

Malik, Malik Beshir. "Applied Linear Regression." Technometrics 47, no. 3 (August 2005): 371–72. http://dx.doi.org/10.1198/tech.2005.s286.

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17

Slinker, Bryan K., and Stanton A. Glantz. "Multiple Linear Regression." Circulation 117, no. 13 (April 2008): 1732–37. http://dx.doi.org/10.1161/circulationaha.106.654376.

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18

Sedgwick, P. "Simple linear regression." BMJ 346, apr12 1 (April 12, 2013): f2340. http://dx.doi.org/10.1136/bmj.f2340.

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19

Zhou, Shanggang, and Douglas A. Wolfe. "Localpiecewise linear regression∗." Journal of Nonparametric Statistics 12, no. 1 (January 1999): 63–75. http://dx.doi.org/10.1080/10485259908832800.

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20

Fletcher, J. "Multiple linear regression." BMJ 338, jan28 3 (January 28, 2009): b167. http://dx.doi.org/10.1136/bmj.b167.

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21

Bangdiwala, Shrikant I. "Regression: simple linear." International Journal of Injury Control and Safety Promotion 25, no. 1 (January 2, 2018): 113–15. http://dx.doi.org/10.1080/17457300.2018.1426702.

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22

Bangdiwala, Shrikant I. "Regression: multiple linear." International Journal of Injury Control and Safety Promotion 25, no. 2 (March 21, 2018): 232–36. http://dx.doi.org/10.1080/17457300.2018.1452336.

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23

Sainani, Kristin L. "Understanding Linear Regression." PM&R 5, no. 12 (October 17, 2013): 1063–68. http://dx.doi.org/10.1016/j.pmrj.2013.10.002.

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24

Edwards, Martin H. "Linear regression revisited." Physics Teacher 27, no. 4 (April 1989): 280–81. http://dx.doi.org/10.1119/1.2342759.

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25

Beckman, Richard J. "Applied Linear Regression." Technometrics 29, no. 1 (February 1987): 119–20. http://dx.doi.org/10.1080/00401706.1987.10488195.

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26

Jajuga, Krzysztof. "Linear fuzzy regression." Fuzzy Sets and Systems 20, no. 3 (November 1986): 343–53. http://dx.doi.org/10.1016/s0165-0114(86)90045-x.

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27

Ari, Bertan, and H. Altay Güvenir. "Clustered linear regression." Knowledge-Based Systems 15, no. 3 (March 2002): 169–75. http://dx.doi.org/10.1016/s0950-7051(01)00154-x.

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28

Loynes, R. M., G. A. F. Seber, and C. J. Wild. "Non-Linear Regression." Statistician 39, no. 4 (1990): 467. http://dx.doi.org/10.2307/2349091.

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29

Grégoire, G. "Simple Linear Regression." EAS Publications Series 66 (2014): 19–39. http://dx.doi.org/10.1051/eas/1466003.

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30

Grégoire, G. "Multiple Linear Regression." EAS Publications Series 66 (2014): 45–72. http://dx.doi.org/10.1051/eas/1466005.

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31

Chatfield, C., and S. Weisberg. "Applied Linear Regression." Journal of the Royal Statistical Society. Series A (General) 149, no. 3 (1986): 279. http://dx.doi.org/10.2307/2981572.

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32

Yusuf, Ayomide, and Shadi Alawneh. "GPU Implementation of Sales Forecasting with Linear Regression." International Journal of Innovative Research in Computer Science & Technology 6, no. 4 (July 2018): 43–48. http://dx.doi.org/10.21276/ijircst.2018.6.4.1.

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33

Subramani, J. "A FAMILY OF UNBIASED MODIFIED LINEAR REGRESSION ESTIMATORS." Mathematical Journal of Interdisciplinary Sciences 4, no. 1 (September 1, 2015): 77–94. http://dx.doi.org/10.15415/mjis.2015.41008.

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34

Štulajter, František. "Robustness of the best linear unbiased estimator and predictor in linear regression models." Applications of Mathematics 35, no. 2 (1990): 162–68. http://dx.doi.org/10.21136/am.1990.104398.

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35

Kim, Sungsu, and Ashis SenGupta. "Inverse Circular–Linear/Linear–Circular Regression." Communications in Statistics - Theory and Methods 44, no. 22 (November 17, 2015): 4772–82. http://dx.doi.org/10.1080/03610926.2013.804561.

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36

Yao, Weixin, and Longhai Li. "A New Regression Model: Modal Linear Regression." Scandinavian Journal of Statistics 41, no. 3 (October 31, 2013): 656–71. http://dx.doi.org/10.1111/sjos.12054.

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37

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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38

Potocký, Rastislav, and Van Ban To. "On parameter-effects arrays in non-linear regression models." Applications of Mathematics 38, no. 2 (1993): 123–32. http://dx.doi.org/10.21136/am.1993.104539.

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39

Marriott, F. H. C., J. Neter, W. Wasserman, and M. H. Kutner. "Applied Linear Regression Models." Biometrics 41, no. 2 (June 1985): 596. http://dx.doi.org/10.2307/2530893.

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40

Thoni, H., J. Neter, W. Wasserman, and M. H. Kutner. "Applied Linear Regression Models." Biometrics 46, no. 1 (March 1990): 282. http://dx.doi.org/10.2307/2531657.

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41

Svítek, Miroslav. "ITERATED NON-LINEAR REGRESSION." Neural Network World 24, no. 4 (2014): 411–20. http://dx.doi.org/10.14311/nnw.2014.24.024.

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42

Krief, Jerome M. "Semi‐linear mode regression." Econometrics Journal 20, no. 2 (June 1, 2017): 149–67. http://dx.doi.org/10.1111/ectj.12088.

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43

Yuan, Zheng, and Yuhong Yang. "Combining Linear Regression Models." Journal of the American Statistical Association 100, no. 472 (December 2005): 1202–14. http://dx.doi.org/10.1198/016214505000000088.

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44

Sedgwick, P. "Correlation versus linear regression." BMJ 346, apr26 1 (April 26, 2013): f2686. http://dx.doi.org/10.1136/bmj.f2686.

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45

Huang, Yijian. "Fast Censored Linear Regression." Scandinavian Journal of Statistics 40, no. 4 (September 4, 2013): 789–806. http://dx.doi.org/10.1111/sjos.12031.

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46

Groeneboom, Piet, and Kim Hendrickx. "Current status linear regression." Annals of Statistics 46, no. 4 (August 2018): 1415–44. http://dx.doi.org/10.1214/17-aos1589.

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47

Mateos, Gonzalo, Juan Andrés Bazerque, and Georgios B. Giannakis. "Distributed Sparse Linear Regression." IEEE Transactions on Signal Processing 58, no. 10 (October 2010): 5262–76. http://dx.doi.org/10.1109/tsp.2010.2055862.

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48

Young, Sidney, and Andrzej Wierzbicki. "Linear Least-Squares Regression." Journal of Chemical Education 77, no. 5 (May 2000): 669. http://dx.doi.org/10.1021/ed077p669.1.

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49

Kianifard, Farid. "Applied Linear Regression Models." Technometrics 32, no. 3 (August 1990): 352–53. http://dx.doi.org/10.1080/00401706.1990.10484703.

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50

Radaelli, Paolo, and Michele Zenga. "Quantity quantiles linear regression." Statistical Methods and Applications 17, no. 4 (October 23, 2007): 455–69. http://dx.doi.org/10.1007/s10260-007-0071-7.

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