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Journal articles on the topic 'Regression'

To see the other types of publications on this topic, follow the link: Regression.

Author: Grafiati

Published: 4 June 2021

Last updated: 14 September 2024

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1

Sabzekar, Mostafa, and Seyed Mohammad Hossein Hasheminejad. "Robust regression using support vector regressions." Chaos, Solitons & Fractals 144 (March 2021): 110738. http://dx.doi.org/10.1016/j.chaos.2021.110738.

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2

Phillips, Peter C. B. "Partitioned Regression with Rank-Deficient Regressions." Econometric Theory 8, no. 2 (June 1992): 307–9. http://dx.doi.org/10.1017/s0266466600012901.

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3

Plaehn, Dave C., and David S. Lundahl. "Regression with multiple regressor arrays." Journal of Chemometrics 21, no. 12 (2007): 621–34. http://dx.doi.org/10.1002/cem.1092.

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4

Steece, Bert M. "Regressor space outliers in ridge regression." Communications in Statistics - Theory and Methods 15, no. 12 (January 1986): 3599–605. http://dx.doi.org/10.1080/03610928608829333.

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5

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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6

Duarte, Rigoberto Fimia, del Valle LD, Armiñana GR, Hidalgo MET, and Fimia DR. "Perspectives and Potentials of the Objective Regressive Regression Methodology in Terms of One Health." International Journal of Zoology and Animal Biology 7, no. 4 (2024): 1–6. http://dx.doi.org/10.23880/izab-16000603.

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The possibility of having a methodology that allows the modelling and prediction, in the short, medium and long term, of biological, social and natural disaster processes and/ or phenomena is something great.

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7

Moore, Roger H. "Regression Graphics: Ideas for Studying Regressions Through Graphics." Technometrics 41, no. 4 (November 1999): 368–69. http://dx.doi.org/10.1080/00401706.1999.10485937.

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8

Kubáček, Lubomír. "Multistage regression model." Applications of Mathematics 31, no. 2 (1986): 89–96. http://dx.doi.org/10.21136/am.1986.104189.

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9

Bouchenaki, F., K. Badache, N. Habchi, M. S. Benachour, and S. Bakhti. "Caudal Regression Syndrome." Clinical Research and Clinical Trials 4, no. 3 (September 29, 2021): 01–06. http://dx.doi.org/10.31579/2693-4779/066.

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Caudal Regression Syndrome (CRS) is a rare malformation syndrome associating to varying degrees agenesis of the coccygeal or lumbosacral vertebrae.This vertebral anomaly can therefore be reduced clinically to a simple coccygeal agenesis without any deficit or even lumbosacral agenesis accompanying a clinical picture with sphincter disorders associated or not with transit disorders and various deficits involving the lower limbs.This syndrome is accompanied by other orthopedic malformations such as shortening of the lower limbs, and / or gastrointestinal abnormalities, see also genitourinary as well as cardiovascular.Its incidence is 1 to 5 cases per 100,000 births. Its precise cause has not yet been identified, but its relationship to maternal diabetes is well established. We report in our study 5 patients with CRS from different clinics whose sphincter disorders were found in the foreground in all our patients and whose results vary according to the inaugural clinical picture. MRI made it possible to refine and confirm the diagnosis highlighting the congenital anomaly and the associated lesions.We have obtained 75% good results and 25% clinical stabilization; nor do we deplore any case of aggravation or death. The interest is to suspect the diagnosis of CRS; document it at the start of the prenatal period and determine its severity and associated abnormalities in order to present options for patient management; because once the diagnosis is made, surgical treatment becomes imperative due to the formidable neurological sequelae compromising the functional prognosis.

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10

Sandler, J., and A. Sandler. "Regression und Anti-Regression." Zeitschrift für psychoanalytische Theorie und Praxis 22, no. 2 (2007): 147–58. http://dx.doi.org/10.15534/zptp/2007/2/2.

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11

Baksalary, Jerzy K., and P. D. Puri. "F3. A comment regarding “on regressing regression coefficients”." Journal of Statistical Planning and Inference 16 (January 1987): 133–35. http://dx.doi.org/10.1016/0378-3758(87)90062-0.

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12

Duarte, Rigoberto Fimia. "Potential Application of the Objective Regression Regressive Methodology." Bioscience Biotechnology Research Communications 16, no. 1 (March 25, 2023): 50–53. http://dx.doi.org/10.21786/bbrc/16.1.9.

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The possibility of having a methodology that allows the modeling and prediction, in the short, medium and long term, of biological, social and natural disaster processes and/or phenomena is something great. The objective of the research consisted in demonstrating the potentialities and real capacity of application of the methodology of the Regressive Objective Regression (ROR) in the different fields and branches of scientific research. In the ORR methodology, in a first step, dichotomous variables DS, DI and NoC are created. Then, the module corresponding to the Regression analysis of the SPSS statistical package (ENTER method) is executed, where the predicted variable and the ERROR are obtained; subsequently, the autocorrelograms of the ERROR variable are obtained, paying attention to the maximum of the significant partial autocorrelations, and the new variables are calculated according to the significant Lag of the PACF. Finally, these regressed variables are included in the new regression in a process of successive approximations until a white noise is obtained. Wide possibilities of modeling and forecasting in the short, medium and long term, which go beyond the modeling of infectious entities of parasitic and viral etiology, Acute Respiratory Infections, Acute Bronchial Asthma crises, forecasting of extreme meteorological disturbances, prediction of latitude and longitude of earthquakes, modeling of climatic variables, and even the own electric consumption of a municipality, province and nation. The ROR methodology has demonstrated potential and real capabilities of application in dissimilar fields and branches of science, so it is a novel contribution to the science of modeling and forecasting of variables to know the future, as well as the impact that different variables contribute to an event or phenomenon, and being universal, it can be applied anywhere in the universe.

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13

Falah, Bouchaib, and Souhail Marghabi. "Towards Regression Testing Constraints." International Journal of Modeling and Optimization 4, no. 6 (December 2014): 504–9. http://dx.doi.org/10.7763/ijmo.2014.v4.425.

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14

Alexandrov, Yuri, Olga Svarnik, Irina Znamenskaya, Marina Kolbeneva, Karina Arutyunova, Andrey Krylov, Alexandra Bulava, and Brian Feldman. "Regression II. Development through regression." Journal of Analytical Psychology 65, no. 3 (May 14, 2020): 476–96. http://dx.doi.org/10.1111/1468-5922.12596.

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15

Sen, Pranab Kumar. "Regression quantiles in nonparametric regression." Journal of Nonparametric Statistics 3, no. 3-4 (January 1994): 237–53. http://dx.doi.org/10.1080/10485259408832585.

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16

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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17

Heydari, M. Reza, Saber Salehkaleybar, and Kun Zhang. "Adversarial orthogonal regression: Two non-linear regressions for causal inference." Neural Networks 143 (November 2021): 66–73. http://dx.doi.org/10.1016/j.neunet.2021.05.018.

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18

Bura, Efstathia, and R. Dennis Cook. "Estimating the structural dimension of regressions via parametric inverse regression." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 63, no. 2 (2001): 393–410. http://dx.doi.org/10.1111/1467-9868.00292.

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19

Bhattacharyya, Amit. "Book Review: Regression graphics: ideas for studying regressions through graphics." Statistical Methods in Medical Research 9, no. 6 (December 2000): 602–3. http://dx.doi.org/10.1177/096228020000900606.

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20

Lihua, YANG, and SHEN Pengyun. "Regressive Effect of Institutional Change and Anti-regression Mechanism." Social Policy Review 2, no. 2 (2018): 95–128. http://dx.doi.org/10.47297/wspsprwsp2515-471003.20180202.

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21

Azzedine, Nadjia, Ali Laksaci, and Elias Ould-Saïd. "On robust nonparametric regression estimation for a functional regressor." Statistics & Probability Letters 78, no. 18 (December 2008): 3216–21. http://dx.doi.org/10.1016/j.spl.2008.06.018.

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22

Melki, Gabriella, Alberto Cano, Vojislav Kecman, and Sebastián Ventura. "Multi-target support vector regression via correlation regressor chains." Information Sciences 415-416 (November 2017): 53–69. http://dx.doi.org/10.1016/j.ins.2017.06.017.

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23

Metzger, Elizabeth. "REGRESSION." Yale Review 103, no. 4 (2015): 69. http://dx.doi.org/10.1353/tyr.2015.0093.

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24

Maurer, Brian T. "Regression." Journal of the American Academy of Physician Assistants 33, no. 4 (April 2020): 58. http://dx.doi.org/10.1097/01.jaa.0000657228.79637.e0.

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25

Francis, J. L. "Regression." Blood Coagulation & Fibrinolysis 2, no. 2 (April 1991): 400. http://dx.doi.org/10.1097/00001721-199104000-00032.

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26

Vetter, Thomas R., and Patrick Schober. "Regression." Anesthesia & Analgesia 127, no. 1 (July 2018): 277–83. http://dx.doi.org/10.1213/ane.0000000000003424.

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27

Tu, Renjin, and M. C. Spruill. "regression." Annals of Statistics 29, no. 4 (August 2001): 1050–57. http://dx.doi.org/10.1214/aos/1013699991.

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28

Truong, Young K., Charles J. Stone, Charles Kooperberg, and Jianhua Z. Huang. "regression." Annals of Statistics 28, no. 4 (August 2000): 961–99. http://dx.doi.org/10.1214/aos/1015956704.

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29

Rousson, Valentin, Peter Hall, and Edwin Choi. "regression." Annals of Statistics 28, no. 5 (October 2000): 1339–55. http://dx.doi.org/10.1214/aos/1015957396.

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30

Franke, Tobias, and Holger Dette. "regression." Annals of Statistics 28, no. 6 (December 2000): 1702–27. http://dx.doi.org/10.1214/aos/1015957477.

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31

Fitzmaurice, Garrett M. "Regression." Diagnostic Histopathology 22, no. 7 (July 2016): 271–78. http://dx.doi.org/10.1016/j.mpdhp.2016.06.004.

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32

Sullivan, Karen. "REGRESSION." Early Years Educator 5, no. 9 (January 2004): 48–50. http://dx.doi.org/10.12968/eyed.2004.5.9.14420.

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33

Metzger, Elizabeth. "REGRESSION." Yale Review 103, no. 4 (September 25, 2015): 69. http://dx.doi.org/10.1111/yrev.13014.

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34

Seigel, D. "Regression." Archives of Ophthalmology 105, no. 2 (February 1, 1987): 185–86. http://dx.doi.org/10.1001/archopht.1987.01060020039023.

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35

Shott, S. "Regression." Journal of the American Veterinary Medical Association 198, no. 5 (March 1, 1991): 798–801. http://dx.doi.org/10.2460/javma.1991.198.05.798.

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36

Gao, Chao. "Robust regression via mutivariate regression depth." Bernoulli 26, no. 2 (May 2020): 1139–70. http://dx.doi.org/10.3150/19-bej1144.

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37

Wang, C. Y., Li Hsu, Z. D. Feng, and Ross L. Prentice. "Regression Calibration in Failure Time Regression." Biometrics 53, no. 1 (March 1997): 131. http://dx.doi.org/10.2307/2533103.

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38

Alexandrov, Yuri, Brian Feldman, Olga Svarnik, Irina Znamenskaya, Marina Kolbeneva, Karina Arutyunova, Andrey Krylov, and Alexandra Bulava. "Regression I. Experimental approaches to regression." Journal of Analytical Psychology 65, no. 2 (March 13, 2020): 345–65. http://dx.doi.org/10.1111/1468-5922.12580.

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39

Gutenbrunner, C., and J. Jureckova. "Regression Rank Scores and Regression Quantiles." Annals of Statistics 20, no. 1 (March 1992): 305–30. http://dx.doi.org/10.1214/aos/1176348524.

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40

Stout, Quentin F. "Unimodal regression via prefix isotonic regression." Computational Statistics & Data Analysis 53, no. 2 (December 2008): 289–97. http://dx.doi.org/10.1016/j.csda.2008.08.005.

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41

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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42

Leng, Ling, and Wei Zhu. "Compound Regression and Constrained Regression: Nonparametric Regression Frameworks for EIV Models." American Statistician 74, no. 3 (May 20, 2019): 226–32. http://dx.doi.org/10.1080/00031305.2018.1556734.

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43

Verma, S. S. "Regression models in physiological research." Zeitschrift für Morphologie und Anthropologie 83, no. 1 (March 1, 2001): 129–38. http://dx.doi.org/10.1127/zma/83/2001/129.

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44

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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45

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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46

Shetty, Soumya, Janet Jyothi Dsouza, and Iqbal Thonse Hawaldar. "Rolling regression technique and cross-sectional regression: A tool to analyze Capital Asset Pricing Model." Investment Management and Financial Innovations 18, no. 4 (November 25, 2021): 241–51. http://dx.doi.org/10.21511/imfi.18(4).2021.21.

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The Capital Asset Pricing Model (henceforth, CAPM) is considered an extensively used technique to approximate asset pricing in the field of finance. The CAPM holds the power to explicate stock movements by means of its sole factor that is beta co-efficient. This study focuses on the application of rolling regression and cross-sectional regression techniques on Indian BSE 30 stocks. The study examines the risk-return analysis by using this modern technique. The applicability of these techniques is being viewed in changing business environments. These techniques help to find the effect of selected variables on average stock returns. A rolling regression study rolls the data for changing the windows for every 3-month period for three years. The study modifies the model with and without intercept values. This has been applied to the monthly prices of 30 BSE stocks. The study period is from January 2009 to December 2018. The study revealed that beta is a good predictor for analyzing stock returns, but not the intercept values in the developed model. On the other hand, applying cross-section regression accepts the null hypothesis. α, β, β2 ≠ 0. Therefore, a researcher is faced with the task of finding limitations of each methodology and bringing the best output in the model.

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47

Abdi, Hervé. "Partial least squares regression and projection on latent structure regression (PLS Regression)." WIREs Computational Statistics 2, no. 1 (January 2010): 97–106. http://dx.doi.org/10.1002/wics.51.

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48

Tonhasca, Athayde, John C. Palumbo, and David N. Byrne. "Evaluation of the Power Law and Patchiness Regressions with Regression Diagnostics." Journal of Economic Entomology 89, no. 6 (December 1, 1996): 1477–84. http://dx.doi.org/10.1093/jee/89.6.1477.

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49

Pilgrim, Charlie. "piecewise-regression (aka segmented regression) in Python." Journal of Open Source Software 6, no. 68 (December 2, 2021): 3859. http://dx.doi.org/10.21105/joss.03859.

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50

Ahn, Hongshik. "Log-gamma regression modeling 'through regression trees." Communications in Statistics - Theory and Methods 25, no. 2 (January 1996): 295–311. http://dx.doi.org/10.1080/03610929608831696.

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