Relevant bibliographies by topics / Data Regression

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Data Regression'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Data Regression"

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D, Christy Sujatha, and Gnana Jayanthi Dr.J. "LASH Tree: LASSO Regression Hoeffding for Streaming Data." International Journal of Psychosocial Rehabilitation 24, no. 04 (2020): 3022–33. http://dx.doi.org/10.37200/ijpr/v24i4/pr201415.

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Fera, Sarmada. "CENSORED DATA MODELING: A NOVEL ANTI-REGRESSION FRAMEWORK." CURRENT RESEARCH JOURNAL OF HISTORY 04, no. 05 (2023): 26–29. http://dx.doi.org/10.37547/history-crjh-04-05-05.

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Censored data, where the exact value of an observation is not fully observed, poses a challenge in statistical modelling. Traditional regression approaches often fail to adequately handle such data, leading to biased estimates and inaccurate predictions. In this study, we propose a novel anti-regression framework specifically designed for censored data modelling. The framework integrates advanced statistical techniques and incorporates mechanisms to mitigate the impact of censoring. By leveraging the information available from censored observations, our approach provides more reliable estimate
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Alin, Tomoiaga, Farelly John, and Nakamura Shintaro. "Genomic Big Data Regression Analysis." Genomic Big Data Regression Analysis 2, no. 1 (2018): 24–30. https://doi.org/10.5281/zenodo.1438968.

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DNA analysis is now a data intensive discipline.  New technology has transformed biomedical research by making a plethora of molecular data available at reduced costs and great speeds. Large consortiums and many individual laboratories have already generated vast datasets: as an example, one such database, the Gene Expression Omnibus (GEO contains more than 1.8 million samples. This data is readily, publicly available but analyzing it requires computational and statistical resources. A popular concern in biological research is to identify those genomic pathways that are related to the org
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K., Ashfaq Ahmed, and Dr Shaheda Akthar. "Ridge Regression based Missing Data Estimation with Dimensionality Reduction: Microarray Gene Expression Data." Webology 19, no. 1 (2022): 4113–28. http://dx.doi.org/10.14704/web/v19i1/web19271.

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Data is considered to be the important element in the field of Data Science and Machine Learning. Performance of Machine Learning and Data Mining algorithms greatly influenced by the characteristics of data and data with missing values. Performance of all these Machine Learning algorithms greatly improved and they can give accurate results when the data is in full without missing values. So before applying these algorithms; dataset and its missing values are completely filled. To impute these missing values in the dataset there are numerous methods were proposed. In this paper we used micro ar
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Jaqaman, Khuloud, and Gaudenz Danuser. "Linking data to models: data regression." Nature Reviews Molecular Cell Biology 7, no. 11 (2006): 813–19. http://dx.doi.org/10.1038/nrm2030.

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Chen, Aiyou, Art B. Owen, and Minghui Shi. "Data enriched linear regression." Electronic Journal of Statistics 9, no. 1 (2015): 1078–112. http://dx.doi.org/10.1214/15-ejs1027.

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Peng, Xinjun, and Dong Xu. "Projection support vector regression algorithms for data regression." Knowledge-Based Systems 112 (November 2016): 54–66. http://dx.doi.org/10.1016/j.knosys.2016.08.030.

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B. Vinoth, B. Vinoth, and A. Rajarathinam A. Rajarathinam. "Outlier Detection in Simple Linear Regression Models and Robust Regression – A Case Study on Wheat Production Data." International Journal of Scientific Research 3, no. 2 (2012): 531–36. http://dx.doi.org/10.15373/22778179/feb2014/179.

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Valton, Kamberaj. "CATEGORICAL DATA ANALYSIS USING LOGISTIC REGRESSION." https://link.springer.com/journal/10898 3 (September 7, 2021): 11. https://doi.org/10.5281/zenodo.5534800.

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The categorized data that will be analyzed in this paper will be of the type that will use the logistic regression method. The rest of the paper will focus on logistic regression and then the combination of these in the title of the topic mentioned above. Logistic regression is a technique widely used for categorical data analysis, offering increased flexibility compared to traditional intersection analysis. A binary result can be predicted using one or more categorical variables, continuous variables or combinations.
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P. Singh, Brijesh. "Statistical Investigation of Household Size using Count Data Regression Models." Journal of Advanced Research in Applied Mathematics and Statistics 2, no. 1&2 (2017): 10–21. http://dx.doi.org/10.24321/2455.7021.201702.

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Dissertations / Theses on the topic "Data Regression"

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Wiencierz, Andrea. "Regression analysis with imprecise data." Diss., Ludwig-Maximilians-Universität München, 2013. http://nbn-resolving.de/urn:nbn:de:bvb:19-166786.

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Statistical methods usually require that the analyzed data are correct and precise observations of the variables of interest. In practice, however, often only incomplete or uncertain information about the quantities of interest is available. The question studied in the present thesis is, how a regression analysis can reasonably be performed when the variables are only imprecisely observed. At first, different approaches to analyzing imprecisely observed variables that were proposed in the Statistics literature are discussed. Then, a new likelihood-based methodology for regression analysis wit
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Lee, Ho-Jin. "Functional data analysis: classification and regression." Texas A&M University, 2004. http://hdl.handle.net/1969.1/2805.

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Functional data refer to data which consist of observed functions or curves evaluated at a finite subset of some interval. In this dissertation, we discuss statistical analysis, especially classification and regression when data are available in function forms. Due to the nature of functional data, one considers function spaces in presenting such type of data, and each functional observation is viewed as a realization generated by a random mechanism in the spaces. The classification procedure in this dissertation is based on dimension reduction techniques of the spaces. One commonly used metho
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Ormerod, John T. Mathematics &amp Statistics Faculty of Science UNSW. "On semiparametric regression and data mining." Awarded by:University of New South Wales. Mathematics & Statistics, 2008. http://handle.unsw.edu.au/1959.4/40913.

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Semiparametric regression is playing an increasingly large role in the analysis of datasets exhibiting various complications (Ruppert, Wand & Carroll, 2003). In particular semiparametric regression a plays prominent role in the area of data mining where such complications are numerous (Hastie, Tibshirani & Friedman, 2001). In this thesis we develop fast, interpretable methods addressing many of the difficulties associated with data mining applications including: model selection, missing value analysis, outliers and heteroscedastic noise. We focus on function estimation using penalised splines
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Bocci, Cynthia Jacqueline. "Linear regression with spatially correlated data." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape10/PQDD_0012/NQ52271.pdf.

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Jin, Yi. "Regression Analysis of University Giving Data." Digital WPI, 2007. https://digitalcommons.wpi.edu/etd-theses/1.

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This project analyzed the giving data of Worcester Polytechnic Institute's alumni and other constituents (parents, friends, neighbors, etc.) from fiscal year 1983 to 2007 using a two-stage modeling approach. Logistic regression analysis was conducted in the first stage to predict the likelihood of giving for each constituent, followed by linear regression method in the second stage which was used to predict the amount of contribution to be expected from each contributor. Box-Cox transformation was performed in the linear regression phase to ensure the assumption underlying the model holds. Due
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Li, Yi-Hwei. "Regression analysis of failure time data /." The Ohio State University, 1991. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487694702784082.

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Widman, Linnea. "Regression då data utgörs av urval av ranger." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-60664.

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För alpina skidåkare mäter man prestationer i så kallad FIS-ranking. Vi undersöker några metoder för hur man kan analysera data där responsen består av ranger som dessa. Vid situationer då responsdata utgörs av urval av ranger finns ingen självklar analysmetod. Det vi undersöker är skillnaderna vid användandet av olika regressionsanpassningar så som linjär, logistisk och ordinal logistisk regression för att analysera data av denna typ. Vidare används bootstrap för att bilda konfidensintervall. Det visar sig att för våra datamaterial ger metoderna liknande resultat när det gäller att hitta bety
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Stokes, Michael Jeffrey. "Advancements in rapid load test data regression." [Tampa, Fla] : University of South Florida, 2006. http://purl.fcla.edu/usf/dc/et/SFE0001929.

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Wiencierz, Andrea [Verfasser]. "Regression analysis with imprecise data / Andrea Wiencierz." München : Verlag Dr. Hut, 2014. http://d-nb.info/1050331575/34.

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Daud, Isa Bin. "Influence diagnostics in regression with censored data." Thesis, Loughborough University, 1987. https://dspace.lboro.ac.uk/2134/11728.

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The work in this thesis is concerned with the development and extension of techniques for the assessment of influence diagnostics in data that include censored observations. Various regression models with censored data are presented and we concentrate on two models which are the accelerated failure time model, where the errors are generated by mixtures of normal distributions,and the Cox proportional hazards model. For the former, both finite discrete and continuous mixtures are considered, and an EM algorithm is used to determine measures of influence for each case. For the Cox proportional h
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Books on the topic "Data Regression"

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Cameron, Adrian Colin. Regression analysis of count data. Cambridge University Press, 1998.

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McMillen, Daniel P. Quantile Regression for Spatial Data. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-31815-3.

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1949-, Bell David E., ed. Data analysis, regression, and forecasting. Course Technology, 1995.

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Hilbe, Joseph. Logistic regression models. Chapman & Hall/CRC, 2009.

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Müller, Hans-Georg. Nonparametric Regression Analysis of Longitudinal Data. Springer New York, 1988. http://dx.doi.org/10.1007/978-1-4612-3926-0.

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Müller, Hans-Georg. Nonparametric regression analysis of longitudinal data. Springer-Verlag, 1988.

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Freund, Rudolf Jakob. Regression using JMP. J. Wiley, 2003.

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Belsley, David A. Conditioning diagnostics: Collinearity and weak data in regression. Wiley, 1991.

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C, Littell Ramon, and SAS Institute, eds. SAS System for regression. 2nd ed. SAS Institute, 1991.

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C, Littell Ramon, ed. SAS System for regression. 3rd ed. SAS Institute, 2000.

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Book chapters on the topic "Data Regression"

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Timbers, Tiffany, Trevor Campbell, Melissa Lee, Joel Ostblom, and Lindsey Heagy. "Regression II: linear regression." In Data Science. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003438397-8.

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Timbers, Tiffany, Trevor Campbell, and Melissa Lee. "Regression II: linear regression." In Data Science. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003080978-8.

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Runkler, Thomas A. "Regression." In Data Mining. Vieweg+Teubner, 2010. http://dx.doi.org/10.1007/978-3-8348-9353-6_6.

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Runkler, Thomas A. "Regression." In Data Analytics. Vieweg+Teubner Verlag, 2012. http://dx.doi.org/10.1007/978-3-8348-2589-6_6.

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Runkler, Thomas A. "Regression." In Data Analytics. Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-14075-5_6.

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Runkler, Thomas A. "Regression." In Data Mining. Springer Fachmedien Wiesbaden, 2015. http://dx.doi.org/10.1007/978-3-8348-2171-3_6.

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Runkler, Thomas A. "Regression." In Data Analytics. Springer Fachmedien Wiesbaden, 2020. http://dx.doi.org/10.1007/978-3-658-29779-4_6.

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Runkler, Thomas A. "Regression." In Data Analytics. Springer Fachmedien Wiesbaden, 2024. http://dx.doi.org/10.1007/978-3-658-45951-2_6.

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Mills, Terence C. "Regression." In Analysing Economic Data. Palgrave Macmillan UK, 2014. http://dx.doi.org/10.1057/9781137401908_6.

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Judd, Charles M., Gary H. McClelland, and Carey S. Ryan. "Logistic Regression." In Data Analysis. Routledge, 2017. http://dx.doi.org/10.4324/9781315744131-14.

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Conference papers on the topic "Data Regression"

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Bordes, Philippe, Kevin Reuzé, Franck Galpin, et al. "Regression-Based Geometric Partitioning Mode Coding." In 2025 Data Compression Conference (DCC). IEEE, 2025. https://doi.org/10.1109/dcc62719.2025.00039.

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Shoji, Yoshifumi, and Masahiro Yukawa. "Robust Quantile Regression Under Unreliable Data." In 2024 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, 2024. https://doi.org/10.1109/apsipaasc63619.2025.10849248.

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"Predicting poverty using regression." In Data Mining and Data Warehauses – Sikdd 2024. Jožef Stefan Instutute, 2024. http://dx.doi.org/10.70314/is.2024.sikdd.20.

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Holcomb, Tyler R., and Manfred Morari. "Significance Regression: Robust Regression for Collinear Data." In 1993 American Control Conference. IEEE, 1993. http://dx.doi.org/10.23919/acc.1993.4793203.

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Bhat, Harish S., Nitesh Kumar, and Garnet J. Vaz. "Towards scalable quantile regression trees." In 2015 IEEE International Conference on Big Data (Big Data). IEEE, 2015. http://dx.doi.org/10.1109/bigdata.2015.7363741.

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Center, Julian L., Kevin H. Knuth, Ariel Caticha, Julian L. Center, Adom Giffin, and Carlos C. Rodríguez. "Regression for Proportion Data." In BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING. AIP, 2007. http://dx.doi.org/10.1063/1.2821266.

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Lin, Dongyu, and Dean P. Foster. "VIF Regression: A Fast Regression Algorithm for Large Data." In 2009 Ninth IEEE International Conference on Data Mining (ICDM). IEEE, 2009. http://dx.doi.org/10.1109/icdm.2009.146.

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Du, Wei, Xintao Wu, and Hanghang Tong. "Fair Regression under Sample Selection Bias." In 2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022. http://dx.doi.org/10.1109/bigdata55660.2022.10021107.

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Eppert, Martin, Philipp Fent, and Thomas Neumann. "A Tailored Regression for Learned Indexes: Logarithmic Error Regression." In SIGMOD/PODS '21: International Conference on Management of Data. ACM, 2021. http://dx.doi.org/10.1145/3464509.3464891.

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Kang, Rui, Shaoxu Song, and Chaokun Wang. "Conditional Regression Rules." In 2022 IEEE 38th International Conference on Data Engineering (ICDE). IEEE, 2022. http://dx.doi.org/10.1109/icde53745.2022.00231.

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Reports on the topic "Data Regression"

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Graham, Bryan, Jinyong Hahn, Alexandre Poirier, and James Powell. Quantile Regression with Panel Data. National Bureau of Economic Research, 2015. http://dx.doi.org/10.3386/w21034.

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Powell, James L., Alexandre Poirier, Bryan S. Graham, and Jinyong Hahn. Quantile regression with panel data. Institute for Fiscal Studies, 2015. http://dx.doi.org/10.1920/wp.cem.2015.1215.

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Di Gessa, Giorgio, Paola Zaninotto, and Andrea Lisette Castro. Applied Regression with Continuous and Categorical Data. Instats Inc., 2024. http://dx.doi.org/10.61700/7xri7v6fq6k8o1543.

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This comprehensive 4-day workshop equips researchers with advanced skills in both linear and Poisson regression analysis using real-world data. Offered by professors Zaninotto and Di Gessa from the Institute of Epidemiology & Health Care at UCL, participants will enhance their data preparation, model building, and interpretation capabilities, gaining practical, hands-on experience essential for impactful research across various social, health, and life science disciplines.
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Robinson, Peter. Asymptotic theory for nonparametric regression with spatial data. Institute for Fiscal Studies, 2011. http://dx.doi.org/10.1920/wp.cem.2011.1111.

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Linton, Oliver, and Qiying Wang. Non-parametric transformation regression with non-stationary data. Institute for Fiscal Studies, 2013. http://dx.doi.org/10.1920/wp.cem.2013.1613.

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Quan, Lin. Piecewise linear regression for leaf appearance rate data. Iowa State University, 2021. http://dx.doi.org/10.31274/cc-20240624-1124.

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Frome E.L., Watkins J. P. ,. Ellis E. D. Poisson Regression Analysis of Illness and Injury Surveillance Data. Office of Scientific and Technical Information (OSTI), 2012. http://dx.doi.org/10.2172/1060522.

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Meloncelli, Daniel. Regression Analysis using SPSS. Instats Inc., 2025. https://doi.org/10.61700/9zohmz8j1gzcj1476.

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This seminar provides a comprehensive introduction to regression analysis using SPSS, equipping researchers with the skills to apply linear and logistic regression techniques in their work. Participants will gain practical experience in model fitting, assumption checking, and interpreting results, enhancing their ability to leverage data effectively in their respective fields.
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Meloncelli, Daniel. Regression Analysis using R. Instats Inc., 2025. https://doi.org/10.61700/j3s1r521de2ee1472.

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This seminar provides a comprehensive introduction to regression analysis using R. Participants will explore the fundamentals of correlation, simple linear regression, multiple regression, and logistic regression. The seminar emphasises practical application, guiding attendees through data preparation, model fitting, assumption checking, and interpretation of results. Hands-on sessions will enable participants to apply regression techniques to real-world datasets, enhancing their analytical skills for research purposes.
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Garrett, Thomas A. Aggregated vs. Disaggregated Data in Regression Analysis: Implications for Inference. Federal Reserve Bank of St. Louis, 2002. http://dx.doi.org/10.20955/wp.2002.024.

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