Relevant bibliographies by topics / Logistic regression analysis

Contents

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

Academic literature on the topic 'Logistic regression analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Logistic regression analysis"

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Jessen, Hans Christian, and S. Menard. "Applied Logistic Regression Analysis." Statistician 45, no. 4 (1996): 534. http://dx.doi.org/10.2307/2988559.

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Kilic, Selim. "Binary logistic regression analysis." Journal of Mood Disorders 5, no. 4 (2015): 191. http://dx.doi.org/10.5455/jmood.20151202122141.

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Ziegel, Eric R., and Scott Menard. "Applied Logistic Regression Analysis." Technometrics 38, no. 2 (1996): 192. http://dx.doi.org/10.2307/1270433.

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YANG, MIIN-SHEN, and HWEI-MING CHEN. "FUZZY CLASS LOGISTIC REGRESSION ANALYSIS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 12, no. 06 (2004): 761–80. http://dx.doi.org/10.1142/s0218488504003193.

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Distribution mixtures are used as models to analyze grouped data. The estimation of parameters is an important step for mixture distributions. The latent class model is generally used as the analysis of mixture distributions for discrete data. In this paper, we consider the parameter estimation for a mixture of logistic regression models. We know that the expectation maximization (EM) algorithm was most used for estimating the parameters of logistic regression mixture models. In this paper, we propose a new type of fuzzy class model and then derive an algorithm for the parameter estimation of
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Tripepi, G., K. J. Jager, F. W. Dekker, and C. Zoccali. "Linear and logistic regression analysis." Kidney International 73, no. 7 (2008): 806–10. http://dx.doi.org/10.1038/sj.ki.5002787.

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ABBOTT, ROBERT D. "LOGISTIC REGRESSION IN SURVIVAL ANALYSIS." American Journal of Epidemiology 121, no. 3 (1985): 465–71. http://dx.doi.org/10.1093/oxfordjournals.aje.a114019.

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Shott, S. "Logistic regression and discriminant analysis." Journal of the American Veterinary Medical Association 198, no. 11 (1991): 1902–5. http://dx.doi.org/10.2460/javma.1991.198.11.1902.

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Wu, Ju. "Study Applicable for Multi-Linear Regression Analysis and Logistic Regression Analysis." Open Electrical & Electronic Engineering Journal 8, no. 1 (2014): 782–86. http://dx.doi.org/10.2174/1874129001408010782.

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Current study focus on using method of multi-linear regression analysis and logistic regression analysis, and discuss about the condition and scope of multi-linear regression analysis and logistic regression analysis. A modeling method has been introduced keeping in the basic principles of multi-linear regression analysis and logistic regression analysis. The modeling method and two forms of analytic methods have been analyzed, based on two clinic test data of diabetes and Model-2 diabetes as objects of study in combination with the analytic methods of multi-linear regression and logistic regr
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Qiu, Qixuan. "Based on Bayesian Regression and Logistic Regression Model Forecast Logistics Transportation Delay." Applied and Computational Engineering 135, no. 1 (2025): 175–83. https://doi.org/10.54254/2755-2721/2025.21217.

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In the logistics supply chain transportation, it is inevitable to encounter some unpredictable risks, these risks will greatly affect the accuracy and timeliness of logistics transportation, in order to explore the problem of logistics transport delays, this article uses Bayesian regression and logistic regression to establish a model, by analyzing the relationship between the journey risk and the logistics transport delays, Bayesian regression through Mean Squared Error(MSE) and R carry out a test, logistic regression through Accuracy, Receiver Operating Characteristic Curve (ROC Curve) and P
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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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Dissertations / Theses on the topic "Logistic regression analysis"

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Lo, Sau Yee. "Measurement error in logistic regression model /." View abstract or full-text, 2004. http://library.ust.hk/cgi/db/thesis.pl?MATH%202004%20LO.

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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2004.<br>Includes bibliographical references (leaves 82-83). Also available in electronic version. Access restricted to campus users.
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Olsén, Johan. "Logistic regression modelling for STHR analysis." Thesis, KTH, Matematisk statistik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-148971.

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Coronary artery heart disease (CAD) is a common condition which can impair the quality of life and lead to cardiac infarctions. Traditional criteria during exercise tests are good but far from perfect. A lot of patients with inconclusive tests are referred to radiological examinations. By finding better evaluation criteria during the exercise test we can save a lot of money and let the patients avoid unnecessary examinations. Computers record amounts of numerical data during the exercise test. In this retrospective study 267 patients with inconclusive exercise test and performed radiological e
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Hu, ChungLynn. "Nonignorable nonresponse in the logistic regression analysis /." The Ohio State University, 1998. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487950153601414.

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Emfevid, Lovisa, and Hampus Nyquist. "Financial Risk Profiling using Logistic Regression." Thesis, KTH, Matematisk statistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229821.

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As automation in the financial service industry continues to advance, online investment advice has emerged as an exciting new field. Vital to the accuracy of such service is the determination of the individual investors’ ability to bear financial risk. To do so, the statistical method of logistic regression is used. The aim of this thesis is to identify factors which are significant in determining a financial risk profile of a retail investor. In other words, the study seeks to map out the relationship between several socioeconomic- and psychometric variables to develop a predictive model able
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Webster, Gregg. "Bayesian logistic regression models for credit scoring." Thesis, Rhodes University, 2011. http://hdl.handle.net/10962/d1005538.

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The Bayesian approach to logistic regression modelling for credit scoring is useful when there are data quantity issues. Data quantity issues might occur when a bank is opening in a new location or there is change in the scoring procedure. Making use of prior information (available from the coefficients estimated on other data sets, or expert knowledge about the coefficients) a Bayesian approach is proposed to improve the credit scoring models. To achieve this, a data set is split into two sets, “old” data and “new” data. Priors are obtained from a model fitted on the “old” data. This model is
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Pan, Tianshu. "Using the multivariate multilevel logistic regression model to detect DIF a comparison with HGLM and logistic regression DIF detection methods /." Diss., Connect to online resource - MSU authorized users, 2008.

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Thesis (PH. D.)--Michigan State University. Measurement and Quantitative Methods, 2008.<br>Title from PDF t.p. (viewed on Sept. 8, 2009) Includes bibliographical references (p. 85-89). Also issued in print.
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McGlothlin, Anna E. Stamey James D. Seaman John Weldon. "Logistic regression with misclassified response and covariate measurement error a Bayesian approach /." Waco, Tex. : Baylor University, 2007. http://hdl.handle.net/2104/5101.

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Lindroth, Henriksson Amelia, and Simon Koller. "Logistic Regression Analysis of Patent Approval Rate in Sweden." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230143.

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This thesis was conducted to investigate what factors impact the outcome of a patent application for the Swedish market. The method used was logistic regression and the data was extracted from the database of The Swedish Patent and Registration Offi ce, PRV. The analysis in this thesis started with 47 covariates, including the 35 IPO technical fields, resulting in a model consisting of five covariates. The most important covariates were determined to be the number of notices issued by PRV, whether or not a patent attorney was used and applicant type. The number of notices had a positive impact
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Heise, Mark A. "Optimal designs for a bivariate logistic regression model." Diss., Virginia Tech, 1993. http://hdl.handle.net/10919/38538.

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In drug-testing experiments the primary responses of interest are efficacy and toxicity. These can be modeled as a bivariate quantal response using the Gumbel model for bivariate logistic regression. D-optimal and Q-optimal experimental designs are developed for this model The Q-optimal design minimizes the average asymptotic prediction variance of p(l,O;d), the probability of efficacy without toxicity at dose d, over a desired range of doses. In addition, a new optimality criterion, T -optimality, is developed which minimizes the asymptotic variance of the estimate of the therapeutic index.
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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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Books on the topic "Logistic regression analysis"

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Menard, Scott W. Logistic regression. Sage Publications, 2009.

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Menard, Scott. Applied Logistic Regression Analysis. SAGE Publications, Inc., 2002. http://dx.doi.org/10.4135/9781412983433.

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

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Hosmer, David W. Applied Logistic Regression. John Wiley & Sons, Ltd., 2004.

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Stanley, Lemeshow, ed. Applied logistic regression. Wiley, 1989.

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Stanley, Lemeshow, ed. Applied logistic regression. 2nd ed. Wiley, 2000.

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Institute, SAS, ed. Logistic regression examples using the SAS system. 6th ed. SAS Institute, 1995.

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Mitchel, Klein, and Pryor Erica Rihl, eds. Logistic regression: A self-learning text. 2nd ed. Springer, 2002.

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Nakache, Jean-Pierre. Statistique explicative appliquée: Analyse discriminante, modèle logistique, segmentation par arbre. Technip, 2003.

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Vach, Werner. Logistic regression with missing values in the covariates. Springer-Verlag, 1994.

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Book chapters on the topic "Logistic regression analysis"

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Backhaus, Klaus, Bernd Erichson, Sonja Gensler, Rolf Weiber, and Thomas Weiber. "Logistic Regression." In Multivariate Analysis. Springer Fachmedien Wiesbaden, 2023. http://dx.doi.org/10.1007/978-3-658-40411-6_5.

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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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Backhaus, Klaus, Bernd Erichson, Sonja Gensler, Rolf Weiber, and Thomas Weiber. "Logistic Regression." In Multivariate Analysis. Springer Fachmedien Wiesbaden, 2021. http://dx.doi.org/10.1007/978-3-658-32589-3_5.

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Correll, Joshua, Abigail M. Folberg, Charles M. Judd, Gary H. McClelland, and Carey S. Ryan. "Logistic Regression." In Data Analysis, 4th ed. Routledge, 2025. https://doi.org/10.4324/9781003438335-23.

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Afifi, A. A., and V. Clark. "Logistic regression." In Computer-Aided Multivariate Analysis. Springer US, 1996. http://dx.doi.org/10.1007/978-1-4899-3342-3_12.

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Hartmann, Florian G., Johannes Kopp, and Daniel Lois. "Logistic Regression." In Social Science Data Analysis. Springer Fachmedien Wiesbaden, 2023. http://dx.doi.org/10.1007/978-3-658-41230-2_8.

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Heiberger, Richard M., and Burt Holland. "Logistic Regression." In Statistical Analysis and Data Display. Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2122-5_17.

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Heiberger, Richard M., and Burt Holland. "Logistic Regression." In Statistical Analysis and Data Display. Springer New York, 2004. http://dx.doi.org/10.1007/978-1-4757-4284-8_17.

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Kleinbaum, David G. "Analysis of Matched Data Using Logistic Regression." In Logistic Regression. Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4757-4108-7_8.

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Heumann, Christian, Michael Schomaker, and Shalabh. "Logistic Regression." In Introduction to Statistics and Data Analysis. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11833-3_12.

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Conference papers on the topic "Logistic regression analysis"

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Poonkuzhali, R., J. Hitesh Paliwal, G. Hariharan, and R. Arasu. "Predictive Power of Logistic Regression in Cryptocurrency Price Analysis." In 2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS). IEEE, 2024. http://dx.doi.org/10.1109/ickecs61492.2024.10617170.

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Xiao, Yichen, Guanwen Yan, Yushi Yan, and Yuhao Xiang. "Sentiment analysis of movie reviews based on logistic regression model." In Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), edited by Lvqing Yang. SPIE, 2024. http://dx.doi.org/10.1117/12.3038109.

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Parjan, Yeni Anistyasari, Ekohariadi, and Shintami Chusnul Hidayati. "Predicting Academic Achievement Through Engagement Analysis with Sparse Logistic Regression." In 2024 International Seminar on Intelligent Technology and Its Applications (ISITIA). IEEE, 2024. http://dx.doi.org/10.1109/isitia63062.2024.10667991.

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Nagar, Sameer, Praveen Bhanodia, Kamal Kumar Sethi, and Narendra Pal Singh Rathore. "Logistic Regression Based Approach for Human Sentiment Analysis Across Domains." In 2024 International Conference on Advances in Computing Research on Science Engineering and Technology (ACROSET). IEEE, 2024. http://dx.doi.org/10.1109/acroset62108.2024.10743868.

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Nancy, P., Prachi Pandi, and Amrita Kumari. "Sleeping Pattern Analysis Using Extreme Gradient Boosting and Logistic Regression." In 2024 International Conference on IoT, Communication and Automation Technology (ICICAT). IEEE, 2024. https://doi.org/10.1109/icicat62666.2024.10923026.

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C, Kiruthika, E. S. Gopi, and Gutlapalli Swaroopa. "Performance Analysis of Logistic Regression Classifier for OTFS Symbol Detection." In 2025 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI). IEEE, 2025. https://doi.org/10.1109/iatmsi64286.2025.10985692.

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Astuti, Fathia Dwi, Widodo, and Bambang Prasetya Adhi. "Sentiment Analysis of Thesis Policy by Instagram Users Using Logistic Regression." In 2024 11th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI). IEEE, 2024. https://doi.org/10.1109/eecsi63442.2024.10776372.

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Pamulaparthyvenkata, Saigurudatta, Manish Vishwanath, Nithin Reddy Desani, Prakash Murugesan, and Dinesh Gottipalli. "Non Linear-Logistic Regression Analysis for AI-Driven Medicare Fraud Detection." In 2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity (ICDSCNC). IEEE, 2024. https://doi.org/10.1109/icdscnc62492.2024.10939147.

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van Erp, N., and P. van Gelder. "Bayesian logistic regression analysis." In BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: 32nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. AIP, 2013. http://dx.doi.org/10.1063/1.4819994.

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Duller, Christine. "Model selection for logistic regression models." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2012: International Conference of Numerical Analysis and Applied Mathematics. AIP, 2012. http://dx.doi.org/10.1063/1.4756152.

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Reports on the topic "Logistic regression analysis"

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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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Jakobsen, Tor Georg. Using ChatGPT with Stata: Regression Analysis (Part II). Instats Inc., 2025. https://doi.org/10.61700/2w5b85mmn916t1568.

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This comprehensive 3-day workshop focuses on refining statistical modeling and regression analysis skills by integrating advanced AI tools like ChatGPT with traditional software such as Stata. Participants will gain practical competencies in various regression techniques, including interaction effects (or 'moderation'), logistic regression, missing data, scale construction, transformations, weighting, and how to deal with regression assumptions, along with AI-powered assistance from ChatGPT to clarify concepts and troubleshoot commands.
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Fraser, R., R. Fernandes, and R. Latifovic. Multi-temporal Burned area Mapping Using Logistic Regression Analysis and Change Metrics. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2002. http://dx.doi.org/10.4095/219870.

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Fessel, Kimberly. Machine Learning Essentials (Free Seminar). Instats Inc., 2024. http://dx.doi.org/10.61700/l6x4izy1bov9p1764.

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This comprehensive one-hour seminar provides PhD students, academics, and professional researchers with fundamental insights into machine learning concepts, crucial for modern data analysis in many disciplines. Led by data science expert Dr Kimberly Fessel, participants will explore key topics such as supervised and unsupervised learning, model performance (under- vs. overfitting), and popular algorithms like linear and logistic regression, decision trees, and neural networks.
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Liu, Hongrui, and Rahul Ramachandra Shetty. Analytical Models for Traffic Congestion and Accident Analysis. Mineta Transportation Institute, 2021. http://dx.doi.org/10.31979/mti.2021.2102.

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In the US, over 38,000 people die in road crashes each year, and 2.35 million are injured or disabled, according to the statistics report from the Association for Safe International Road Travel (ASIRT) in 2020. In addition, traffic congestion keeping Americans stuck on the road wastes millions of hours and billions of dollars each year. Using statistical techniques and machine learning algorithms, this research developed accurate predictive models for traffic congestion and road accidents to increase understanding of the complex causes of these challenging issues. The research used US Accident
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Arhin, Stephen, Babin Manandhar, and Adam Gatiba. Influence of Pavement Conditions on Commercial Motor Vehicle Crashes. Mineta Transportation Institute, 2023. http://dx.doi.org/10.31979/mti.2023.2343.

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Commercial motor vehicle (CMV) safety is a major concern in the United States, including the District of Columbia (DC), where CMVs make up 15% of traffic. This research uses a comprehensive approach, combining statistical analysis and machine learning techniques, to investigate the impact of road pavement conditions on CMV accidents. The study integrates traffic crash data from the Traffic Accident Reporting and Analysis Systems Version 2.0 (TARAS2) database with pavement condition data provided by the District Department of Transportation (DDOT). Data spanning from 2016 to 2020 was collected
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Tabuga, Aubrey, Anna Rita Vargas, and Madeleine Louise Baiño. Analyzing the Resilience of Farming Households in Upland Areas. Philippine Institute for Development Studies, 2023. http://dx.doi.org/10.62986/dp2023.24.

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The challenges faced by farming communities, such as typhoons, floods, droughts, volcanic eruptions, and pest infestations, can pose significant costs to their livelihoods. This study examines the resilience of upland farming households using a small yet novel survey conducted in the municipality of Atok in Benguet. To analyze resilience, the study explores indicators based on the conceptual framework Schipper and Langston (2015) put forward, namely learning, options, and flexibility. Principal Components Analysis (PCA) was applied for index creation, while ordered logistic regression was empl
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ศุภศันสนีย์, ประพิม, та สุชาดา รัชชุกูล. ความสัมพันธ์ระหว่างลักษณะทางสังคมกับการป่วยด้วยโรคเรื้อรังและไม่ติดต่อ. จุฬาลงกรณ์มหาวิทยาลัย, 2002. https://doi.org/10.58837/chula.res.2002.22.

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การศึกษาย้อนหลังจากผลไปหาเหตุ (Case-control study) ครั้งนี้ มีวัตถุประสงค์เพื่อหาตัวชี้วัดลักษณะทางสังคมของบุคคลที่ป่วยด้วยโรคเรื้อรังและไม่ติดต่อ และหาความสัมพันธ์ระหว่างลักษณะทางสังคมของบุคคลที่ป่วยด้วยโรคเรื้อรังและไม่ติดต่อกับบุคคลที่มีสุขภาพดี กลุ่มตัวอย่างแบ่งเป็น 2 กลุ่มคือ กลุ่มศึกษา (case) เป็นผู้ป่วยโรคหลอดเลือดหัวใจ 198 ราย ผู้ป่วยโรคหลอดเลือดสมอง 198 ราย และกลุ่มควบคุม (control) เป็นบุคคลที่มีสุขภาพดีจำนวน 202 ราย โดยสุ่มตัวอย่างหลายขั้นตอนในโรงพยาบาล 11 แห่ง เครื่องมือที่ใช้ในการศึกษาเป็นแบบสอบถาม ซึ่งมีค่าความเที่ยงเท่ากับ .80 วิเคราะห์ข้อมูลโดยใช้สถิติความถี่ ค่าร้อยละ และการวิเ
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Chierichetti, Maria, Armin Chierichetti, and Fatemeh Davoudi. Design of an Evaluation Plan for Senate Bill 1046. Mineta Transportation Institute, 2022. http://dx.doi.org/10.31979/mti.2021.2209.

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In an effort to understand and decrease alcohol-impaired driving as a primary collision factor In California, the research team designed an evaluation plan for California Senate Bill 1046 and its focus on ignition interlock devices as a sentence for Driving Under Influence offense. This plan will evaluate whether Senate Bill 1046 affected the Driving Under the Influence crash frequency and severity, and whether sociodemographic and geographic factors influence its effectiveness. This report lays the foundation for the evaluation that will be conducted in 2024. The research team conducted a met
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