Relevant bibliographies by topics / Logistic regression

Academic literature on the topic 'Logistic regression'

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Published: 4 June 2021
Last updated: 25 May 2024

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Journal articles on the topic "Logistic regression"

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Debanne, Sara M., and Douglas Y. Rowland. "Logistic regression." Gastrointestinal Endoscopy 55, no. 1 (January 2002): 0142–43. http://dx.doi.org/10.1067/mge.2002.119725.

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Debanne, Sara M., and Douglas Y. Rowland. "Logistic regression." Gastrointestinal Endoscopy 55, no. 1 (January 2002): 142–43. http://dx.doi.org/10.1067/mge.2002.120659a.

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Lever, Jake, Martin Krzywinski, and Naomi Altman. "Logistic regression." Nature Methods 13, no. 7 (June 29, 2016): 541–42. http://dx.doi.org/10.1038/nmeth.3904.

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Hersh, A., and T. B. Newman. "Logistic Regression." AAP Grand Rounds 30, no. 5 (November 1, 2013): 55. http://dx.doi.org/10.1542/gr.30-5-55.

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LaValley, Michael P. "Logistic Regression." Circulation 117, no. 18 (May 6, 2008): 2395–99. http://dx.doi.org/10.1161/circulationaha.106.682658.

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Sedgwick, P. "Logistic regression." BMJ 347, jul12 2 (July 12, 2013): f4488. http://dx.doi.org/10.1136/bmj.f4488.

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Wieland, G. Darryl, and James Sayre. "Logistic Regression." Journal of the American Geriatrics Society 35, no. 6 (June 1987): 596–97. http://dx.doi.org/10.1111/j.1532-5415.1987.tb01411.x.

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Ostir, Glenn V., and Tatsuo Uchida. "Logistic Regression." American Journal of Physical Medicine & Rehabilitation 79, no. 6 (November 2000): 565–72. http://dx.doi.org/10.1097/00002060-200011000-00017.

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Sainani, Kristin L. "Logistic Regression." PM&R 6, no. 12 (December 2014): 1157–62. http://dx.doi.org/10.1016/j.pmrj.2014.10.006.

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Pagano, Marcello. "Logistic regression." Nutrition 12, no. 2 (February 1996): 135. http://dx.doi.org/10.1016/s0899-9007(97)85056-4.

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Dissertations / Theses on the topic "Logistic regression"

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Kazemi, Seyed Mehran. "Relational logistic regression." Thesis, University of British Columbia, 2014. http://hdl.handle.net/2429/50091.

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Aggregation is a technique for representing conditional probability distributions as an analytic function of parents. Logistic regression is a commonly used representation for aggregators in Bayesian belief networks when a child has multiple parents. In this thesis, we consider extending logistic regression to directed relational models, where there are objects and relations among them, and we want to model varying populations and interactions among parents. We first examine the representational problems caused by population variation. We show how these problems arise even in simple cases with a single parametrized parent, and propose a linear relational logistic regression which we show can represent arbitrary linear (in population size) decision thresholds, whereas the traditional logistic regression cannot. Then we examine representing interactions among the parents of a child node, and representing non-linear dependency on population size. We propose a multi-parent relational logistic regression which can represent interactions among parents and arbitrary polynomial decision thresholds. We compare our relational logistic regression to Markov logic networks and represent their analogies and differences. Finally, we show how other well-known aggregators can be represented using relational logistic regression.
Science, Faculty of
Computer Science, Department of
Graduate
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Nargis, Suraiya, and n/a. "Robust methods in logistic regression." University of Canberra. Information Sciences & Engineering, 2005. http://erl.canberra.edu.au./public/adt-AUC20051111.141200.

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My Masters research aims to deepen our understanding of the behaviour of robust methods in logistic regression. Logistic regression is a special case of Generalized Linear Modelling (GLM), which is a powerful and popular technique for modelling a large variety of data. Robust methods are useful in reducing the effect of outlying values in the response variable on parameter estimates. A literature survey shows that we are still at the beginning of being able to detect extreme observations in logistic regression analyses, to apply robust methods in logistic regression and to present informatively the results of logistic regression analyses. In Chapter 1 I have made a basic introduction to logistic regression, with an example, and to robust methods in general. In Chapters 2 through 4 of the thesis I have described traditional methods and some relatively new methods for presenting results of logistic regression using powerful visualization techniques as well as the concepts of outliers in binomial data. I have used different published data sets for illustration, such as the Prostate Cancer data set, the Damaged Carrots data set and the Recumbent Cow data set. In Chapter 4 I summarize and report on the modem concepts of graphical methods, such as central dimension reduction, and the use of graphics as pioneered by Cook and Weisberg (1999). In Section 4.6 I have then extended the work of Cook and Weisberg to robust logistic regression. In Chapter 5 I have described simulation studies to investigate the effects of outlying observations on logistic regression (robust and non-robust). In Section 5.2 I have come to the conclusion that, in the case of classical or robust multiple logistic regression with no outliers, robust methods do not necessarily provide more reasonable estimates of the parameters for the data that contain no st~ong outliers. In Section 5.4 I have looked into the cases where outliers are present and have come to the conclusion that either the breakdown method or a sensitivity analysis provides reasonable parameter estimates in that situation. Finally, I have identified areas for further study.
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Rashid, Mamunur. "Inference on Logistic Regression Models." Bowling Green State University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1214165101.

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Williams, Ulyana P. "On Some Ridge Regression Estimators for Logistic Regression Models." FIU Digital Commons, 2018. https://digitalcommons.fiu.edu/etd/3667.

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The purpose of this research is to investigate the performance of some ridge regression estimators for the logistic regression model in the presence of moderate to high correlation among the explanatory variables. As a performance criterion, we use the mean square error (MSE), the mean absolute percentage error (MAPE), the magnitude of bias, and the percentage of times the ridge regression estimator produces a higher MSE than the maximum likelihood estimator. A Monto Carlo simulation study has been executed to compare the performance of the ridge regression estimators under different experimental conditions. The degree of correlation, sample size, number of independent variables, and log odds ratio has been varied in the design of experiment. Simulation results show that under certain conditions, the ridge regression estimators outperform the maximum likelihood estimator. Moreover, an empirical data analysis supports the main findings of this study. This thesis proposed and recommended some good ridge regression estimators of the logistic regression model for the practitioners in the field of health, physical and social sciences.
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Mak, Carmen. "Polychotomous logistic regression via the Lasso." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape10/PQDD_0004/NQ41227.pdf.

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Li, Yin. "Application of logistic regression in biostatistics." Thesis, McGill University, 1993. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=68201.

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The primary objective of this paper is a focused introduction to the logistic regression model and its use in methods for modeling the relationship between a dichotomous outcome variable and a set of covariates. The approach we will take is to develop the model from a regression analysis point of view. Also in this paper, an estimator of the common odds ratio in one-to-one matched case-control studies is proposed. The connection between this estimator and the James-Stein estimating procedure is highlighted through the argument of estimating functions. Comparisons are made between this estimator, the conditional maximum likelihood estimator, and the estimator ignoring the matching.
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Al-Sarraf, Z. J. "Some problems connected with logistic regression." Thesis, Brunel University, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.374301.

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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 examinations were included. The purpose was to use clinical considerations as-well as mathematical statistics to be able to find new diagnostic criteria. We created a few new parameters and evaluated them together with previously used parameters. For women we found some interesting univariable results where new parameters discriminated better than the formerly used. However, the number of females with observed CAD was small (14) which made it impossible to obtain strong significance. For men we computed a multivariable model, using logistic regression, which discriminates way better than the traditional parameters for these patients. The area under the ROC curve was 0:90 (95 % CI: 0.83-0.97) which is excellent to outstanding discrimination in a group initially included due to their inconclusive results. If the model can be proved to hold for another population it could contribute a lot to the diagnostics of this common medical conditions
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Batchelor, John Stephen. "Trauma scoring models using logistic regression." Thesis, University College London (University of London), 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.418022.

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MOREIRA, RODRIGO PINTO. "SMOOTH TRANSITION LOGISTIC REGRESSION MODEL TREE." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2008. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=13437@1.

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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
FUNDAÇÃO DE APOIO À PESQUISA DO ESTADO DO RIO DE JANEIRO
Este trabalho tem como objetivo principal adaptar o modelo STR-Tree, o qual é a combinação de um modelo Smooth Transition Regression com Classification and Regression Tree (CART), a fim de utilizá-lo em Classificação. Para isto algumas alterações foram realizadas em sua forma estrutural e na estimação. Devido ao fato de estarmos fazendo classificação de variáveis dependentes binárias, se faz necessária a utilização das técnicas empregadas em Regressão Logística, dessa forma a estimação dos parâmetros da parte linear passa a ser feita por Máxima Verossimilhança. Assim o modelo, que é paramétrico não-linear e estruturado por árvore de decisão, onde cada nó terminal representa um regime os quais têm seus parâmetros estimados da mesma forma que em uma Regressão Logística, é denominado Smooth Transition Logistic Regression-Tree (STLR-Tree). A inclusão dos regimes, determinada pela divisão dos nós da árvore, é feita baseada em testes do tipo Multiplicadores de Lagrange, que em sua forma para o caso Gaussiano utiliza a Soma dos Quadrados dos Resíduos em suas estatísticas de teste, aqui são substituídas pela Função Desvio (Deviance), que é equivalente para o caso dos modelos não Gaussianos, cuja distribuição da variável dependente pertença à família exponencial. Na aplicação a dados reais selecionou-se dois conjuntos das variáveis explicativas de cada uma das duas bases utilizadas, que resultaram nas melhores taxas de acerto, verificadas através de Tabelas de Classificação (Matrizes de Confusão). Esses conjuntos de variáveis foram usados com outros métodos de classificação existentes, são eles: Generalized Additive Models (GAM), Regressão Logística, Redes Neurais, Análise Discriminante, k-Nearest Neighbor (K-NN) e Classification and Regression Trees (CART).
The main goal of this work is to adapt the STR-Tree model, which is the combination of a Smooth Transition with Regression model with Classi cation and Regression Tree (CART), in order to use it in Classification. Some changes were made in its structural form and in the estimation. Due to the fact we are doing binary dependent variables classification, is necessary to use the techniques employed in Logistic Regression, so the estimation of the linear part will be made by Maximum Likelihood. Thus the model, which is nonlinear parametric and structured by a decision tree, where each terminal node represents a regime that have their parameters estimated in the same way as in a Logistic Regression, is called Smooth Transition Logistic Regression Tree (STLR-Tree). The inclusion of the regimes, determined by the splitting of the tree's nodes, is based on Lagrange Multipliers tests, which for the Gaussian cases uses the Residual Sum-of-squares in their test statistic, here are replaced by the Deviance function, which is equivalent to the case of non-Gaussian models, that has the distribution of the dependent variable in the exponential family. After applying the model in two datasets chosen from the bibliography comparing with other methods of classi cation such as: Generalized Additive Models (GAM), Logistic Regression, Neural Networks, Discriminant Analyses, k-Nearest Neighbor (k-NN) and Classification and Regression Trees (CART). It can be seen, verifying in the Classification Tables (Confusion Matrices) that STLR-Tree showed the second best result for the overall rate of correct classification in three of the four applications shown, being in all of them, behind only from GAM.
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Books on the topic "Logistic regression"

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Menard, Scott W. Logistic regression. Thousand Oaks, Calif: Sage Publications, 2009.

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Logistic regression. Thousand Oaks, Calif: Sage Publications, 2009.

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Kleinbaum, David G. Logistic Regression. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4757-4108-7.

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Kleinbaum, David G., and Mitchel Klein. Logistic Regression. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-1742-3.

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Pampel, Fred. Logistic Regression. 2455 Teller Road, Thousand Oaks California 91320 United States of America: SAGE Publications, Inc., 2000. http://dx.doi.org/10.4135/9781412984805.

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

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Hosmer, David W., and Stanley Lemeshow. Applied Logistic Regression. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2000. http://dx.doi.org/10.1002/0471722146.

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Hosmer, David W., Stanley Lemeshow, and Rodney X. Sturdivant. Applied Logistic Regression. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118548387.

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Stanley, Lemeshow, ed. Applied logistic regression. New York: Wiley, 1989.

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

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Book chapters on the topic "Logistic regression"

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Kleinbaum, David G. "Introduction to Logistic Regression." In Logistic Regression, 1–38. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4757-4108-7_1.

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Kleinbaum, David G. "Important Special Cases of the Logistic Model." In Logistic Regression, 39–72. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4757-4108-7_2.

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Kleinbaum, David G. "Computing the Odds Ratio in Logistic Regression." In Logistic Regression, 73–99. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4757-4108-7_3.

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Kleinbaum, David G. "Maximum Likelihood Techniques: An Overview." In Logistic Regression, 101–24. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4757-4108-7_4.

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Kleinbaum, David G. "Statistical Inferences Using Maximum Likelihood Techniques." In Logistic Regression, 125–60. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4757-4108-7_5.

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Kleinbaum, David G. "Modeling Strategy Guidelines." In Logistic Regression, 161–89. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4757-4108-7_6.

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Kleinbaum, David G. "Modeling Strategy for Assessing Interaction and Confounding." In Logistic Regression, 191–226. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4757-4108-7_7.

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Kleinbaum, David G. "Analysis of Matched Data Using Logistic Regression." In Logistic Regression, 227–51. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4757-4108-7_8.

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Nick, Todd G., and Kathleen M. Campbell. "Logistic Regression." In Topics in Biostatistics, 273–301. Totowa, NJ: Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-530-5_14.

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Holmes, William H., and William C. Rinaman. "Logistic Regression." In Statistical Literacy for Clinical Practitioners, 397–422. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12550-3_15.

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Conference papers on the topic "Logistic regression"

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Cui, Zhicheng, Muhan Zhang, and Yixin Chen. "Deep Embedding Logistic Regression." In 2018 IEEE International Conference on Big Knowledge (ICBK). IEEE, 2018. http://dx.doi.org/10.1109/icbk.2018.00031.

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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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Liu, Fanghui, Xiaolin Huang, and Jie Yang. "Indefinite Kernel Logistic Regression." In MM '17: ACM Multimedia Conference. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3123266.3123295.

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Lubenko, Ivans, and Andrew D. Ker. "Steganalysis using logistic regression." In IS&T/SPIE Electronic Imaging, edited by Nasir D. Memon, Jana Dittmann, Adnan M. Alattar, and Edward J. Delp III. SPIE, 2011. http://dx.doi.org/10.1117/12.872245.

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Yun, Woo-han, Do-Hyung Kim, Su-young Chi, and Ho-Sub Yoon. "Two-Dimensional Logistic Regression." In 19th IEEE International Conference on Tools with Artificial Intelligence(ICTAI 2007). IEEE, 2007. http://dx.doi.org/10.1109/ictai.2007.48.

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Isaac, Jackson, and Sandhya Harikumar. "Logistic regression within DBMS." In 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I). IEEE, 2016. http://dx.doi.org/10.1109/ic3i.2016.7918045.

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Lv, Cui, and Di-Rong Chen. "Interpretable Functional Logistic Regression." In the 2nd International Conference. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3207677.3277962.

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Chen, Wenlin, Yixin Chen, Yi Mao, and Baolong Guo. "Density-based logistic regression." In KDD' 13: The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2013. http://dx.doi.org/10.1145/2487575.2487583.

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Ceko, Enriko. "On the Relationship between ISO Standards and the Logistic Performance Index." In 9th International Scientific Conference ERAZ - Knowledge Based Sustainable Development. Association of Economists and Managers of the Balkans, Belgrade, Serbia, 2023. http://dx.doi.org/10.31410/eraz.2023.189.

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Using regression analysis, the paper aims to clarify the relationship between trade logistics issues, expressed with the logistic performance index, and quality management, expressed with the ISO standards index. The paper opted for an exploratory study using regression analysis to find relations be­tween the logistics performance index and the ISO standards index, using data complemented Logistic Performance Index, and ISO Standards certificates is­sued worldwide, providing statistical insights into the relations between the LPI and QM, and the ISO Standards Index. It suggests that successful business or­ganizations should invest in QM, especially in ISO standards improving their logistics, and competitive advantage. This research addresses a previously stated requirement by doing a regression analysis to investigate how quality management (ISO certifications) and logistics are significantly connected. The study’s findings emphasize the importance of investing in quality management to gain a competitive advantage in logistics, recognizing the importance of the ISO certification process and quality management procedures and processes.
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Putri Wibowo, Velery Virgina, Zuherman Rustam, Afifah Rofi Laeli, and Alva Andhika Said. "Logistic Regression and Logistic Regression-Genetic Algorithm for Classification of Liver Cancer Data." In 2021 International Conference on Decision Aid Sciences and Application (DASA). IEEE, 2021. http://dx.doi.org/10.1109/dasa53625.2021.9682242.

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Reports on the topic "Logistic regression"

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Bai, Z. D., P. R. Krishnaiah, and L. C. Zhao. Variable Selection in Logistic Regression. Fort Belvoir, VA: Defense Technical Information Center, June 1987. http://dx.doi.org/10.21236/ada186032.

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BG Amindan and DN Hagedorn. Logistic Regression Applied to Seismic Discrimination. Office of Scientific and Technical Information (OSTI), October 1998. http://dx.doi.org/10.2172/1360.

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Graham, Bryan. Sparse Network Asymptotics for Logistic Regression. Cambridge, MA: National Bureau of Economic Research, October 2020. http://dx.doi.org/10.3386/w27962.

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Stefanski, L. A., and R. J. Carroll. Covariate Measurement Error in Logistic Regression. Fort Belvoir, VA: Defense Technical Information Center, April 1985. http://dx.doi.org/10.21236/ada160277.

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Churchill, Alexandrea, and Grace Kissling. Convergence in Mixed Effects Logistic Regression Models. Journal of Young Investigators, February 2019. http://dx.doi.org/10.22186/jyi.36.2.18-35.

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Buttrey, Samuel E. The Smarter Regression" Add-In for Linear and Logistic Regression in Excel". Fort Belvoir, VA: Defense Technical Information Center, July 2007. http://dx.doi.org/10.21236/ada470645.

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Belloni, Alexandre, Victor Chernozhukov, and Ying Wei. Honest confidence regions for a regression parameter in logistic regression with a large number of controls. Institute for Fiscal Studies, December 2013. http://dx.doi.org/10.1920/wp.cem.2013.6713.

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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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Stefanski, L. A., R. J. Carroll, and D. Ruppert. Optimally Bounded Score Functions for Generalized Linear Models with Applications to Logistic Regression. Fort Belvoir, VA: Defense Technical Information Center, April 1985. http://dx.doi.org/10.21236/ada160348.

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Salazar, Lina, Alessandro Maffioli, Julián Aramburu, and Marcos Agurto Adrianzen. Estimating the Impacts of a Fruit Fly Eradication Program in Peru: A Geographical Regression Discontinuity Approach. Inter-American Development Bank, March 2016. http://dx.doi.org/10.18235/0012282.

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In this paper, we evaluate the short term impact of a Fruit Fly Eradication Program in the coastal areas of Peru. Exploiting arbitrary variation in the program's intervention borders, as well as precise geographic location data of farmer's households, we use a Geographical Regression Discontinuity (GRD) approach to identify the program's effects on agricultural outcomes. For this purpose, baseline and follow up surveys were collected for 615 households -307 treated and 308 controls- . Baseline data shows that producer and farm-level characteristics in treated and control areas are balanced. This confirms that the program's intervention borders were set only as a function of financial and logistic restrictions and independently of the pest incidence levels and/or other producer and/or farm characteristics. The results show that farmers in treated areas improved pest knowledge and are more likely to implement best practices for plague prevention and control. Beneficiary farmers also present increased fruit crops productivity and sales. The robustness of these findings is confirmed using placebo tests.
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