Relevant bibliographies by topics / Logistic regression analysis model

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

Academic literature on the topic 'Logistic regression analysis model'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 February 2022

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Journal articles on the topic "Logistic regression analysis model"

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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 (December 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 a fuzzy class logistic regression model. The effects of the explanatory variables on the response variables are described. The focus is on binary responses for the logistic regression mixture analysis with a fuzzy class model. An algorithm, called a fuzzy classification maximum likelihood (FCML), is then created. The mean squared error (MSE) based accuracy criterion for the FCML and EM algorithms to the parameter estimation of logistic regression mixture models are compared using the samples drawn from logistic regression mixtures of two classes. Numerical results show that the proposed FCML algorithm presents good accuracy and is recommended as a new tool for the parameter estimation of the logistic regression mixture models.
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Midi, Habshah, S. K. Sarkar, and Sohel Rana. "Collinearity diagnostics of binary logistic regression model." Journal of Interdisciplinary Mathematics 13, no. 3 (June 2010): 253–67. http://dx.doi.org/10.1080/09720502.2010.10700699.

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Wong, George Y., and William M. Mason. "The Hierarchical Logistic Regression Model for Multilevel Analysis." Journal of the American Statistical Association 80, no. 391 (September 1985): 513–24. http://dx.doi.org/10.1080/01621459.1985.10478148.

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Gbohounme, Idelphonse Leandre Tawanou, Oscar Owino Ngesa, and Jude Eggoh. "Self-Selecting Robust Logistic Regression Model." International Journal of Statistics and Probability 6, no. 3 (May 14, 2017): 132. http://dx.doi.org/10.5539/ijsp.v6n3p132.

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Logistic regression model is the most common model used for the analysis of binary data. However, the problem of atypical observations in the data has an unduly effect on the parameter estimates. Many researchers have developed robust statistical model to solve this problem of outliers. Gelman (2004) proposed GRLR, a robust model by trimming the probability of success in LR. The trimming values in this model were fixed and the user is required to specify this value well in advance. In particular this study developed SsRLR model by allowing the data itself to select the alpha value. We proposed a Restricted LR model to substitute the LR in presence of outliers. We proved that the SsRLR model is the more robust to the presence of leverage points in the data. Parameter estimations is done using a full Bayesian approach implemented in WinBUGS 14 software.
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Fathurahman, M., Purhadi, Sutikno, and Vita Ratnasari. "Geographically Weighted Multivariate Logistic Regression Model and Its Application." Abstract and Applied Analysis 2020 (August 1, 2020): 1–10. http://dx.doi.org/10.1155/2020/8353481.

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This study investigates the geographically weighted multivariate logistic regression (GWMLR) model, parameter estimation, and hypothesis testing procedures. The GWMLR model is an extension to the multivariate logistic regression (MLR) model, which has dependent variables that follow a multinomial distribution along with parameters associated with the spatial weighting at each location in the study area. The parameter estimation was done using the maximum likelihood estimation and Newton-Raphson methods, and the maximum likelihood ratio test was used for hypothesis testing of the parameters. The performance of the GWMLR model was evaluated using a real dataset and it was found to perform better than the MLR model.
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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 (December 31, 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 regression. Analysis result indicate that glycosylated hemoglobin, glycerin trilaurate, total cholesterol of serum and blood sugar concentration present obvious positive relation (P < 0.05), whereas insulin and blood sugar present negative relation(P < 0.05); body mass index (BMI) and relative factors are dangerous; physical excise and relative factors are protective. In conclusion, multi-linear regression analysis and logistic regression analysis respectively have their own emphasis; for example, multi-linear regression analysis emphasizes on analyzing linear dependent relation with an dependent variable and multiple independent variables, whereas logistic regression analysis emphasizes on analyzing the relation between probability of occurring an incident and independent variables.
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Changpetch, Pannapa, and Dennis K. J. Lin. "Model selection for logistic regression via association rules analysis." Journal of Statistical Computation and Simulation 83, no. 8 (August 2013): 1415–28. http://dx.doi.org/10.1080/00949655.2012.662231.

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裴, 亚蕾. "The Discriminant Analysis and Logistic Regression Analysis of SMEs Bankruptcy Model." Pure Mathematics 08, no. 06 (2018): 604–12. http://dx.doi.org/10.12677/pm.2018.86081.

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Ciu, Tania, and Raymond Sunardi Oetama. "Logistic Regression Prediction Model for Cardiovascular Disease." IJNMT (International Journal of New Media Technology) 7, no. 1 (July 2, 2020): 33–38. http://dx.doi.org/10.31937/ijnmt.v7i1.1340.

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— It is undeniable that cardiovascular disease is the number one cause of death in the world. Various factors such as age, cholesterol level, and unhealthy lifestyle can trigger cardiovascular disease. The symptoms of cardiovascular disease are also challenging to identify. It takes careful understanding and analysis related to patient medical record data and identification of the parameters that cause this disease. This study was conducted to predict the main factors causing cardiovascular disease. In this study, a dataset consisting of 14 attributes with class labels was used as the basis for identification as a link between factors that cause cardiovascular disease. The research area used is the area of ​​analysis data where the analyzed data are on factors that influence the presence of cardiovascular disease in the State of Cleveland. In predicting cardiovascular disease, a logistic regression algorithm will be used to see the interrelation between the dependent variable and the independent variables involved. With this research, it is expected to be able to increase readers' knowledge and insight related to how to analyze cardiovascular disease using logistic regression algorithms and the main factors that cause cardiovascular disease.
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Kang. "Development of the U-turn Accident Model at Signalized Intersections in Urban Areas by Logistic Regression Analysis." Journal of the Korean Society of Civil Engineers 34, no. 4 (2014): 1279. http://dx.doi.org/10.12652/ksce.2014.34.4.1279.

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Dissertations / Theses on the topic "Logistic regression analysis model"

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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.
Includes bibliographical references (leaves 82-83). Also available in electronic version. Access restricted to campus users.
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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. Most experimenters will be less familiar with the Gumbel bivariate logistic regression model than with the univariate logistic regression models which comprise its marginals. Therefore, the optimal designs based on the Gumbel model are evaluated based on univariate logistic regression D-efficiencies; conversely, designs derived from the univariate logistic regression model are evaluated with respect to the Gumbel optimality criteria. Further practical considerations motivate an exploration of designs providing a maximum compromise between the three Gumbel-based criteria D, Q and T. Finally, 5-point designs which can be generated by fitted equations are proposed as a practical option for experimental use.
Ph. D.
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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.
Title from PDF t.p. (viewed on Sept. 8, 2009) Includes bibliographical references (p. 85-89). Also issued in print.
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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 assumed to be a scoring model used by a financial institution in the current location. The financial institution is then assumed to expand into a new economic location where there is limited data. The priors from the model on the “old” data are then combined in a Bayesian model with the “new” data to obtain a model which represents all the available information. The predictive performance of this Bayesian model is compared to a model which does not make use of any prior information. It is found that the use of relevant prior information improves the predictive performance when the size of the “new” data is small. As the size of the “new” data increases, the importance of including prior information decreases
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MATYATIM, Rosliza. "The Classification Model for Corporate Failures in Malaysia." Graduate School of International Development, Nagoya University, 2006. http://hdl.handle.net/2237/7314.

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Lee, Michelle Oi San. "Sample size calculation for testing an interaction effect in a logistic regression under measurement error model /." View Abstract or Full-Text, 2003. http://library.ust.hk/cgi/db/thesis.pl?MATH%202003%20LEE.

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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2003.
Includes bibliographical references (leaves 66-67). Also available in electronic version. Access restricted to campus users.
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Pavlik, Kaylin. "A Model to Predict Matriculation of Concordia College Applicants." Thesis, North Dakota State University, 2017. https://hdl.handle.net/10365/28463.

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Colleges and universities are under mounting pressure to meet enrollment goals in the face of declining college attendance. Insight into student-level probability of enrollment, as well as the identification of features relevant in student enrollment decisions, would assist in the allocation of marketing and recruitment resources and the development of future yield programs. A logistic regression model was fit to predict which applicants will ultimately matriculate (enroll) at Concordia College. Demographic, geodemographic and behavioral features were used to build a logistic regression model to assign probability of enrollment to each applicant. Behaviors indicating interest (campus visits, submitting a deposit) and residing in a zip code with high alumni density were found to be strong predictors of matriculation. The model was fit to minimize false negative rate, which was limited to 18.1 percent, compared to 50-60 percent reported by comparable studies. Overall, the model was 80.13 percent accurate.
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Kim, Hyun-Joo. "Model selection criteria based on Kullback information measures for Weibull, logistic, and nonlinear regression frameworks /." free to MU campus, to others for purchase, 2000. http://wwwlib.umi.com/cr/mo/fullcit?p9988677.

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Spaulding, Aleigha, Jessica R. Barbee, Nathan L. Hale, Shimin Zheng, Michael G. Smith, Edward Francis Leinaar, and Amal Jamil Khoury. "Analysis of Birth Rate and Predictors Using Linear Regression Model and Propensity Score Matching Method." Digital Commons @ East Tennessee State University, 2019. https://dc.etsu.edu/asrf/2019/schedule/35.

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Evaluating the effectiveness of an intervention can pose challenges if there is not an adequate control group. The effects of the intervention can be distorted by observable differences in the characteristics of the control and treatment groups. Propensity score matching can be used to confirm the outcomes of an intervention are due to the treatment and not other characteristics that may also explain the intervention effects. Propensity score matching is an advanced statistical technique that uses background information on the characteristics of the study population to establish matched pairs of treated participants and controls. This technique improves the quality of control groups and allowing for a better evaluation of the true effects of an intervention. The purpose of this study was to implement this technique to derive county-level matches across the southeastern United States for existing counties within a single state where future statewide initiatives are planned. Statistical analysis was performed using SAS 9.4 (Cary, NC, USA). A select set of key county-level socio-demographic measures theoretically relevant for deriving appropriate matches was examined. These include the proportion of African Americans in population, population density, and proportion of the female population below poverty level. To derive the propensity-matched counties, a logistic regression model with the state of primary interest as the outcome was conducted. The baseline covariates of interest were included in the model and used to predict the probability of a county being in the state of primary interest; this acts as the propensity score used to derive matched controls. A caliper of 0.2 was used to ensure the ratio of the variance of the linear propensity score in the control group to the variance of the linear propensity score in the treatment group is close to 1. The balance of covariates before and after the propensity score matching were assessed to determine if significant differences in each respective covariate persisted after the propensity score matching. Before matching, a significant difference was found in the proportion of African Americans in control group (21.08%, n=3,450) and treatment group (36.95%, n=230) using the t-test (P<0.0001). The percent of females below poverty level showed significant difference between control and treatment group (P=0.0264). The t-test of population density also showed significant differences between the groups (P=0.0424). After matching, the mean differences for the treated-control groups were all zero for these three covariates and the characteristics were no longer showing any significant differences between the two groups. This study found that the use of propensity score matching methods improved the accuracy of matched controls. Ensuring that the control and treatment counties have statistically similar characteristics is important for improving the rigor of future studies examining county-level outcomes. Propensity score matching does not account for unobserved differences between the treatment and control groups that may affect the observed outcomes; however, it does ensure that the observable characteristics between the groups are statistically similar.This method reduces the threat to internal validity that observable characteristics pose on interventions by matching for these potentially confounding characteristics.
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Hardin, Patrik, and Sam Tabari. "Modelling Non-life Insurance Policyholder Price Sensitivity : A Statistical Analysis Performed with Logistic Regression." Thesis, KTH, Matematisk statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-209773.

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This bachelor thesis within mathematical statistics studies the possibility of modelling the renewal probability for commercial non-life insurance policyholders. The project was carried out in collaboration with the non-life insurance company If P&C Insurance Ltd. at their headquarters in Stockholm, Sweden. The paper includes an introduction to underlying concepts within insurance and mathematics and a detailed review of the analytical process followed by a discussion and conclusions. The first stages of the project were the initial collection and processing of explanatory insurance data and the development of a logistic regression model for policy renewal. An initial model was built and modern methods of mathematics and statistics were applied in order obtain a final model consisting of 9 significant characteristics. The regression model had a predictive power of 61%. This suggests that it to a certain degree is possible to predict the renewal probability of non-life insurance policyholders based on their characteristics. The results from the final model were ultimately translated into a measure of price sensitivity which can be implemented in both pricing models and CRM systems. We believe that price sensitivity analysis, if done correctly, is a natural step in improving the current pricing models in the insurance industry and this project provides a foundation for further research in this area.
Detta kandidatexamensarbete inom matematisk statistik undersöker möjligheten att modellera förnyelsegraden för kommersiella skadeförsärkringskunder. Arbetet utfördes i samarbete med If Skadeförsäkring vid huvudkontoret i Stockholm, Sverige. Uppsatsen innehåller en introduktion till underliggande koncept inom försäkring och matematik samt en utförlig översikt över projektets analytiska process, följt av en diskussion och slutsatser. De huvudsakliga delarna av projektet var insamling och bearbetning av förklarande försäkringsdata samt utvecklandet och tolkningen av en logistisk regressionsmodell för förnyelsegrad. En första modell byggdes och moderna metoder inom matematik och statistik utfördes för att erhålla en slutgiltig regressionsmodell uppbyggd av 9  signifikanta kundkaraktäristika. Regressionsmodellen hade en förklaringsgrad av 61% vilket pekar på att det till en viss grad är möjligt att förklara förnyelsegraden hos försäkringskunder utifrån dessa karaktäristika. Resultaten från den slutgiltiga modellen översattes slutligen till ett priskänslighetsmått vilket möjliggjorde implementering i prissättningsmodeller samt CRM-system. Vi anser att priskänslighetsanalys, om korrekt genomfört, är ett naturligt steg i utvecklingen av dagens prissättningsmodeller inom försäkringsbranschen och detta projekt lägger en grund för fortsatta studier inom detta område.
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Books on the topic "Logistic regression analysis model"

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

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Houston, Walter M. Empirical Bayes estimates of parameters from the logistic regression model. Iowa City, Iowa: ACT, Inc., 1997.

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Houston, Walter M. Empirical Bayes estimates of parameters from the logistic regression model. Iowa City, Iowa: ACT, Inc., 1997.

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Regression modeling strategies: With applications to linear models, logistic regression, and survival analysis. New York: Springer, 2001.

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Bent, Gardner C. A logistic regression equation for estimating the probability of a stream flowing perennially in Massachusetts. Northborough, Mass: U.S. Dept. of the Interior, U.S. Geological Survey, 2002.

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Bent, Gardner C. A logistic regression equation for estimating the probability of a stream flowing perennially in Massachusetts. Northborough, Mass: U.S. Dept. of the Interior, U.S. Geological Survey, 2002.

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Bent, Gardner C. A logistic regression equation for estimating the probability of a stream flowing perennially in Massachusetts. Northborough, Mass: U.S. Dept. of the Interior, U.S. Geological Survey, 2002.

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Bent, Gardner C. A logistic regression equation for estimating the probability of a stream flowing perennially in Massachusetts. Northborough, MA (10 Bearfoot Rd., Northborough 01532): U.S. Department of the Interior, U.S. Geological Survey, 2002.

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Murphy, Karl. Nested Analysis: Identifying and Applying Qualitative Illustration Support for Logistic Regression Models. 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526496478.

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Spray, Judith A. Comparison of loglinear and logistic regression models for detecting changes in proportions. Iowa City: American College Testing Program, 1988.

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Book chapters on the topic "Logistic regression analysis model"

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Friendly, Michael, David Meyer, and Achim Zeileis. "Logistic Regression Models." In Discrete Data Analysis with R, 261–322. Boca Raton : Taylor & Francis, 2016. | Series: Chapman & hall/CRC texts in statistical science series ; 120 | “A CRC title.”: Chapman and Hall/CRC, 2015. http://dx.doi.org/10.1201/b19022-10.

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Rocca, Michele La. "Robust Inference in the Logistic Regression Model." In Advances in Classification and Data Analysis, 209–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-59471-7_26.

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Adèr, H. J., Joop Kuik, and H. A. van Rossum. "Parallel Model Selection in Logistic Regression Analysis." In COMPSTAT, 163–68. Heidelberg: Physica-Verlag HD, 1996. http://dx.doi.org/10.1007/978-3-642-46992-3_14.

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Bianco, Ana M., and Víctor J. Yohai. "Robust Estimation in the Logistic Regression Model." In Robust Statistics, Data Analysis, and Computer Intensive Methods, 17–34. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4612-2380-1_2.

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Verma, J. P. "Logistic Regression: Developing a Model for Risk Analysis." In Data Analysis in Management with SPSS Software, 413–42. India: Springer India, 2012. http://dx.doi.org/10.1007/978-81-322-0786-3_13.

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Mukhopadhyay, Parimal. "Analysis of Categorical Data Under Logistic Regression Model." In Complex Surveys, 157–77. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0871-9_6.

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Takabe, Isao, and Satoshi Yamashita. "New Statistical Matching Method Using Multinomial Logistic Regression Model." In Studies in Classification, Data Analysis, and Knowledge Organization, 265–74. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3311-2_21.

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Torsney, B., and J. López-Fidalgo. "Minimax Designs for Logistic Regression in a Compact Interval." In mODa 6 — Advances in Model-Oriented Design and Analysis, 243–50. Heidelberg: Physica-Verlag HD, 2001. http://dx.doi.org/10.1007/978-3-642-57576-1_27.

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Li, Huichan, Zhiju Chen, Xiaohui Li, and Yadan Yan. "Friendliness Analysis for Bike Trips on Urban Roads Using Logistic Regression Model." In Smart Innovation, Systems and Technologies, 175–82. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8683-1_18.

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Maneejuk, Paravee, Woraphon Yamaka, and Duentemduang Nachaingmai. "Bayesian Analysis of the Logistic Kink Regression Model Using Metropolis-Hastings Sampling." In Beyond Traditional Probabilistic Methods in Economics, 1073–83. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04200-4_78.

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Conference papers on the topic "Logistic regression analysis model"

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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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Zou, Xiaonan, Yong Hu, Zhewen Tian, and Kaiyuan Shen. "Logistic Regression Model Optimization and Case Analysis." In 2019 IEEE 7th International Conference on Computer Science and Network Technology (ICCSNT). IEEE, 2019. http://dx.doi.org/10.1109/iccsnt47585.2019.8962457.

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Cavusoglu, Behiye, Kemal Cek, and Serife Z. Eyupoglu. "Modelling job satisfaction using a logistic regression model." In INTERNATIONAL CONFERENCE ON ANALYSIS AND APPLIED MATHEMATICS (ICAAM 2020). AIP Publishing, 2021. http://dx.doi.org/10.1063/5.0040383.

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Araveeporn, Autcha, and Choojai Kuharatanachai. "Comparing Penalized Regression Analysis of Logistic Regression Model with Multicollinearity." In the 2019 2nd International Conference. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3343485.3343487.

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Zhou, Siyuan, and Ya Zhang. "Active learning for cost-sensitive classification using logistic regression model." In 2016 IEEE International Conference on Big Data Analysis (ICBDA). IEEE, 2016. http://dx.doi.org/10.1109/icbda.2016.7509840.

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Amit, Norani Binti, Hasimah Binti Sapiri, and Zahayu Binti Md Yusof. "Logistic regression analysis on the determinants of homeownership." In INTERNATIONAL UZBEKISTAN-MALAYSIA CONFERENCE ON “COMPUTATIONAL MODELS AND TECHNOLOGIES (CMT2020)”: CMT2020. AIP Publishing, 2021. http://dx.doi.org/10.1063/5.0057251.

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Xu, Easton Li, Xiaoning Qian, Tie Liu, and Shuguang Cui. "Pairwise interaction analysis of logistic regression models." In 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2016. http://dx.doi.org/10.1109/globalsip.2016.7905829.

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Ghahramani, Mohammadhossein, MengChu Zhou, Chi Tin Hon, and Gang Wang. "Retention analysis based on a logistic regression model: A case study." In 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC). IEEE, 2018. http://dx.doi.org/10.1109/icnsc.2018.8361375.

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Yunjia, Li, Zhou Anhui, Qin Zijian, Wang Fei, Liu Rulei, and Chen Guoyan. "Analysis of Space Crowdsourcing Mission Completion Based on Logistic Regression Model." In 2018 IEEE 3rd International Conference on Cloud Computing and Internet of Things (CCIOT). IEEE, 2018. http://dx.doi.org/10.1109/cciot45285.2018.9032436.

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Chen, Xiaoxin, and Rong Ye. "Identification Model of Logistic Regression Analysis on Listed Firms' Frauds in China." In 2009 Second International Workshop on Knowledge Discovery and Data Mining (WKDD). IEEE, 2009. http://dx.doi.org/10.1109/wkdd.2009.35.

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Reports on the topic "Logistic regression analysis model"

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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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2

Hutny, W. P., and J. T. Price. Analysis and regression model of blast furnace coal injection. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1987. http://dx.doi.org/10.4095/304361.

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3

Krishnaiah, P. R., and S. Sarkar. Principal Component Analysis Under Correlated Multivariate Regression Equations Model. Fort Belvoir, VA: Defense Technical Information Center, April 1985. http://dx.doi.org/10.21236/ada160266.

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4

Sun, T. C. Using Regression Analysis Method to Develop a Material Outgassing Model. Office of Scientific and Technical Information (OSTI), February 2019. http://dx.doi.org/10.2172/1499976.

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5

Haubrich, Julia, Sarah Benz, Ullrich Isermann, Beat Schäffer, Rainer Schmid, Dirk Schreckenberg, Jean Marc Wunderli, and Rainer Guski. Leq+X - Lärmexposition, Ereignishäufigkeiten und Belästigung: Re-Analyse von Daten zur Belästigung und Schlafstörung durch Fluglärm an deutschen und Schweizer Flughäfen. Universitätsbibliothek der Ruhr-Universität Bochum, 2020. http://dx.doi.org/10.46586/rub.164.139.

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In this study, part of the data sets from 4 large Swiss and German aircraft noise impact studies are re-analysed using logistic multi-level regression models. The aim is to investigate the assumptions that the prediction of a) the percentage of persons highly annoyed by aircraft noise or b) the percentage of persons highly sleep disturbed by aircraft noise can be improved if (i) instead of the energy-equivalent continuous noise level alone, either additional or alternative, more frequency-based aircraft noise metrics and (ii) also airport-specific characteristics are used as predictors. The results support both assumptions; both regarding the percentage of persons highly annoyed and regarding the percentage of persons highly sleep disturbed.
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Gaver, Donald P., Patricia A. Jacobs, and I. G. O'Muircheartaigh. Regression Analysis of Hierarchical Poisson-Like Event Rate Data: Super- Population Model Effect on Predictions. Fort Belvoir, VA: Defense Technical Information Center, August 1990. http://dx.doi.org/10.21236/ada230297.

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7

Kim, Changmo, Ghazan Khan, Brent Nguyen, and Emily L. Hoang. Development of a Statistical Model to Predict Materials’ Unit Prices for Future Maintenance and Rehabilitation in Highway Life Cycle Cost Analysis. Mineta Transportation Institute, December 2020. http://dx.doi.org/10.31979/mti.2020.1806.

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The main objectives of this study are to investigate the trends in primary pavement materials’ unit price over time and to develop statistical models and guidelines for using predictive unit prices of pavement materials instead of uniform unit prices in life cycle cost analysis (LCCA) for future maintenance and rehabilitation (M&R) projects. Various socio-economic data were collected for the past 20 years (1997–2018) in California, including oil price, population, government expenditure in transportation, vehicle registration, and other key variables, in order to identify factors affecting pavement materials’ unit price. Additionally, the unit price records of the popular pavement materials were categorized by project size (small, medium, large, and extra-large). The critical variables were chosen after identifying their correlations, and the future values of each variable were predicted through time-series analysis. Multiple regression models using selected socio-economic variables were developed to predict the future values of pavement materials’ unit price. A case study was used to compare the results between the uniform unit prices in the current LCCA procedures and the unit prices predicted in this study. In LCCA, long-term prediction involves uncertainties due to unexpected economic trends and industrial demand and supply conditions. Economic recessions and a global pandemic are examples of unexpected events which can have a significant influence on variations in material unit prices and project costs. Nevertheless, the data-driven scientific approach as described in this research reduces risk caused by such uncertainties and enables reasonable predictions for the future. The statistical models developed to predict the future unit prices of the pavement materials through this research can be implemented to enhance the current LCCA procedure and predict more realistic unit prices and project costs for the future M&R activities, thus promoting the most cost-effective alternative in LCCA.
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8

Over, Thomas, Riki Saito, Andrea Veilleux, Padraic O’Shea, Jennifer Sharpe, David Soong, and Audrey Ishii. Estimation of Peak Discharge Quantiles for Selected Annual Exceedance Probabilities in Northeastern Illinois. Illinois Center for Transportation, June 2016. http://dx.doi.org/10.36501/0197-9191/16-014.

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This report provides two sets of equations for estimating peak discharge quantiles at annual exceedance probabilities (AEPs) of 0.50, 0.20, 0.10, 0.04, 0.02, 0.01, 0.005, and 0.002 (recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years, respectively) for watersheds in Illinois based on annual maximum peak discharge data from 117 watersheds in and near northeastern Illinois. One set of equations was developed through a temporal analysis with a two-step least squares-quantile regression technique that measures the average effect of changes in the urbanization of the watersheds used in the study. The resulting equations can be used to adjust rural peak discharge quantiles for the effect of urbanization, and in this study the equations also were used to adjust the annual maximum peak discharges from the study watersheds to 2010 urbanization conditions. The other set of equations was developed by a spatial analysis. This analysis used generalized least-squares regression to fit the peak discharge quantiles computed from the urbanization-adjusted annual maximum peak discharges from the study watersheds to drainage-basin characteristics. The peak discharge quantiles were computed by using the Expected Moments Algorithm following the removal of potentially influential low floods defined by a multiple Grubbs-Beck test. To improve the quantile estimates, regional skew coefficients were obtained from a newly developed regional skew model in which the skew increases with the urbanized land use fraction. The skew coefficient values for each streamgage were then computed as the variance-weighted average of at-site and regional skew coefficients. The drainage-basin characteristics used as explanatory variables in the spatial analysis include drainage area, the fraction of developed land, the fraction of land with poorly drained soils or likely water, and the basin slope estimated as the ratio of the basin relief to basin perimeter. This report also provides: (1) examples to illustrate the use of the spatial and urbanization-adjustment equations for estimating peak discharge quantiles at ungaged sites and to improve flood-quantile estimates at and near a gaged site; (2) the urbanization-adjusted annual maximum peak discharges and peak discharge quantile estimates at streamgages from 181 watersheds including the 117 study watersheds and 64 additional watersheds in the study region that were originally considered for use in the study but later deemed to be redundant. The urbanization-adjustment equations, spatial regression equations, and peak discharge quantile estimates developed in this study will be made available in the web-based application StreamStats, which provides automated regression-equation solutions for user-selected stream locations. Figures and tables comparing the observed and urbanization-adjusted peak discharge records by streamgage are provided at http://dx.doi.org/10.3133/sir20165050 for download.
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