Journal articles: '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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Ghosh, D., and Z. Yuan. "An improved model averaging scheme for logistic regression." Journal of Multivariate Analysis 100, no. 8 (September 2009): 1670–81. http://dx.doi.org/10.1016/j.jmva.2009.01.006.

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Cai, Junmeng, Ronghou Liu, and Chen Sun. "Logistic Regression Model for Isoconversional Kinetic Analysis of Cellulose Pyrolysis." Energy & Fuels 22, no. 2 (March 2008): 867–70. http://dx.doi.org/10.1021/ef7006672.

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Li, Yong, Xiaoqian Jiang, Shuang Wang, Hongkai Xiong, and Lucila Ohno-Machado. "VERTIcal Grid lOgistic regression (VERTIGO)." Journal of the American Medical Informatics Association 23, no. 3 (November 9, 2015): 570–79. http://dx.doi.org/10.1093/jamia/ocv146.

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Objective To develop an accurate logistic regression (LR) algorithm to support federated data analysis of vertically partitioned distributed data sets. Material and Methods We propose a novel technique that solves the binary LR problem by dual optimization to obtain a global solution for vertically partitioned data. We evaluated this new method, VERTIcal Grid lOgistic regression (VERTIGO), in artificial and real-world medical classification problems in terms of the area under the receiver operating characteristic curve, calibration, and computational complexity. We assumed that the institutions could “align” patient records (through patient identifiers or hashed “privacy-protecting” identifiers), and also that they both had access to the values for the dependent variable in the LR model (eg, that if the model predicts death, both institutions would have the same information about death). Results The solution derived by VERTIGO has the same estimated parameters as the solution derived by applying classical LR. The same is true for discrimination and calibration over both simulated and real data sets. In addition, the computational cost of VERTIGO is not prohibitive in practice. Discussion There is a technical challenge in scaling up federated LR for vertically partitioned data. When the number of patients m is large, our algorithm has to invert a large Hessian matrix. This is an expensive operation of time complexity O(m3) that may require large amounts of memory for storage and exchange of information. The algorithm may also not work well when the number of observations in each class is highly imbalanced. Conclusion The proposed VERTIGO algorithm can generate accurate global models to support federated data analysis of vertically partitioned data.

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Lee, Chun Yin. "Nested logistic regression models and ΔAUC applications: Change-point analysis." Statistical Methods in Medical Research 30, no. 7 (June 14, 2021): 1654–66. http://dx.doi.org/10.1177/09622802211022377.

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The area under the receiver operating characteristic curve (AUC) is one of the most popular measures for evaluating the performance of a predictive model. In nested models, the change in AUC (ΔAUC) can be a discriminatory measure of whether the newly added predictors provide significant improvement in terms of predictive accuracy. Recently, several authors have shown rigorously that ΔAUC can be degenerate and its asymptotic distribution is no longer normal when the reduced model is true, but it could be the distribution of a linear combination of some [Formula: see text] random variables [ 1 , 2 ]. Hence, the normality assumption and existing variance estimate cannot be applied directly for developing a statistical test under the nested models. In this paper, we first provide a brief review on the use of ΔAUC for comparing nested logistic models and the difficulty of retrieving the reference distribution behind. Then, we present a special case of the nested logistic regression models that the newly added predictor to the reduced model contains a change-point in its effects. A new test statistic based on ΔAUC is proposed in this setting. A simple resampling scheme is proposed to approximate the critical values for the test statistic. The inference of the change-point parameter is done via m-out-of- n bootstrap. Large-scale simulation is conducted to evaluate the finite-sample performance of the ΔAUC test for the change-point model. The proposed method is applied to two real-life datasets for illustration.

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Li, Yu Qi, Huan Zhang, and Yi Ran Liu. "Regression Analysis of Observed Foundation Settlement by Modified Logistic Growth Model." Advanced Materials Research 250-253 (May 2011): 2583–87. http://dx.doi.org/10.4028/www.scientific.net/amr.250-253.2583.

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Logistic model is modified through introducing the pseudo construction settlement. Based on the observed settlement data of foundation in Yangshan deepwater port project, Logistic growth model and modified Logistic growth model are used for nonlinear regression analysis of foundation settlement respectively. It is indicated that the fitting curves by using modified Logistic growth model agree better with the observed settlement values than those by using Logistic growth model and that the correlation coefficients by using modified Logistic growth model are also bigger. Model parameters of different geological conditions obtained by nonlinear regression analysis can be used for significant reference to foundation settlement prediction of similar geological condition in other deepwater port.

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Xiao, Daiquan, Xuecai Xu, and Li Duan. "Spatial-Temporal Analysis of Injury Severity with Geographically Weighted Panel Logistic Regression Model." Journal of Advanced Transportation 2019 (August 20, 2019): 1–15. http://dx.doi.org/10.1155/2019/8521649.

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This study is intended to investigate the influencing factors of injury severity by considering the heterogeneity issue of unobserved factors at different arterials and the spatial attributes in geographically weighted regression models. To achieve the objectives, geographically weighted panel logistic regression model was developed, in which the geographically weighted logistic regression model addressed the injury severity from the spatial perspective, while the panel data model accommodated the heterogeneity attributed to unobserved factors from the temporal perspective. The geo-crash data of Las Vegas metropolitan area from 2014 to 2016 was collected, involving 27 arterials with 25,029 injury samples. By comparing the conventional logistic regression model and geographically weighted logistic regression models, the geographically weighted panel logistic regression model showed preference to the other models. Results revealed that four main factors, human-beings (drivers/pedestrians/cyclists), vehicles, roadway, and environment, were potentially significant factors of increasing the injury severity. The findings provide useful insights for practitioners and policy makers to improve safety along arterials.

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Brito-Pacheco, Carlos, Carlos Brito-Loeza, and Anabel Martin-Gonzalez. "A regularized logistic regression based model for supervised learning." Journal of Algorithms & Computational Technology 14 (January 2020): 174830262097153. http://dx.doi.org/10.1177/1748302620971535.

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In this work, we introduce a new regularized logistic model for the supervised classification problem. Current logistic models have become the preferred tools for supervised classification in many situations. They mostly use either L1 or L2 regularization of the weight vector of parameters. Here we take a different approach by applying regularization not to the weight vector but to the gradient vector of the function representing the separating hyper-surface. We present the mathematical analysis of the model in its continuous setting and provide experimental evidence to show that the new model is competitive with state of the art models.

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Lakshmi, Rayasam, Satya R. B. Divya, and R. Valarmathi. "Analysis of sentiment in twitter using logistic regression." International Journal of Engineering & Technology 7, no. 3.3 (June 8, 2018): 619. http://dx.doi.org/10.14419/ijet.v7i2.33.14849.

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Social Platforms such as Twitter, Facebook are not always the good places and when explored there exists a dark side to it. The main objective of this research is to identify the sentiment of a tweet in twitter and also further analyse a twitter accounts activity. Logistic regression and text blob are used to identify the sentiment of the tweets, as for the taken datasets they provided the highest accuracy when compared with other algorithms such as GaussianNB, BernoulliNB, SVM. The datasets are extracted from twitter and split into training and testing data using which the model is trained to classify the sentiments of a tweet and then the analysis of a twitter account is done.

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Cheng, Si, Yun Sheng Wang, and Fei Yu Chen. "Geohazard Risk Assessment Method Based on Logistic Regression Model." Advanced Materials Research 588-589 (November 2012): 1934–37. http://dx.doi.org/10.4028/www.scientific.net/amr.588-589.1934.

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Logistic regression model refers a regress analysis contains two types of variants. In geohazard analysis, each geological factor can be defined as independent variable, whether a geohazard happened or not can be defined as a dependent variable. 1 represents an occurrence of a hazard while 0 represents a hazard doesn’t break out. Because those factors aren’t continual variable, lineal regress is inadequate to deduce the relationship of such kind of independent and dependent variable. Therefore using logistic regress method is a feasible way to solve such technique problem.

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Moineddin, Rahim, Christopher Meaney, and Eva Grunfeld. "On the analysis of composite measures of quality in medical research." Statistical Methods in Medical Research 26, no. 2 (October 8, 2014): 633–60. http://dx.doi.org/10.1177/0962280214553330.

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Composite endpoints are commonplace in biomedical research. The complex nature of many health conditions and medical interventions demand that composite endpoints be employed. Different approaches exist for the analysis of composite endpoints. A Monte Carlo simulation study was employed to assess the statistical properties of various regression methods for analyzing binary composite endpoints. We also applied these methods to data from the BETTER trial which employed a binary composite endpoint. We demonstrated that type 1 error rates are poor for the Negative Binomial regression model and the logistic generalized linear mixed model (GLMM). Bias was minimal and power was highest in the binomial logistic regression model, the linear regression model, the Poisson (corrected for over-dispersion) regression model and the common effect logistic generalized estimating equation (GEE) model. Convergence was poor in the distinct effect GEE models, the logistic GLMM and some of the zero-one inflated beta regression models. Considering the BETTER trial data, the distinct effect GEE model struggled with convergence and the collapsed composite method estimated an effect, which was greatly attenuated compared to other models. All remaining models suggested an intervention effect of similar magnitude. In our simulation study, the binomial logistic regression model (corrected for possible over/under-dispersion), the linear regression model, the Poisson regression model (corrected for over-dispersion) and the common effect logistic GEE model appeared to be unbiased, with good type 1 error rates, power and convergence properties.

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Janampa-Sarmiento, P. C., R. Takata, T. M. Freitas, M. M. B. Pereira, L. Sá-Freire, V. Lugert, C. Sarturi, and M. M. Pereira. "Nonlinear regression analysis of length growth in cultured rainbow trout." Arquivo Brasileiro de Medicina Veterinária e Zootecnia 72, no. 5 (September 2020): 1778–88. http://dx.doi.org/10.1590/1678-4162-11776.

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ABSTRACT Length growth as a function of time has a non-linear relationship, so nonlinear equations are recommended to represent this kind of curve. We used six nonlinear models to calculate the length gain of rainbow trout (Oncorhynchus mykiss) during the final grow-out phase of 98 days under three different feed types in triplicate groups. We fitted the von Bertalanffy, Gompertz, Logistic, Brody, Power Function, and Exponential equations to individual length-at-age data of 900 fish. Equations were fitted to the data based on the least square method using the Marquardt iterative algorithm. Accuracy of the fitted models was evaluated using a model performance metrics combining mean squared residuals (MSR), mean absolute error (MAE) and Akaike's Information Criterion corrected for small sample sizes (AICc). All models converged in all cases tested. Evaluation criteria for the Logistic model indicated the best overall fit (0.67 of combined metric MSR, MAE and AICc) under all different feeding types, followed by the Exponential model (0.185), and the von Bertalanffy and Brody model (0.074, respectively). Additionally, ∆AICc results identify the Logistic and Gompertz models as being substantially supported by the data in 100% of cases. The logistic model can be suggested for length growth prediction in aquaculture of rainbow trout.

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Hashimoto, Elizabeth M., Edwin M. M. Ortega, Gauss M. Cordeiro, and G. G. Hamedani. "The Log-gamma-logistic Regression Model: Estimation, Sensibility and Residual Analysis." Journal of Statistical Theory and Applications 16, no. 4 (2017): 547. http://dx.doi.org/10.2991/jsta.2017.16.4.9.

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Xia, Fan, Jun Chen, Wing Kam Fung, and Hongzhe Li. "A Logistic Normal Multinomial Regression Model for Microbiome Compositional Data Analysis." Biometrics 69, no. 4 (October 15, 2013): 1053–63. http://dx.doi.org/10.1111/biom.12079.

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Satoh, Kenichi, Tetsuji Tonda, and Shizue Izumi. "Logistic Regression Model for Survival Time Analysis Using Time-Varying Coefficients." American Journal of Mathematical and Management Sciences 35, no. 4 (August 31, 2016): 353–60. http://dx.doi.org/10.1080/01966324.2016.1215945.

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Kumar, Rakesh, S. Nandy, Reshu Agarwal, and S. P. S. Kushwaha. "Forest cover dynamics analysis and prediction modeling using logistic regression model." Ecological Indicators 45 (October 2014): 444–55. http://dx.doi.org/10.1016/j.ecolind.2014.05.003.

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Wang, Xiaoyin. "Bayesian Relative Importance Analysis of Logistic Regression Models." Journal of Statistics Applications & Probability Letters 3, no. 2 (May 1, 2016): 53–69. http://dx.doi.org/10.18576/jsapl/030201.

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Taneichi, Nobuhiro, Yuri Sekiya, and Jun Toyama. "Improved transformed deviance statistic for testing a logistic regression model." Journal of Multivariate Analysis 102, no. 9 (October 2011): 1263–79. http://dx.doi.org/10.1016/j.jmva.2011.04.010.

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Kang, Qiong. "Correlation Analysis of Stocks and PMI Index Based on Logistic Regression Model." Journal of Sensors 2021 (September 15, 2021): 1–12. http://dx.doi.org/10.1155/2021/1089266.

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In order to explore the correlation between stocks and the PMI index, based on the generalized logistic loss and margin distribution, this paper designs a margin distribution logistic regression model that is easy to optimize, has robustness, and generalization ability, and gives a multiclass margin distribution logistic regression framework. This framework can be used to perform two-classification, multiclassification, and feature selection tasks. Moreover, this paper gives a training algorithm for margin distribution logistic regression on large-scale data sets through the pairwise stochastic gradient descent method. In addition, this paper combines the logistic regression model to construct a correlation analysis model between stocks and PMI index and uses the PMI data of the National Bureau of Statistics as a sample to design experiments to verify the performance of the system model constructed in this paper. From the experimental analysis, it can be seen that the algorithm constructed in this paper has a certain effect, and the strong correlation between PMI and stocks has been further verified.

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Simmonds, Mark C., and Julian PT Higgins. "A general framework for the use of logistic regression models in meta-analysis." Statistical Methods in Medical Research 25, no. 6 (July 11, 2016): 2858–77. http://dx.doi.org/10.1177/0962280214534409.

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Where individual participant data are available for every randomised trial in a meta-analysis of dichotomous event outcomes, “one-stage” random-effects logistic regression models have been proposed as a way to analyse these data. Such models can also be used even when individual participant data are not available and we have only summary contingency table data. One benefit of this one-stage regression model over conventional meta-analysis methods is that it maximises the correct binomial likelihood for the data and so does not require the common assumption that effect estimates are normally distributed. A second benefit of using this model is that it may be applied, with only minor modification, in a range of meta-analytic scenarios, including meta-regression, network meta-analyses and meta-analyses of diagnostic test accuracy. This single model can potentially replace the variety of often complex methods used in these areas. This paper considers, with a range of meta-analysis examples, how random-effects logistic regression models may be used in a number of different types of meta-analyses. This one-stage approach is compared with widely used meta-analysis methods including Bayesian network meta-analysis and the bivariate and hierarchical summary receiver operating characteristic (ROC) models for meta-analyses of diagnostic test accuracy.

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MENDES, ALEXANDRE C., and NASSER FARD. "BINARY LOGISTIC REGRESSION AND PHM ANALYSIS FOR RELIABILITY DATA." International Journal of Reliability, Quality and Safety Engineering 21, no. 05 (September 18, 2014): 1450023. http://dx.doi.org/10.1142/s0218539314500235.

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This study proposes a modification for the binary logistic regression to treat time-dependent covariates for reliability studies. The proportional hazard model (PHM) properties are well suited for modeling survival data when there are categorical predictors; as it compares hazards to a reference category. However, time-dependent covariates present a challenge for the analysis as stratification does not produce hazards for the covariate stratified or creation of dummy time-dependent covariates faces difficulty on selecting the time interval for the interaction and the coefficient results may be difficult to interpret. The findings show that the logistic regression can provide equal or better results than the PHM applied for reliability analysis when time-dependent covariate is evaluated. The PHM is potentially preferred to address data set without time-dependent variables as it does not require any data manipulation. The logistic regression ignores the information on timing of the events; which is corrected by breaking each subject survival history into a set of discrete time intervals that are treated as distinct observations evaluated as a binary distribution. Recurrent events can be addressed by both methods with proper correction for lack of heterogeneity. The application of the modified logistic regression model for the study of reliability is innovative and with readily potential application for step-stress time-dependent accelerated life testing.

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Miroshnychenko, V. O. "Residual analysis in regression mixture model." Bulletin of Taras Shevchenko National University of Kyiv. Series: Physics and Mathematics, no. 3 (2019): 8–16. http://dx.doi.org/10.17721/1812-5409.2019/3.1.

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We consider data in which each observed subject belongs to one of different subpopulations (components). The true number of component which a subject belongs to is unknown, but the researcher knows the probabilities that a subject belongs to a given component (concentration of the component in the mixture). The concentrations are different for different observations. So the distribution of the observed data is a mixture of components’ distributions with varying concentrations. A set of variables is observed for each subject. Dependence between these variables is described by a nonlinear regression model. The coefficients of this model are different for different components. An estimator is proposed for these regression coefficients estimation based on the least squares and generalized estimating equations. Consistency of this estimator is demonstrated under general assumptions. A mixture of logistic regression models with continuous response is considered as an example. It is shown that the general consistency conditions are satisfied for this model under very mild assumptions. Performance of the estimator is assessed by simulations and applied for sociological data analysis. Q-Q diagrams are built for visual comparison of residuals’ distributions.

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Abdulqader, Qais M. "Applying the Binary Logistic Regression Analysis on The Medical Data." Science Journal of University of Zakho 5, no. 4 (December 30, 2017): 330. http://dx.doi.org/10.25271/2017.5.4.388.

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In this paper, the Binary Logistic Regression Analysis BLRA technique has been used and applied for building the best model for Hepatitis disease data using best subsets regression and stepwise procedures and depending on some laboratory tests such as glutamate oxalate transaminase, glutamate pyruvate transaminase, alkaline phosphatase, and total serum bilirubin which represents explanatory variables. Also, the technique has used for classifying persons into two groups which are infected and non-infected with viral Hepatitis disease. A random sample size consists of 200 persons has been selected which represents 86 of uninfected and 114 of infected persons. The results of the analysis showed that first, the two procedures identified the same three explanatory variables out of four and they were statistically significant, and it has been reliable in building the logistic model. And second, the percentage of visible correct classification rate was about 98% which represents the high ability of the model for classification.

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Kushta, Elmira Elmira, and Gladiola Trushaj. "Implementation of the Logistic Regression Model and its Applications." JOURNAL OF ADVANCES IN MATHEMATICS 18 (January 18, 2020): 46–51. http://dx.doi.org/10.24297/jam.v18i.8557.

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The purpose of an analysis using this method is the same as that of any technique in constructing models in statistics, namely to find the best and most reasonable model to describe the relationship between a result variable and a set of variables independent. We are interested in how the costs affect them and if a customer has a travel card. Credit card customers are shown to be 6 times more likely to use it regardless of the cost they make.It is also shown that a customer is more likely to use a travel card when costs increase Through logistic regression, which gives the probability that a result is an exponential function of the independent variables, we will see how through our data we will come to very important conclusions.

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N. Zumaeta, Jorge. "A Logistic Regression Analysis to Assist Welfare Recipients Attaining Employment." Journal of Social Sciences Research, no. 72 (June 25, 2021): 75–91. http://dx.doi.org/10.32861/jssr.72.75.91.

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This study reports on an experiment using logistic regression to uncover the preponderant factors influencing the likelihood of attaining employment by a welfare recipient in Broward County, Florida. Our study considers whether profiling the participants and tailoring the workforce development services based on their respective profiles can increase their likelihood of finding employment (Black et al., 2003). The study finds that our econometric model predicted the probability of employment with reasonably strong reliability. This finding is in alignment with the Welfare Profiling Model of Michigan’s (Barnow et al., 2012; Eberts, 1997;2002) and the Factors Influencing AFDC Duration and Labor Market Outcomes Research Study of Texas (Schexnayder et al., 1991). More specifically, the results indicate that education and prior employment history are significant factors increasing the likelihood of departing from welfare and achieving employment. Furthermore, the study concludes that the number of children, participant’s age, and the ethnicity of the welfare recipient also play a role in breaking away from welfare. The results from the experiment show that using the econometric model to assign services to individuals increases the likelihood of finding employment from 11% to 24% on average. This is a very encouraging finding since it motivates researchers to perform further research in this area of study.

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Elias, Bartholomew. "A Model of Air to Ground Target Acquisition Based on Logistic Regression Analysis." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 44, no. 21 (July 2000): 3–484. http://dx.doi.org/10.1177/154193120004402128.

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Logistic regression, a technique for describing relationships between a binary or dichotomous dependent variable and one or more independent variables that can be either discrete or continuous, is demonstrated to be an effective analytical tool for evaluating data collected using psychophysical methods and signal detection procedures. One specific application of logistic regression is the assessment of operational factors on human performance in visual target acquisition. Visual target acquisition data collected using signal detection procedures were reanalyzed using logistic regression techniques. The application of these logistic regression techniques produced empirically derived psychophysical models of target detection capabilities under various conditions. Such models can be used to predict human performance in visual target acquisition under various operational constraints.

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Yadav, Amit, Li Hui, Mohsin Ali, and Maria Anis. "Analysis of Healthcare Data of Nepal Hospital using Multinomial Logistic Regression Model." INTERNATIONAL JOURNAL OF MANAGEMENT & INFORMATION TECHNOLOGY 11, no. 2 (June 30, 2016): 2720–30. http://dx.doi.org/10.24297/ijmit.v11i2.4864.

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Patient data had been collected from the hospital of Nepal with the help of hospital administration, doctors and patient cooperation. Data scrutiny attempts to shows the significant relationship between disease and factors causal of disease. Research explores the utility of multinomial logistic regression (MLR) technique in health domain and its most beneficial use for categorical data. Paper try to exhibit various factors which results in happening of health disorder and highlight application of data mining technique in healthcare. It is conceived that this work render more accuracy and reliability in detection of factors causal of disease, espial of fraud, helpful for all parties associated with healthcare, reduce cost, lessen time and treatment process.Â

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Sohn, S. Y. "Random effects logistic regression model for ranking efficiency in data envelopment analysis." Journal of the Operational Research Society 57, no. 11 (November 2006): 1289–99. http://dx.doi.org/10.1057/palgrave.jors.2602117.

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Chenini, Ismail, and Mohamed Haythem Msaddek. "Groundwater recharge susceptibility mapping using logistic regression model and bivariate statistical analysis." Quarterly Journal of Engineering Geology and Hydrogeology 53, no. 2 (July 9, 2019): 167–75. http://dx.doi.org/10.1144/qjegh2019-047.

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Duan, Wenjing. "Ordinal Logistic Regression Analysis on Influencing Factors of Space Tourism Expectation Model." Journal of Physics: Conference Series 1651 (November 2020): 012066. http://dx.doi.org/10.1088/1742-6596/1651/1/012066.

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Wang, Liang-Jie, Kazuhide Sawada, and Shuji Moriguchi. "Landslide susceptibility analysis with logistic regression model based on FCM sampling strategy." Computers & Geosciences 57 (August 2013): 81–92. http://dx.doi.org/10.1016/j.cageo.2013.04.006.

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Alvarez-Santiago, Sonia A., F. García-Oliva, and Lucía Varela. "Analysis of vesicular-arbuscular mycorrhizal colonization data with a logistic regression model." Mycorrhiza 6, no. 3 (May 22, 1996): 197–200. http://dx.doi.org/10.1007/s005720050126.

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Sujatha, Evangelin Ramani, and Venkataramana Sridhar. "Landslide Susceptibility Analysis: A Logistic Regression Model Case Study in Coonoor, India." Hydrology 8, no. 1 (March 5, 2021): 41. http://dx.doi.org/10.3390/hydrology8010041.

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Landslides are a common geologic hazard that disrupts the social and economic balance of the affected society. Therefore, identifying zones prone to landslides is necessary for safe living and the minimal disruption of economic activities in the event of the hazard. The factors causing landslides are often a function of the local geo-environmental set-up and need a region-specific study. This study evaluates the site characteristics primarily altered by anthropogenic activities to understand and identify the various factors causing landslides in Coonoor Taluk of Uthagamandalam District in Tamil Nadu, India. Studies on landslide susceptibility show that slope gradient, aspect, relative relief, topographic wetness index, soil type, and land use of the region influence slope instability. Rainfall characteristics have also played a significant role in causing landslides. Logistic Regression, a popular statistical tool used for predictive analysis, is employed to assess the various selected factors’ impact on landslide susceptibility. The factors are weighted and combined in a GIS platform to develop the region’s landslide susceptibility map. This region has a direct link between natural physical systems, hydrology, and humans from the socio-hydrological perspective. The landslide susceptibility map derived using the watershed’s physical and environmental conditions offers the best tool for planning the developmental activities and prioritizing areas for mitigation activities in the region. The Coonoor region’s tourism and agriculture sectors can significantly benefit from identifying zones prone to landslides for their economic stability and growth.

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Qawqzeh, Yousef K., Abdullah S. Bajahzar, Mahdi Jemmali, Mohammad Mahmood Otoom, and Adel Thaljaoui. "Classification of Diabetes Using Photoplethysmogram (PPG) Waveform Analysis: Logistic Regression Modeling." BioMed Research International 2020 (August 11, 2020): 1–6. http://dx.doi.org/10.1155/2020/3764653.

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In this research, the photoplethysmogram (PPG) waveform analysis is utilized to develop a logistic regression-based predictive model for the classification of diabetes. The classifier has three predictors age, b/a, and SP indices in which they achieved an overall accuracy of 92.3% in the prediction of diabetes. In this study, a total of 587 subjects were enrolled. A total of 459 subjects were used for model training and development, while the rest of the 128 subjects were used for model testing and validation. The classifier was able to diagnose 63 patients correctly as diabetes while 27 subjects were wrongly classified as nondiabetes with an accuracy of 70%. Again, the model classified 479 subjects as nondiabetes correctly while it incorrectly classified 18 subjects as diabetes with an accuracy of 96.4%. Finally, the proposed model revealed an overall predictive accuracy of 92.3% which makes it a reliable surrogate measure for diabetes classification and prediction in clinical settings.

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Souza Vasconcelos, Julio Cezar, Gauss M. Cordeiro, Edwin M. M. Ortega, and Elton G. Araújo. "The New Odd Log-Logistic Generalized Inverse Gaussian Regression Model." Journal of Probability and Statistics 2019 (January 10, 2019): 1–13. http://dx.doi.org/10.1155/2019/8575424.

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We define a new four-parameter model called the odd log-logistic generalized inverse Gaussian distribution which extends the generalized inverse Gaussian and inverse Gaussian distributions. We obtain some structural properties of the new distribution. We construct an extended regression model based on this distribution with two systematic structures, which can provide more realistic fits to real data than other special regression models. We adopt the method of maximum likelihood to estimate the model parameters. In addition, various simulations are performed for different parameter settings and sample sizes to check the accuracy of the maximum likelihood estimators. We provide a diagnostics analysis based on case-deletion and quantile residuals. Finally, the potentiality of the new regression model to predict price of urban property is illustrated by means of real data.

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Li, Huifang, Yumin Chen, Susu Deng, Meijie Chen, Tao Fang, and Huangyuan Tan. "Eigenvector Spatial Filtering-Based Logistic Regression for Landslide Susceptibility Assessment." ISPRS International Journal of Geo-Information 8, no. 8 (July 27, 2019): 332. http://dx.doi.org/10.3390/ijgi8080332.

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Logistic regression methods have been widely used for landslide research. However, previous studies have seldom paid attention to the frequent occurrence of spatial autocorrelated residuals in regression models, which indicate a model misspecification problem and unreliable results. This study accounts for spatial autocorrelation by implementing eigenvector spatial filtering (ESF) into logistic regression for landslide susceptibility assessment. Based on a landslide inventory map and 11 landslide predisposing factors, we developed the eigenvector spatial filtering-based logistic regression (ESFLR) model, as well as a conventional logistic regression (LR) model and an autologistic regression (ALR) model for comparison. The three models were evaluated and compared in terms of their prediction capability and model fit. The ESFLR model performed better than the other two models. The overall predictive accuracy of the ESFLR model was 90.53%, followed by the ALR model (76.21%) and the LR model (74.76%), and the areas under the ROC curves for the ESFLR, ALR and LR models were 0.957, 0.828 and 0.818, respectively. The ESFLR model adequately addressed the spatial autocorrelation of residuals by reducing the Moran’s I value of the residuals to 0.0270. In conclusion, the ESFLR model is an effective and flexible method for landslide analysis.

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Tew, You Hoo, and Enylina Nordin. "Predicting corporate financial distress using logistic regression : Malaysian evidence." Social and Management Research Journal 3, no. 1 (June 1, 2006): 123. http://dx.doi.org/10.24191/smrj.v3i1.5108.

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This study attempts to construct and test financial distress prediction model for Malaysian Companies. The samplefor this study consists of84 companies listed on Bursa Malaysia that became financially distressed in 200/ and 2002 and a matched (by industry and firm size) sample 0/ 84 financially healthy companies. The model is constructed by employing logistic regression analysis based on pooled data of5 years prior tofinancial distress. The model isfirst derived using the estimation sample andthen tested using the validation sample. Adding to the existing research onfinancial distress prediction models, the current model utilizes measures ofshareholders' equity to total liabilities, shareholders' equity to total assets, current liabilities to total assets, total borrowings to total assets andinventory turnover. The results are encouraging, as the model developed/or predicting corporatefinancial distress in Malaysia is reliable up to 5 years prior to financial distress. II is also believed thai the prediction model can be useful to different groups of users such as policy makers, financial institutions, creditors, managers, bankers, investors and shareholders.

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Eddama, MMR, KC Fragkos, S. Renshaw, M. Aldridge, G. Bough, L. Bonthala, A. Wang, and R. Cohen. "Logistic regression model to predict acute uncomplicated and complicated appendicitis." Annals of The Royal College of Surgeons of England 101, no. 2 (February 2019): 107–18. http://dx.doi.org/10.1308/rcsann.2018.0152.

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Introduction While patients with acute uncomplicated appendicitis may be treated conservatively, those who suffer from complicated appendicitis require surgery. We describe a logistic regression equation to calculate the likelihood of acute uncomplicated appendicitis and complicated appendicitis in patients presenting to the emergency department with suspected acute appendicitis. Materials and methods A cohort of 895 patients who underwent appendicectomy were analysed retrospectively. Depending on the final histology, patients were divided into three groups; normal appendix, acute uncomplicated appendicitis and complicated appendicitis. Normal appendix was considered the reference category, while acute uncomplicated appendicitis and complicated appendicitis were the nominal categories. Multivariate and univariate regression models were undertaken to detect independent variables with significant odds ratio that can predict acute uncomplicated appendicitis and complicated appendicitis. Subsequently, a logistic regression equation was generated to produce the likelihood acute uncomplicated appendicitis and complicated appendicitis. Results Pathological diagnosis of normal appendix, acute uncomplicated appendicitis and complicated appendicitis was identified in 188 (21%), 525 (59%) and 182 patients (20%), respectively. The odds ratio from a univariate analysis to predict complicated appendicitis for age, female gender, log2 white cell count, log2 C-reactive protein and log2 bilirubin were 1.02 (95% confidence interval, CI, 1.01, 1.04), 2.37 (95% CI 1.51, 3.70), 9.74 (95% CI 5.41, 17.5), 1.57 (95% CI 1.40, 1.74), 2.08 (95% CI 1.56, 2.76), respectively. For the same variable, similar odds ratios were demonstrated in a multivariate analysis to predict complicated appendicitis and univariate and multivariate analysis to predict acute uncomplicated appendicitis. Conclusions The likelihood of acute uncomplicated appendicitis and complicated appendicitis can be calculated by using the reported predictive equations integrated into a web application at www.appendistat.com. This will enable clinicians to determine the probability of appendicitis and the need for urgent surgery in case of complicated appendicitis.

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Yun, Hongwon. "Prediction model of algal blooms using logistic regression and confusion matrix." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 3 (June 1, 2021): 2407. http://dx.doi.org/10.11591/ijece.v11i3.pp2407-2413.

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Algal blooms data are collected and refined as experimental data for algal blooms prediction. Refined algal blooms dataset is analyzed by logistic regression analysis, and statistical tests and regularization are performed to find the marine environmental factors affecting algal blooms. The predicted value of algal bloom is obtained through logistic regression analysis using marine environment factors affecting algal blooms. The actual values and the predicted values of algal blooms dataset are applied to the confusion matrix. By improving the decision boundary of the existing logistic regression, and accuracy, sensitivity and precision for algal blooms prediction are improved. In this paper, the algal blooms prediction model is established by the ensemble method using logistic regression and confusion matrix. Algal blooms prediction is improved, and this is verified through big data analysis.

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ROBINS, JAMES M., and DON BLEVINS. "ANALYSIS OF PROPORTIONATE MORTALITY DATA USING LOGISTIC REGRESSION MODELS." American Journal of Epidemiology 125, no. 3 (March 1987): 524–35. http://dx.doi.org/10.1093/oxfordjournals.aje.a114559.

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Wibowo, Ananto, and M. Rismawan Ridha. "Comparison of Logistic Regression Model and MARS Using Multicollinearity Data Simulation." JTAM | Jurnal Teori dan Aplikasi Matematika 4, no. 1 (April 24, 2020): 39. http://dx.doi.org/10.31764/jtam.v4i1.1801.

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There are several statistical methods used to model the effect of predictor variables on categorical response variables, namely logistic regression and Multivariate Adaptive Regression Splines (MARS). However, neither MARS nor logistic regression allows multicollinearity on any predictor variables. This study applies the use of both methods to the simulation data with principal component analysis as an improvement in multicollinearity to find out which regression has better performance. The result of the analysis shows that MARS is very powerful in modeling research simulation data. Besides, based on the criteria of the number of significant major components, accuracy, sensitivity, and specificity values, MARS has more appropriate performance than logistic regression.

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Journal articles on the topic 'Logistic regression analysis model'

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