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Journal articles on the topic 'Binary Logistic Regression Model'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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1

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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2

Dombi, József, and Tamás Jónás. "Kappa Regression: An Alternative to Logistic Regression." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 28, no. 02 (April 2020): 237–67. http://dx.doi.org/10.1142/s0218488520500105.

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In this study, a new regression method called Kappa regression is introduced to model conditional probabilities. The regression function is based on Dombi’s Kappa function, which is well known in fuzzy theory. Here, we discuss how the Kappa function relates to the Logistic function as well as how it can be used to approximate the Logistic function. We introduce the so-called Generalized Kappa Differential Equation and show that both the Kappa and the Logistic functions can be derived from it. Kappa regression, like binary Logistic regression, models the conditional probability of the event that a dichotomous random variable takes a particular value at a given value of an explanatory variable. This new regression method may be viewed as an alternative to binary Logistic regression, but while in binary Logistic regression the explanatory variable is defined over the entire Euclidean space, in the Kappa regression model the predictor variable is defined over a bounded subset of the Euclidean space. We will also show that asymptotic Kappa regression is Logistic regression. The advantages of this novel method are demonstrated by means of an example, and afterwards some implications are discussed.
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Kuha, Jouni, and Colin Mills. "On Group Comparisons With Logistic Regression Models." Sociological Methods & Research 49, no. 2 (January 7, 2018): 498–525. http://dx.doi.org/10.1177/0049124117747306.

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It is widely believed that regression models for binary responses are problematic if we want to compare estimated coefficients from models for different groups or with different explanatory variables. This concern has two forms. The first arises if the binary model is treated as an estimate of a model for an unobserved continuous response and the second when models are compared between groups that have different distributions of other causes of the binary response. We argue that these concerns are usually misplaced. The first of them is only relevant if the unobserved continuous response is really the subject of substantive interest. If it is, the problem should be addressed through better measurement of this response. The second concern refers to a situation which is unavoidable but unproblematic, in that causal effects and descriptive associations are inherently group dependent and can be compared as long as they are correctly estimated.
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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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Shrestha, Noora. "Assessing Discriminatory Performance of a Binary Logistic Regression Model." International Journal of Advances in Scientific Research and Engineering 5, no. 7 (2019): 194–98. http://dx.doi.org/10.31695/ijasre.2019.33448.

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6

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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Dukalang, Hendra H. "PERBANDINGAN REGRESI LOGISTIK BINER DAN PROBIT BINER DALAM PEMODELAN TINGKAT PARTISIPASI ANGKATAN KERJA." Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi 7, no. 2 (December 30, 2019): 62–70. http://dx.doi.org/10.34312/euler.v7i2.10355.

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Regression is a data analysis method used to model the relationship between one response variable and one or more predictor variables. In regression modelling, data is often used. In general, the regression model that is often used is simple or multiple regression in modelling where the response variable is quantitative data. The fundamental difference from regression models using quantitative data is the main objective is to estimate the average value of the dependent variable using certain values of the independent variable. Whereas in a regression model with a qualitative dependent variable the main objective is to find the probability of something happening (probability model). One of the development methods of the regression model for data with qualitative response variables is Logistic and Probit regression. The purpose of this study was to compare the best model using binary logistic regression with binary probit regression in the case of Labor Force Participation Rate (TPAK) in Gorontalo City. The research method used is quantitative research methods, with binary logistic regression modelling and binary probit regression. The results showed that the variable that has a significant effect on TPAK Gorontalo City is the open unemployment rate, and the best model between the binary logistic regression model with an AIC value of 1.289 is smaller than the AIC value of the binary Probit regression 1.318, likewise from the R2 value the R2 value for regression is obtained. binary logistic of 12.74%, greater than the R2 value of binary probit regression of 10.70%.
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Kubkowski, Mariusz, and Jan Mielniczuk. "Projections of a general binary model on a logistic regression." Linear Algebra and its Applications 536 (January 2018): 152–73. http://dx.doi.org/10.1016/j.laa.2017.09.013.

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9

McCormick, Tyler H., Adrian E. Raftery, David Madigan, and Randall S. Burd. "Dynamic Logistic Regression and Dynamic Model Averaging for Binary Classification." Biometrics 68, no. 1 (August 12, 2011): 23–30. http://dx.doi.org/10.1111/j.1541-0420.2011.01645.x.

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10

Sarkar, S. K., and Habshah Midi. "Importance of Assessing the Model Adequacy of Binary Logistic Regression." Journal of Applied Sciences 10, no. 6 (March 1, 2010): 479–86. http://dx.doi.org/10.3923/jas.2010.479.486.

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11

Zakaria, Nurul Anasuhah, Tahir Ahmad, Siti Rahmah Awang, and Ajmain Safar. "Determination of Huffaz Academic Achievement Using Binary Logistic Regression Model." Journal of Physics: Conference Series 1988, no. 1 (July 1, 2021): 012104. http://dx.doi.org/10.1088/1742-6596/1988/1/012104.

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12

Bulso, Nicola, Matteo Marsili, and Yasser Roudi. "On the Complexity of Logistic Regression Models." Neural Computation 31, no. 8 (August 2019): 1592–623. http://dx.doi.org/10.1162/neco_a_01207.

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We investigate the complexity of logistic regression models, which is defined by counting the number of indistinguishable distributions that the model can represent (Balasubramanian, 1997 ). We find that the complexity of logistic models with binary inputs depends not only on the number of parameters but also on the distribution of inputs in a nontrivial way that standard treatments of complexity do not address. In particular, we observe that correlations among inputs induce effective dependencies among parameters, thus constraining the model and, consequently, reducing its complexity. We derive simple relations for the upper and lower bounds of the complexity. Furthermore, we show analytically that defining the model parameters on a finite support rather than the entire axis decreases the complexity in a manner that critically depends on the size of the domain. Based on our findings, we propose a novel model selection criterion that takes into account the entropy of the input distribution. We test our proposal on the problem of selecting the input variables of a logistic regression model in a Bayesian model selection framework. In our numerical tests, we find that while the reconstruction errors of standard model selection approaches (AIC, BIC, [Formula: see text] regularization) strongly depend on the sparsity of the ground truth, the reconstruction error of our method is always close to the minimum in all conditions of sparsity, data size, and strength of input correlations. Finally, we observe that when considering categorical instead of binary inputs, in a simple and mathematically tractable case, the contribution of the alphabet size to the complexity is very small compared to that of parameter space dimension. We further explore the issue by analyzing the data set of the “13 keys to the White House,” a method for forecasting the outcomes of US presidential elections.
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13

LI, R., J. ZHOU, and L. WANG. "ESTIMATION OF THE BINARY LOGISTIC REGRESSION MODEL PARAMETER USING BOOTSTRAP RE-SAMPLING." Latin American Applied Research - An international journal 48, no. 3 (July 31, 2018): 199–204. http://dx.doi.org/10.52292/j.laar.2018.228.

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In this paper, the non-parametric bootstrap and non-parametric Bayesian bootstrap methods are applied for parameter estimation in the binary logistic regression model. A real data study and a simulation study are conducted to compare the Nonparametric bootstrap, Non-parametric Bayesian bootstrap and the maximum likelihood methods. Study results shows that three methods are all effective ways for parameter estimation in the binary logistic regression model. In small sample case, the non-parametric Bayesian bootstrap method performs relatively better than the non-parametric bootstrap and the maximum likelihood method for parameter estimation in the binary logistic regression model.
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14

CONNOLLY, MARGARET A., and KUNG-YEE LIANG. "Conditional logistic regression models for correlated binary data." Biometrika 75, no. 3 (1988): 501–6. http://dx.doi.org/10.1093/biomet/75.3.501.

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15

Park, Cheolyong, and Hyun Seok Choi. "An educational tool for binary logistic regression model using Excel VBA." Journal of the Korean Data and Information Science Society 25, no. 2 (March 31, 2014): 403–10. http://dx.doi.org/10.7465/jkdi.2014.25.2.403.

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16

Özkale, M. Revan, and Engin Arıcan. "First-order r−d class estimator in binary logistic regression model." Statistics & Probability Letters 106 (November 2015): 19–29. http://dx.doi.org/10.1016/j.spl.2015.06.021.

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17

LIN, Wei. "A binary logistic regression model for discriminating real protein-protein interface." Progress in Natural Science 13, no. 6 (2003): 412. http://dx.doi.org/10.1360/03jz9073.

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Lin, Wei, Ping Sun, and Xiangjun Liu. "A binary logistic regression model for discriminating real protein-protein interface*." Progress in Natural Science 13, no. 6 (June 1, 2003): 412–18. http://dx.doi.org/10.1080/10020070312331343770.

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19

Jalava, Katri, Sirpa Räsänen, Kaija Ala-Kojola, Saara Nironen, Jyrki Möttönen, and Jukka Ollgren. "Binary Regression Models with Log-Link in the Cohort Studies." Open Epidemiology Journal 6, no. 1 (October 4, 2013): 18–20. http://dx.doi.org/10.2174/1874297101306010018.

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Regression models have been used to control confounding in food borne cohort studies, logistic regression has been commonly used due to easy converge. However, logistic regression provide estimates for OR only when RR estimate is lower than 10%, an unlikely situation in food borne outbreaks. Recent developments have resolved the binary model convergence problems applying log link. Food items significant in the univariable analysis were included for the multivariable analysis of two recent Finnish norovirus outbreaks. We used both log and logistic regression models in R and Bayesian model in Winbugs by SPSS and R. The log-link model could be used to identify the vehicle in the two norovirus outbreak datasets. Convergence problems were solved using Bayesian modelling. Binary model applying log link provided accurate and useful estimates of RR estimating the true risk, a suitable method of choice for multivariable analysis of outbreak cohort studies.
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20

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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21

Pang, Guqian, Jian He, Yuming Huang, and Liuhong Zhang. "A Binary Logistic Regression Model for Severe Convective Weather with Numerical Model Data." Advances in Meteorology 2019 (November 14, 2019): 1–15. http://dx.doi.org/10.1155/2019/6127281.

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Based on meteorological observations and products of a GRAPES and an ECMWF model from March to April 2014, some indexes and parameters with good relevancy were selected as predictors. Through analyzing the spatial distributions and the binary logistic regressions of the indexes, estimated values of the predictors and severe convective weather diagnostic prediction equations were established to get a severe weather predictor P for forecasting severe convective weather for the next 12 hours in Guangdong province. The equations were tested and analyzed, respectively, with the two models as well as the radiosonde data. The results indicated that the severe weather forecasts’ CSI by the predictor P was obviously higher than by any single index. The TT error between the models and the soundings was small, while the K index of the models was more discrete than the soundings. The index MDPIs were 1 greater than the soundings, but their trends of change were consistent with the soundings.
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22

Minder, C. E., and G. Gillmann. "On Graphically Checking Goodness-of-fit of Binary Logistic Regression Models." Methods of Information in Medicine 48, no. 03 (2009): 306–10. http://dx.doi.org/10.3414/me0571.

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Summary Objectives: This paper is concerned with checking goodness-of-fit of binary logistic regression models. For the practitioners of data analysis, the broad classes of procedures for checking goodness-of-fit available in the literature are described. The challenges of model checking in the context of binary logistic regression are reviewed. As a viable solution, a simple graphical procedure for checking goodness-of-fit is proposed. Methods: The graphical procedure proposed relies on pieces of information available from any logistic analysis; the focus is on combining and presenting these in an informative way. Results: The information gained using this approach is presented with three examples. In the discussion, the proposed method is put into context and compared with other graphical procedures for checking goodness-of-fit of binary logistic models available in the literature. Conclusion: A simple graphical method can significantly improve the understanding of any logistic regression analysis and help to prevent faulty conclusions.
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Rainey, Carlisle. "Dealing with Separation in Logistic Regression Models." Political Analysis 24, no. 3 (2016): 339–55. http://dx.doi.org/10.1093/pan/mpw014.

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When facing small numbers of observations or rare events, political scientists often encounter separation, in which explanatory variables perfectly predict binary events or nonevents. In this situation, maximum likelihood provides implausible estimates and the researcher might want incorporate some form of prior information into the model. The most sophisticated research uses Jeffreys’ invariant prior to stabilize the estimates. While Jeffreys’ prior has the advantage of being automatic, I show that it often provides too much prior information, producing smaller point estimates and narrower confidence intervals than even highly skeptical priors. To help researchers assess the amount of information injected by the prior distribution, I introduce the concept of a partial prior distribution and develop the tools required to compute the partial prior distribution of quantities of interest, estimate the subsequent model, and summarize the results.
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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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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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Díaz-Pérez, Manuel, Ángel Carreño-Ortega, José-Antonio Salinas-Andújar, and Ángel-Jesús Callejón-Ferre. "Application of Logistic Regression Models for the Marketability of Cucumber Cultivars." Agronomy 9, no. 1 (January 3, 2019): 17. http://dx.doi.org/10.3390/agronomy9010017.

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The aim of this study is to establish a binary logistic regression method to evaluate and select cucumber cultivars (Cucumis sativus L.) with a longer postharvest shelf life. Each sample was evaluated for commercial quality (fruit aging, weight loss, wilting, yellowing, chilling injury, and rotting) every 7 days of storage. Simple and multiple binary logistic regression models were applied in which the dependent variable was the probability of marketability and the independent variables were the days of storage, cultivars, fruit weight loss, and months of evaluation. The results showed that cucumber cultivars with a longer shelf life can be selected by a simple and multiple binary logistic regression analysis. Storage time was the main determinant of fruit marketability. Fruit weight loss strongly influenced the probability of marketability. The logistic model allowed us to determine the cucumber weight loss percentage over which a fruit would be rejected in the market.
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Et. al., Mahdi Wahhab Neamah,. "Utilizing the Logistic Regression Model in Analyzing the Categorical Data of Economic Effects." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 4 (April 10, 2021): 638–46. http://dx.doi.org/10.17762/turcomat.v12i4.547.

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The categorical data has a significant role in representing statistical binary variables, and they are analyzed by means of grouping the response variable into ordered categories. Thereby, the dependent variable becomes of type binary qualitative variable. The data related to the financial position of world countries is classified within the categorical data. This work is to study the economic effects of an individual's different factors on determining the richness or poorness levels of a selected population of countries. Moreover, a logistic regression model is to be created to estimate these levels. As a sample of research, the categorical data relevant to the financial status of 20 Arabic countries were drawn from the website of the World Bank, WB. In addition, for comparison purpose, another similar set of categorical data was generated by MATLAB too. The paper has been based on two hypotheses, first is the well-known regression models, like the ordinary least squares or maximum likelihood, are not accurate in case of binary qualitative variables. Second, is utilizing the logistic regression model as an alternative model to achieve the paper goal. The paper results, for both WB data and MATLAB data, have successfully proved the ability of the logistic regression model in manipulating the categorical data and predicting the coefficients of the corresponding regression models.
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Fitzmaurice, Garrett M., Anthony F. Heath, and Peter Clifford. "Logistic Regression Models for Binary Panel Data with Attrition." Journal of the Royal Statistical Society. Series A (Statistics in Society) 159, no. 2 (1996): 249. http://dx.doi.org/10.2307/2983172.

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Li, Xiaohu, Linxiong Li, and Rui Fang. "Copula-based Logistic Regression Models for Bivariate Binary Responses." Journal of Data Science 12, no. 3 (March 10, 2021): 461–76. http://dx.doi.org/10.6339/jds.201407_12(3).0005.

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Cengiz, Memet Ali, Yuksel Bek ., and Rezan Yilmaz . "Bayesian Inference of Binary Logistic Regression Model for Assessing Erythrocyte Sedimentation Rate." Pakistan Journal of Biological Sciences 4, no. 9 (August 15, 2001): 1180–83. http://dx.doi.org/10.3923/pjbs.2001.1180.1183.

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Viteri López, Andrea Salomé, and Carlos Augusto Morales Rodriguez. "Flash Flood Forecasting in São Paulo Using a Binary Logistic Regression Model." Atmosphere 11, no. 5 (May 7, 2020): 473. http://dx.doi.org/10.3390/atmos11050473.

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This study presents a flash flood forecasting model that uses a binary logistic regression method to determine the occurrence of flash flood events in different watersheds in the city of São Paulo, Brazil. This study is based on two years (2015–2016) of rain estimates from a dual-polarization S-band Doppler weather radar (SPOL) and flood locations observed by the Climate Emergency Management Center (CGE) of São Paulo City Hall. The logistic regression model is based on daily accumulated precipitation, a maximum precipitation rate, and daily rainfall duration. The model presented a probability of detection (POD) of 46% (71%) on average for flood events (conditional), while, for events without flash flood, it reached 98% probability. Despite the low averaged POD for flash flood occurrence, the model demonstrated a good performance for watersheds located in the east of the city near the Tietê River and in the southeast with probabilities above 50%.
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Escribano Mesa, José Alberto, Enrique Alonso Morillejo, Tesifón Parrón Carreño, Antonio Huete Allut, José María Narro Donate, Paddy Méndez Román, Ascensión Contreras Jiménez, Francisco Pedrero García, and José Masegosa González. "Risk of Recurrence in Operated Parasagittal Meningiomas: A Logistic Binary Regression Model." World Neurosurgery 110 (February 2018): e112-e118. http://dx.doi.org/10.1016/j.wneu.2017.10.087.

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Shrestha, Noora. "Application of Binary Logistic Regression Model to Assess the Likelihood of Overweight." American Journal of Theoretical and Applied Statistics 8, no. 1 (2019): 18. http://dx.doi.org/10.11648/j.ajtas.20190801.13.

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邹, 媛. "Stochastic Restricted Two-Parameter Maximum Likelihood Estimator in Binary Logistic Regression Model." Statistics and Application 09, no. 04 (2020): 515–24. http://dx.doi.org/10.12677/sa.2020.94055.

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Zhang, Haijun, Xiaoyong Han, and Sha Dai. "Fire Occurrence Probability Mapping of Northeast China With Binary Logistic Regression Model." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 6, no. 1 (February 2013): 121–27. http://dx.doi.org/10.1109/jstars.2012.2236680.

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邹, 媛. "A First-Order Approximated Jackknifed Liu Estimator in Binary Logistic Regression Model." Advances in Applied Mathematics 10, no. 03 (2021): 790–800. http://dx.doi.org/10.12677/aam.2021.103087.

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Yadi, Wang, and Bei Yuxin. "A Binary Logistic Regression Model to Unprosecuted Cases of Assisting a Cybercrime." Journal of Physics: Conference Series 1994, no. 1 (August 1, 2021): 012027. http://dx.doi.org/10.1088/1742-6596/1994/1/012027.

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HR, Titi Kurnianti, Muhammad Nadjib Bustan, and R. Ruliana. "Pemodelan Faktor-Faktor yang Mempengaruhi Jenis Kanker Payudara Menggunakan Regresi Logistik Biner (Kasus : Pasien Penderita Kanker Payudara di RSUP Dr. Wahidin Sudirohusodo tahun 2016)." VARIANSI: Journal of Statistics and Its application on Teaching and Research 1, no. 3 (December 14, 2019): 40. http://dx.doi.org/10.35580/variansiunm12898.

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Abstrak Regresi logistik adalah suatu metode analisis statistik yang diterapkan untuk memodelkan variabel dependen yang memiliki dua kategori atau lebih dengan satu atau lebih variabel independen. Regresi Logistik biner merupakan suatu analisis statistika yang digunakan untuk menganalisis hubungan antara satu atau lebih peubah bebas dengan peubah respon yang bersifat biner atau dichotomous. Peubah bebas pada regresi logistik dapat berupa peubah skala kategorik maupun peubah yang skala kontinu sedangkan peubah respon berupa peubah berskala kategorik. Regresi Logistik Biner dapat diterapkan pada kasus kesehatan, khususnya pada penelitian ini yaitu mengenai kanker payudara. Sesuai uraian diatas maka penulis bermaksud untuk mengkaji dan melakukan penelitian tentang Pemodelan Faktor-Faktor yang Mempengaruhi Jenis Kanker Payudara Menggunakan Regresi Logistik Biner (Kasus : Pasien Penderita Kanker Payudara di Rumah Sakit Umum Pusat Dr. Wahidin Sudirohusodo). Dari hasil analisis didapatkan bahwa peubah penjelas yang berpengaruh nyata terhadap jenis keganasan kanker terhadap pasien penderita kanker payudara adalah peubah Kemoterapi (X2) dan peubah Metastase (X5) yang masing-masing memiliki nilai odds rasio sebesar 0,17 dan 6,16. Kata kunci : Kanker Payudara, Regresi Logistik, Regresi Logistik Biner. Abstract Logistic regression is a method of statistical analysis that is applied to model the dependent variable which has two or more categories with one or more independent variables. Binary Logistic Regression is a statistical analysis that is used to analyze the relationship between one or more independent variables with variable binary or dichotomous responses. The free variables in logistic regression can be either categorical scale or continuous scale variables while the response variables are categorical scale variables. Binary Logistic Regression can be applied to health cases, especially in this study, namely breast cancer. In accordance with the description above, the author intends to study and conduct research on Modeling Factors Affecting Types of Breast Cancer Using Binary Logistic Regression (Case: Patients with Breast Cancer Patients at Dr. Wahidin Sudirohusodo Central General Hospital). From the results of the analysis it was found that the explanatory variables that significantly affected the type of cancer malignancy in patients with breast cancer were Chemotherapy variables (X2) and Metastase variables (X5), each of which had odds ratio values of 0.17 and 6.16. Keywords: Breast Cancer, Logistic Regression, Binary Logistic Regression.
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Popovic-Stijacic, Milica, Ljiljana Mihic, and Dusica Filipovic-Djurdjevic. "Analyzing data from memory tasks - comparison of ANOVA, logistic regression and mixed logit model." Psihologija 51, no. 4 (2018): 469–88. http://dx.doi.org/10.2298/psi170615023p.

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We compared three statistical analyses over binary outcomes. As applying ANOVA over proportions violates at least two classical assumptions of linear models, two alternatives are described: the binary logistic regression and the mixed logit model. Firstly, we compared the effects obtained by the three methods over the same data from a previous memory research. All three methods gave similar results: the effects of the tasks and the number of sensory modalities were observed, but not their interaction. Secondly, by using the bootstrap estimates of the parameters, the efficacy of each method was explored. As predicted, the bootstrap parameter estimates of the ANOVA had large bias and standard errors, and consequently wide confidence intervals. On the other hand, the bootstrap parameter estimates of the binary logistic regression and the mixed logit models were similar ? both had low bias and standard errors and narrow confidence intervals.
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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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Aminudin, Aminudin, and Eko Budi Cahyono. "KORELASI TIME TO LIVE TERHADAP QUERY TIDAK NORMAL PADA DNS MENGGUNAKAN BINARY LOGISTIC REGRESSION." JURTEKSI (Jurnal Teknologi dan Sistem Informasi) 7, no. 2 (April 1, 2021): 143–50. http://dx.doi.org/10.33330/jurteksi.v7i2.924.

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Abstract: DNS plays a vital role in the operation of services on the internet. Almost all services on the internet are under DNS control, such as email, FTP, web apps, etc. So, it is not surprising that various malicious activities involve DNS services such as financial fraud, phishing, malware, and malicious activity, etc. Fortunately, in DNS there is a record with the name time to live which can be used to detect a query or the address accessed from the user is a normal query or an abnormal query. Therefore, the purpose of this study is to determine the correlation value between time to live and abnormal queries on passive DNS data using the Binary Logistic Regression model. The results showed that the Binary Logistic Regression method could model the correlation between TTL, elapsed, and bytes which have an optimal model F1 Score of 0.9997 and also have a condition close to the ideal state by using the Precision-Recall Curve (PRC) graph plot. Keywords: Binary Logistic Regression; DNS Passive; Precision-Recall Curve (PRC); Query Abnormal Abstrak: DNS memegang peranan yang vital di dalam berjalanya service di internet. Hampir seluruh layanan di internet berada di bawah kendali DNS seperti email, ftp, app web dll. Jadi, tidak mengherankan bahwa berbagai kegiatan jahat melibatkan layanan DNS seperti financial fraud, phising, malware dan aktivitas malicious dll. Untungnya, di dalam DNS tersimpan sebuah record dengan nama time to live yang dapat digunakan untuk mendeteksi sebuah query atau alamat yang diakses dari user tersebut bersifat query normal atau query tidak normal. Oleh karena itu, tujuan penelitian ini adalah untuk mengetahui nilai korelasi antara time to live dengan query tidak normal pada data passive DNS dengan menggunakan model Binary Logistic Regression. Hasil penelitian menunjukkan bahwa metode Binary Logistic Regression dapat memodelkan korelasi antara TTL, elapsed dan bytes yang memiliki model optimal F1 Score sebesar 0.9997 dan juga memiliki kondisi hampir mendekati keadaan ideal dengan menggunakan plot grafik Precision Recall Curve (PRC). Kata kunci: Binary Logistic Regression; DNS Passive; Precision-Recall Curve (PRC); Query Abnormal
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Prestigiacomo, Charles J., Wenzhuan He, Jeffrey Catrambone, Stephanie Chung, Lydia Kasper, Latha Pasupuleti, and Neelesh Mittal. "Predicting aneurysm rupture probabilities through the application of a computed tomography angiography–derived binary logistic regression model." Journal of Neurosurgery 110, no. 1 (January 2009): 1–6. http://dx.doi.org/10.3171/2008.5.17558.

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Object The goal of this study was to establish a biomathematical model to accurately predict the probability of aneurysm rupture. Biomathematical models incorporate various physical and dynamic phenomena that provide insight into why certain aneurysms grow or rupture. Prior studies have demonstrated that regression models may determine which parameters of an aneurysm contribute to rupture. In this study, the authors derived a modified binary logistic regression model and then validated it in a distinct cohort of patients to assess the model's stability. Methods Patients were examined with CT angiography. Three-dimensional reconstructions were generated and aneurysm height, width, and neck size were obtained in 2 orthogonal planes. Forward stepwise binary logistic regression was performed and then applied to a prospective cohort of 49 aneurysms in 37 patients (not included in the original derivation of the equation) to determine the log-odds of rupture for this aneurysm. Results A total of 279 aneurysms (156 ruptured and 123 unruptured) were observed in 217 patients. Four of 6 linear dimensions and the aspect ratio were significantly larger (each with p < 0.01) in ruptured aneurysms than unruptured aneurysms. Calculated volume and aneurysm location were correlated with rupture risk. Binary logistic regression applied to an independent prospective cohort demonstrated the model's stability, showing 83% sensitivity and 80% accuracy. Conclusions This binary logistic regression model of aneurysm rupture identified the status of an aneurysm with good accuracy. The use of this technique and its validation suggests that biomorphometric data and their relationships may be valuable in determining the status of an aneurysm.
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Su, Bo, Qingyue Yang, Jinlong Yang, and Manjun Zhang. "Encryption algorithm for network communication information based on binary logistic regression." Journal of Intelligent & Fuzzy Systems 39, no. 2 (August 31, 2020): 1627–37. http://dx.doi.org/10.3233/jifs-179936.

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In order to overcome the problems of long encrypting time, low information availability, low information integrity and low encrypting efficiency when using the current method to encrypt the communication information in the network without constructing the sequence of communication information. This paper proposes a network communication information encryption algorithm based on binary logistic regression, analyses the development of computer architecture, builds a network communication model, layers the main body of information exchange, and realizes the information synchronization of device objects at all levels. Based on the binary Logistic regression model, network communication information sequence is generated, and the fusion tree is constructed by network communication information sequence. The network communication information is encrypted through system initialization stage, data preparation stage, data fusion stage and data validation stage. The experimental results show that the information availability of the proposed algorithm is high, and the maximum usability can reach 97.7%. The encryption efficiency is high, and the shortest encryption time is only 1.9 s, which fully shows that the proposed algorithm has high encryption performance.
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Dubrov, V. I., V. V. Sizonov, I. M. Kagantsov, K. N. Negmatova, and S. G. Bondarenko. "Predicting the outcomes of a single endoscopic correction of vesicoureteral reflux using a dextranomer/hyaluronic acid copolymer: selection of the optimal predictive model." Vestnik Urologii 9, no. 2 (July 10, 2021): 45–55. http://dx.doi.org/10.21886/2308-6424-2021-9-2-45-55.

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Introduction. Endoscopic dextranomer/hyaluronic acid copolymer (DxHA) injection is the most commonly used minimally invasive method of surgical treatment of vesicoureteral reflux (VUR) in children.Purpose of the study. To estimate the accuracy of logistic prognostic models and artificial neural network for prediction a single endoscopic injection DxHA in VUR.Materials and methods. We used endoscopic DxHA in 582 patients (783 ureteric units) of all grades reflux (I - 20, II - 133, III - 443, IV - 187), 53 ureters had complete duplication. A total effectiveness of surgery was 53.2%. A binary logistic regression model and an artificial neural network (multilayer perceptron) were created, taking the following as independent variables: grade of reflux, the patient's age and sex, the ureteral duplication and ureteral dilatation index.Results. The univariate logistic regression showed that the selected predictors were strongly related to the outcome of the treatment. Binary logistic regression and neural network developed high accuracy of the predictions, area under ROC-curve was 0,7 for logistic regression model (a sensitivity of 70.7%, and a specificity of 66.3%) and 0.74 for artificial neural network (a sensitivity of 85.5%, a specificity of 65.3%). Synaptic neural network weights and logistic regression parameters were used in a scoring model to predict the outcome of a single endoscopic injection of DxHA in 2 independent hospitals. An outcomes analysis using predictive models in independent clinics showed a good quality of prediction both with the use of logistic regression (75% and 90% of the correct prognosis) and using a neural network (89.7% and 77% of the correct prediction).Conclusion. An artificial neural network and a binary logistic regression model are an effective tool to assist urologists in identifying and applying endoscopic treatments for VUR in children.
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Sakaya Barasa, Kennedy. "Incorporating Survey Weights into Binary and Multinomial Logistic Regression Models." Science Journal of Applied Mathematics and Statistics 3, no. 6 (2015): 243. http://dx.doi.org/10.11648/j.sjams.20150306.13.

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Stanghellini, Elena, and Marco Doretti. "On marginal and conditional parameters in logistic regression models." Biometrika 106, no. 3 (May 13, 2019): 732–39. http://dx.doi.org/10.1093/biomet/asz019.

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Summary We derive the exact formula linking the parameters of marginal and conditional logistic regression models with binary mediators when no conditional independence assumptions can be made. The formula has the appealing property of being the sum of terms that vanish whenever parameters of the conditional models vanish, thereby recovering well-known results as particular cases. It also permits the disentangling of direct and indirect effects as well as quantifying the distortion induced by the omission of relevant covariates, opening the way to sensitivity analysis. As the parameters of the conditional models are multiplied by terms that are always bounded, the derivations may also be used to construct reasonable bounds on the parameters of interest when relevant intermediate variables are unobserved. We assume that, conditionally on a set of covariates, the data-generating process can be represented by a directed acyclic graph. We also show how the results presented here lead to the extension of path analysis to a system of binary random variables.
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Pesantez-Narvaez, Jessica, Montserrat Guillen, and Manuela Alcañiz. "Predicting Motor Insurance Claims Using Telematics Data—XGBoost versus Logistic Regression." Risks 7, no. 2 (June 20, 2019): 70. http://dx.doi.org/10.3390/risks7020070.

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XGBoost is recognized as an algorithm with exceptional predictive capacity. Models for a binary response indicating the existence of accident claims versus no claims can be used to identify the determinants of traffic accidents. This study compared the relative performances of logistic regression and XGBoost approaches for predicting the existence of accident claims using telematics data. The dataset contained information from an insurance company about the individuals’ driving patterns—including total annual distance driven and percentage of total distance driven in urban areas. Our findings showed that logistic regression is a suitable model given its interpretability and good predictive capacity. XGBoost requires numerous model-tuning procedures to match the predictive performance of the logistic regression model and greater effort as regards to interpretation.
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ZOU Lili, 邹丽丽, 陈晓翔 CHEN Xiaoxiang, 何莹 HE Ying, 黎夏 LI Xia, and 何执兼 HE Zhijian. "Assessment of ardeidae waterfowl habitat suitability based on a binary logistic regression model." Acta Ecologica Sinica 32, no. 12 (2012): 3722–28. http://dx.doi.org/10.5846/stxb201109151350.

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Abdul Hamid, Hamzah, Bee Wah Yap, and Jin Xie Xian-. "COVARIATES AND SAMPLE SIZE EFFECTS ON PARAMETER ESTIMATION FOR BINARY LOGISTIC REGRESSION MODEL." Malaysian Journal of Science 35, no. 1 (April 30, 2016): 44–62. http://dx.doi.org/10.22452/mjs.vol35no1.7.

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Haines, Linda M., Gaëtan M. Kabera, and Principal Ndlovu. "D-optimal designs for the two-variable binary logistic regression model with interaction." Journal of Statistical Planning and Inference 193 (February 2018): 136–50. http://dx.doi.org/10.1016/j.jspi.2017.08.007.

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