Journal articles: 'Multinomial Logistic Model'

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El-Habil, Abdalla M. "An Application on Multinomial Logistic Regression Model." Pakistan Journal of Statistics and Operation Research 8, no. 2 (March 28, 2012): 271. http://dx.doi.org/10.18187/pjsor.v8i2.234.

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Hedeker, Donald. "A mixed-effects multinomial logistic regression model." Statistics in Medicine 22, no. 9 (2003): 1433–46. http://dx.doi.org/10.1002/sim.1522.

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Hung, Pham Ngoc, Pham Van Chung, and Le Thi Thanh An. "Multilevel multinomial logit model to study individual migration decision in Viet Nam." Science & Technology Development Journal - Economics - Law and Management 3, no. 1 (May 27, 2019): 45–51. http://dx.doi.org/10.32508/stdjelm.v3i1.539.

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In this paper we study the impact of relevant factors, such as individual characteristics, wages, living areas, on individual migration decisions. We have been using data from Labor Force Survey 2014 from Genaral Statistics Office of Vietnam (LFS 2014). We are going to evaluate how these above factors affect the status of "short-term migration" and "long-term migration" compared to "nonmigration". The well-known model in this field is the multinomial logistic model. However, the multinomial logistic model does not control the latent factors that have different effects on migration decision. This would result that the estimated coefficients of the variables would no longer be reliable (biased estimates due to lack of important variables). Hence, we have selected a multilevel multinomial logistic model. The levels we choose to control latent factors are province and region levels. As the results, the potential factors of different provinces and regions show different impacts on migration decisions. To sum up, a multilevel multinomial logistic model gives more reliable estimates, so it is more suitable for migration analysis compared to conventional multinomial logistic model.

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Hossain, Shakhawat, S. Ejaz Ahmed, and Hatem A. Howlader. "Model selection and parameter estimation of a multinomial logistic regression model." Journal of Statistical Computation and Simulation 84, no. 7 (November 26, 2012): 1412–26. http://dx.doi.org/10.1080/00949655.2012.746347.

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Salillari, Denisa, and Luela Prifti. "Comparison Study of Logistic Regression Model for Albanian Texts." JOURNAL OF ADVANCES IN MATHEMATICS 12, no. 9 (September 28, 2016): 6572–75. http://dx.doi.org/10.24297/jam.v12i9.127.

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Considering authorship attribution as a classification problem we attempt to estimate the probability to find the right author for each text under study. In this paper using R we first improve the simple model for six Albanian texts, (I) increasing number of texts and number of independent variables and then compare the results taken with them of the multinomial logistic regression (II). The model was applied on a set of one hundred texts of ten different authors. For all the authors under study the average correct predicted probability is 0.918. Analyzing data from different Albanian texts, results that about 40% of their letters consist of vowels. As conclusion comparing results taken with them of (II) multinomial logistic regression model for Albanian texts has more advantages than logistic regression model.

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Salillari, Denisa, and Luela Prifti. "A multinomial logistic regression model for text in Albanian language." JOURNAL OF ADVANCES IN MATHEMATICS 12, no. 7 (July 18, 2016): 6407–11. http://dx.doi.org/10.24297/jam.v12i7.5486.

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In this paper we present a multinomial logistic regression model for authorship identification in the Albanian language texts. In the model fitted the dependent variable is categorical which takes different values from 1 to 10 for each of the author and the independent variables are number of words, number of letters, number of vowels, number of consonants, number of punctuations and number of sentences for each text. The model was applied with success in the set of ten authors, each of them being represented by a set of one hundred texts they authored. As results first, second and the third authors have the higher correct predicted percentage and the highest overall correct predicted probability taken was 0.738. As conclusion adding in the model number of consonants, number of punctuations and number of sentences as independent variables the overall correct predicted percentage is increased.

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Konidina, Radhaiah. "Multinomial Logistic Regression Model for Predicting Flight Arrival & Delay." International Journal for Research in Applied Science and Engineering Technology 6, no. 3 (March 31, 2018): 1455–64. http://dx.doi.org/10.22214/ijraset.2018.3226.

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Zhang, Jingru, and Wei Lin. "Scalable estimation and regularization for the logistic normal multinomial model." Biometrics 75, no. 4 (April 29, 2019): 1098–108. http://dx.doi.org/10.1111/biom.13071.

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Wang, Yu, Xuan Bi, and Annie Qu. "A Logistic Factorization Model for Recommender Systems With Multinomial Responses." Journal of Computational and Graphical Statistics 29, no. 2 (October 25, 2019): 396–404. http://dx.doi.org/10.1080/10618600.2019.1665535.

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Li, Fuxiao, Zhanshou Chen, and Yanting Xiao. "Sequential change-point detection in a multinomial logistic regression model." Open Mathematics 18, no. 1 (July 29, 2020): 807–19. http://dx.doi.org/10.1515/math-2020-0037.

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Abstract Change-point detection in categorical time series has recently gained attention as statistical models incorporating change-points are common in practice, especially in the area of biomedicine. In this article, we propose a sequential change-point detection procedure based on the partial likelihood score process for the detection of changes in the coefficients of multinomial logistic regression model. The asymptotic results are presented under both the null of no change and the alternative of changes in coefficients. We carry out a Monte Carlo experiment to evaluate the empirical size of the proposed procedure as well as its average run length. We illustrate the method by using data on a DNA sequence. Monte Carlo experiments and real data analysis demonstrate the effectiveness of the proposed procedure.

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Gerber, Eric A. E., and Bruce A. Craig. "A mixed effects multinomial logistic-normal model for forecasting baseball performance." Journal of Quantitative Analysis in Sports 17, no. 3 (January 6, 2021): 221–39. http://dx.doi.org/10.1515/jqas-2020-0007.

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Abstract Prediction of player performance is a key component in the construction of baseball team rosters. As a result, most prediction models are the proprietary property of team or industrial sports entities, and little is known about them. Of those models that have been published, the main focus has been to separately model each outcome with nearly no emphasis on uncertainty quantification. This research introduces a joint modeling approach to predict seasonal plate appearance outcome vectors using a mixed-effects multinomial logistic-normal model. This model accounts for positive and negative correlations between outcomes, both across and within player seasons, and provides a joint posterior predictive outcome distribution from which uncertainty can be quantified. It is applied to the important, yet unaddressed, problem of predicting performance for players moving between the Japanese (NPB) and American (MLB) major leagues.

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Apriyani, Mella, Jajang Jajang, and Agustini Tripena Br Sb. "IMPLEMENTASI MODEL REGRESI LOGISTIK MULTINOMIAL PADA PENGELOMPOKAN PENYAKIT TUBERKULOSIS." Jurnal Ilmiah Matematika dan Pendidikan Matematika 13, no. 1 (July 28, 2021): 27. http://dx.doi.org/10.20884/1.jmp.2021.13.1.3612.

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There are three types of Tuberculosis (TB) patients at Banyumas Region Hospital, namely negative pulmonary TB, positive pulmonary TB, and extra pulmonary TB. Types of TB generally caused by age, cae of history, gender, level of education, and domicile. One of the methods that used to find a correlation between types of TB with the affect is regression analysis. This study used multinomial logistic regession analysis because types of TB is categorical and the data is 156 TB’s patients recorded at 2018/2019. The result showed that the level of education be a dominant factor to affect TB. Here, we noted that patients with basic education level have a 5,843 time odds for getting positive pulmonary TB and 2,224 times for getting extra pulmonary TB. The multinomial logistic regression model is then given as probability for getting positive pulmonary TB with factor level of education is greather than negative pulmonary TB and extra pulmonary TB.

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Madhu, B., N. C. Ashok, and S. Balasubramanian. "Multinomial Logistic Regression Predicted Probability Map To Visualize The Influence Of Socio-Economic Factors On Breast Cancer Occurrence in Southern Karnataka." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 27, 2014): 193–96. http://dx.doi.org/10.5194/isprsarchives-xl-8-193-2014.

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Multinomial logistic regression analysis was used to develop statistical model that can predict the probability of breast cancer in Southern Karnataka using the breast cancer occurrence data during 2007–2011. Independent socio-economic variables describing the breast cancer occurrence like age, education, occupation, parity, type of family, health insurance coverage, residential locality and socioeconomic status of each case was obtained. The models were developed as follows: i) Spatial visualization of the Urban- rural distribution of breast cancer cases that were obtained from the Bharat Hospital and Institute of Oncology. ii) Socio-economic risk factors describing the breast cancer occurrences were complied for each case. These data were then analysed using multinomial logistic regression analysis in a SPSS statistical software and relations between the occurrence of breast cancer across the socio-economic status and the influence of other socio-economic variables were evaluated and multinomial logistic regression models were constructed. iii) the model that best predicted the occurrence of breast cancer were identified. This multivariate logistic regression model has been entered into a geographic information system and maps showing the predicted probability of breast cancer occurrence in Southern Karnataka was created. This study demonstrates that Multinomial logistic regression is a valuable tool for developing models that predict the probability of breast cancer Occurrence in Southern Karnataka.

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Zhang, Jia Hua, Cui Hao, and Feng Mei Yao. "Urban Land Use Changes by the Integration of Remote Sensing, GIS, and Dynamics Modeling." Advanced Materials Research 726-731 (August 2013): 4645–49. http://dx.doi.org/10.4028/www.scientific.net/amr.726-731.4645.

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We developed an approach to assess urban land use changes that incorporates socio-economic and environmental factors with multinomial logistic model, remote sensing data and GIS, and to quantify the impact of macro variables on land use of urban areas for the years 1990, 2000 and 2010 in Binhai New Area, China. The Markov transition matrix was designed to integrate with multinomial logistic model to illustrate and visualize the predicted land use surface. The multinomial logistic model was evaluated by means of Likelihood ratio test and Pseudo R-Square and showed a relatively good simulation. The prediction map of 2010 showed accurate rates 78.54%, 57.25% and 70.38%, respectively.

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Murata, Atsuo, Yoshito Fujii, and Kensuke Naitoh. "Multinomial Logistic Regression Model for Predicting Driver's Drowsiness Using Behavioral Measures." Procedia Manufacturing 3 (2015): 2426–33. http://dx.doi.org/10.1016/j.promfg.2015.07.502.

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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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Ahmed, Mohamed Abdelhameed, and Mahmoud Abdelsalam Ahmed. "Farmers’ Perception and Adaptation to Climate Change: Multinomial Logistic Model Evidence." International Journal of Economics and Management Studies 6, no. 10 (October 25, 2019): 143–51. http://dx.doi.org/10.14445/23939125/ijems-v6i10p119.

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Tb Ai, Munandar, Sumiati Sumiati, and Vidila Rosalina. "Predictive Model for Heart Disease Diagnosis Based on Multinomial Logistic Regression." Information Technology and Control 50, no. 2 (June 17, 2021): 308–18. http://dx.doi.org/10.5755/j01.itc.50.2.27672.

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Many computational approaches are used to assist the analysis of influencing factors, as well as for the need forprediction and even classification of certain types of disease. In the case of disease classification, the data usedare often categorical data, both for dependent variables and for independent variables, which are the results ofconversion from numeric data. In other words, the data used are already unnatural. Conversion processes oftendo not have standard rules, thus affecting the accuracy of the classification results. This research was conductedto form a predictive model for heart disease diagnosis based on the natural data from the patients' medicalrecords, using the multinomial logistic regression approach. The medical record data were taken based on thepatients’ electrocardiogram information whose data had been cleansed first. Other models were also tested tosee the accuracy of the heart disease diagnosis against the same data. The results showed that multinomiallogistic regression had the highest level of accuracy compared to other computational techniques, amountingto 75.60%. The highest level of accuracy is obtained by involving all variables (based on the results of the firstexperiment). This research also produced seven regression equations to predict the heart disease diagnosisbased on the patients’ electrocardiogram data.

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Izadbakhsh, Hamidreza, Rassoul Noorossana, and Seyed Taghi Akhavan Niaki. "Monitoring multinomial logistic profiles in Phase I using log-linear models." International Journal of Quality & Reliability Management 35, no. 3 (March 5, 2018): 678–89. http://dx.doi.org/10.1108/ijqrm-04-2017-0068.

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Purpose The purpose of this paper is to apply Poisson generalized linear model (PGLM) with log link instead of multinomial logistic regression to monitor multinomial logistic profiles in Phase I. Hence, estimating the coefficients becomes easier and more accurate. Design/methodology/approach Simulation technique is used to assess the performance of the proposed algorithm using four different control charts for monitoring. Findings The proposed algorithm is faster and more accurate than the previous algorithms. Simulation results also indicate that the likelihood ratio test method is able to detect out-of-control parameters more efficiently. Originality/value The PGLM with log link has not been used to monitor multinomial profiles in Phase I.

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Ardoino, Ilaria, Monica Lanzoni, Giuseppe Marano, Patrizia Boracchi, Elisabetta Sagrini, Alice Gianstefani, Fabio Piscaglia, and Elia M. Biganzoli. "Widen NomoGram for multinomial logistic regression: an application to staging liver fibrosis in chronic hepatitis C patients." Statistical Methods in Medical Research 26, no. 2 (November 20, 2014): 823–38. http://dx.doi.org/10.1177/0962280214560045.

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The interpretation of regression models results can often benefit from the generation of nomograms, ‘user friendly’ graphical devices especially useful for assisting the decision-making processes. However, in the case of multinomial regression models, whenever categorical responses with more than two classes are involved, nomograms cannot be drawn in the conventional way. Such a difficulty in managing and interpreting the outcome could often result in a limitation of the use of multinomial regression in decision-making support. In the present paper, we illustrate the derivation of a non-conventional nomogram for multinomial regression models, intended to overcome this issue. Although it may appear less straightforward at first sight, the proposed methodology allows an easy interpretation of the results of multinomial regression models and makes them more accessible for clinicians and general practitioners too. Development of prediction model based on multinomial logistic regression and of the pertinent graphical tool is illustrated by means of an example involving the prediction of the extent of liver fibrosis in hepatitis C patients by routinely available markers.

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Hong, Seung Hee, and Adel Alaeddini. "A Multi-way Multi-task Learning Approach for Multinomial Logistic Regression." Methods of Information in Medicine 56, no. 04 (2017): 294–307. http://dx.doi.org/10.3414/me16-01-0112.

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SummaryObjectives: Whether they have been engineered for it or not, most healthcare systems experience a variety of unexpected events such as appointment miss-opportunities that can have significant impact on their revenue, cost and resource utilization. In this paper, a multi-way multi-task learning model based on multinomial logistic regression is proposed to jointly predict the occurrence of different types of miss-opportunities at multiple clinics.Methods: An extension of L 1/L 2 regulariza- tion is proposed to enable transfer of information among various types of miss-opportunities as well as different clinics. A proximal algorithm is developed to transform the convex but non-smooth likelihood function of the multi-way multi-task learning model into a convex and smooth optimization problem solvable using gradient descent algorithm.Results: A dataset of real attendance records of patients at four different clinics of a VA medical center is used to verify the performance of the proposed multi-task learning approach. Additionally, a simulation study, investigating more general data situations is provided to highlight the specific aspects of the proposed approach. Various individual and integrated multinomial logistic regression models with/without LASSO penalty along with a number of other common classification algorithms are fitted and compared against the proposed multi-way multi-task learning approach. Fivefold cross validation is used to estimate comparing models parameters and their predictive accuracy. The multi-way multi-task learning framework enables the proposed approach to achieve a considerable rate of parameter shrinkage and superior prediction accuracy across various types of miss-opportunities and clinics.Conclusions: The proposed approach provides an integrated structure to effectively transfer knowledge among different miss-opportunities and clinics to reduce model size, increase estimation efficacy, and more importantly improve predictions results. The proposed framework can be effectively applied to medical centers with multiple clinics, especially those suffering from information scarcity on some type of disruptions and/or clinics.

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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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Kim, Kangmin, and Mingue Park. "Statistical micro matching using a multinomial logistic regression model for categorical data." Communications for Statistical Applications and Methods 26, no. 5 (September 30, 2019): 507–17. http://dx.doi.org/10.29220/csam.2019.26.5.507.

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Mahapatra, Krushna, and Shashi Kant. "Tropical deforestation: a multinomial logistic model and some country-specific policy prescriptions." Forest Policy and Economics 7, no. 1 (January 2005): 1–24. http://dx.doi.org/10.1016/s1389-9341(03)00064-9.

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Cao, Guofeng, Phaedon C. Kyriakidis, and Michael F. Goodchild. "A multinomial logistic mixed model for the prediction of categorical spatial data." International Journal of Geographical Information Science 25, no. 12 (December 2011): 2071–86. http://dx.doi.org/10.1080/13658816.2011.600253.

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Augustin, Nicole H., Roger P. Cummins, and Donald D. French. "Exploring spatial vegetation dynamics using logistic regression and a multinomial logit model." Journal of Applied Ecology 38, no. 5 (October 2001): 991–1006. http://dx.doi.org/10.1046/j.1365-2664.2001.00653.x.

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Szczepaniak-Kozak, Anna, Ewa Bakinowska, and Katerina Strani. "Measuring change in longitudinal research on pragmatic competence: A multinomial logistic model." Biometrical Letters 57, no. 2 (December 1, 2020): 195–220. http://dx.doi.org/10.2478/bile-2020-0013.

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Summary This paper focuses on pragmatic competence development in second or foreign language learners. In particular, it attempts to fill the significant research gap in measuring change in pragmatic competence and capturing pragmalinguistic and sociopragmatic development over time. The paper proposes an innovative approach by applying a logistic model with multinomial distribution for measuring change in InterLanguage Pragmatics Research (ILP). Applied in the field of pragmatics, this statistical tool offers a comprehensive and flexible approach to modelling relations between independent and dependent variables in ILP research. The model is tested in a longitudinal study of Polish undergraduate students learning English, and specifically in the way they formulate requests by means of requestive directness strategies. The paper concludes that, regardless of time elapsing, the factors P (power distance) and D (social distance) have a highly significant influence on the use of requestive directness strategies by Poles learning EFL. Furthermore, the analysis indicates that the pragmatic output of Poles learning EFL is dependent on one more independent variable: the estimation of future social distance (F).

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Lohse, Tina, Sabine Rohrmann, David Faeh, and Torsten Hothorn. "Continuous outcome logistic regression for analyzing body mass index distributions." F1000Research 6 (November 1, 2017): 1933. http://dx.doi.org/10.12688/f1000research.12934.1.

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Body mass indices (BMIs) are applied to monitor weight status and associated health risks in populations. Binary or multinomial logistic regression models are commonly applied in this context, but are only applicable to BMI values categorized within a small set of defined ad hoc BMI categories. This approach precludes comparisons with studies and models based on different categories. In addition, ad hoc categorization of BMI values prevents the estimation and analysis of the underlying continuous BMI distribution and leads to information loss. As an alternative to multinomial regression following ad hoc categorization, we propose a continuous outcome logistic regression model for the estimation of a continuous BMI distribution. Parameters of interest, such as odds ratios for specific categories, can be extracted from this model post hoc in a general way. A continuous BMI logistic regression that describes BMI distributions avoids the necessity of ad hoc and post hoc category choice and simplifies between-study comparisons and pooling of studies for joint analyses. The method was evaluated empirically using data from the Swiss Health Survey.

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Hemri, Stephan, Thomas Haiden, and Florian Pappenberger. "Discrete Postprocessing of Total Cloud Cover Ensemble Forecasts." Monthly Weather Review 144, no. 7 (June 23, 2016): 2565–77. http://dx.doi.org/10.1175/mwr-d-15-0426.1.

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Abstract This paper presents an approach to postprocess ensemble forecasts for the discrete and bounded weather variable of total cloud cover. Two methods for discrete statistical postprocessing of ensemble predictions are tested: the first approach is based on multinomial logistic regression and the second involves a proportional odds logistic regression model. Applying them to total cloud cover raw ensemble forecasts from the European Centre for Medium-Range Weather Forecasts improves forecast skill significantly. Based on stationwise postprocessing of raw ensemble total cloud cover forecasts for a global set of 3330 stations over the period from 2007 to early 2014, the more parsimonious proportional odds logistic regression model proved to slightly outperform the multinomial logistic regression model.

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Camey, Suzi Alves, Vanessa Bielefeldt Leotti Torman, Vania Naomi Hirakata, Renan Xavier Cortes, and Alvaro Vigo. "Bias of using odds ratio estimates in multinomial logistic regressions to estimate relative risk or prevalence ratio and alternatives." Cadernos de Saúde Pública 30, no. 1 (January 2014): 21–29. http://dx.doi.org/10.1590/0102-311x00077313.

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Recent studies have emphasized that there is no justification for using the odds ratio (OR) as an approximation of the relative risk (RR) or prevalence ratio (PR). Erroneous interpretations of the OR as RR or PR must be avoided, as several studies have shown that the OR is not a good approximation for these measures when the outcome is common (> 10%). For multinomial outcomes it is usual to use the multinomial logistic regression. In this context, there are no studies showing the impact of the approximation of the OR in the estimates of RR or PR. This study aimed to present and discuss alternative methods to multinomial logistic regression based upon robust Poisson regression and the log-binomial model. The approaches were compared by simulating various possible scenarios. The results showed that the proposed models have more precise and accurate estimates for the RR or PR than the multinomial logistic regression, as in the case of the binary outcome. Thus also for multinomial outcomes the OR must not be used as an approximation of the RR or PR, since this may lead to incorrect conclusions.

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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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Fatah, Khwazbeen Saida. "A Multinomial Logistic Regression Model for Analyzing Attitudes towards Political Activities: A Case Study in Erbil/ Kurdistan Region of Iraq." Journal of Zankoy Sulaimani - Part A 15, no. 2 (March 4, 2013): 73–87. http://dx.doi.org/10.17656/jzs.10248.

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Kim, Jae-Cheon, and Sun-Ju Kim. "Analysis of the characteristics of housing pension subscribers using the multinomial logistic model." Korea Association Of Real Estate Law 25, no. 2 (June 30, 2021): 51–67. http://dx.doi.org/10.32989/rel.2021.25.2.51.

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Głuszak, Michał. "Multinomial Logit Model Of Housing Demand In Poland." Real Estate Management and Valuation 23, no. 1 (March 1, 2015): 84–89. http://dx.doi.org/10.1515/remav-2015-0008.

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Abstract When compared to mature housing markets, little has been done to understand the nature of demand on emerging housing markets in Central and Eastern Europe and to develop testable models for post-socialist economies. With the exception of Bazyl 2009 and Głuszak 2010, there is hardly any econometric evidence on factors behind housing tenure choices in Poland. The article focus mainly on: permanent (and current) income, household structure, lifecycle, and differences between local market characteristics. In the research, multinomial logistic regression is used to analyze factors that increase the probability of young households becoming homeowners. The major objectives of the study are: 1) estimation of housing demand at household level, 2) discussion of factors increasing the probability of becoming homeowner, 3) discussion of advantages and limitations of using classical qualitative response models to estimate housing demand. The research is based on latest European Union Statistics on Income and Living Conditions (EUSILC) 2007-2010 dataset (panel of approx. 4,500 households).

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Saputri, Devi Arum, and Noven Suprayogi. "FAKTOR-FAKTOR KEUANGAN YANG MEMPENGARUHI RATING SUKUK DENGAN MENGGUNAKAN MODEL REGRESI MULTINOMIAL LOGISTIK." Jurnal Ekonomi Syariah Teori dan Terapan 5, no. 6 (June 18, 2019): 436. http://dx.doi.org/10.20473/vol5iss20186pp436-450.

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This research aims to explain the influence of financial performance towards therate of sukuk listed in Indonesia Stock Exchange in the period of 2013 to 2015. Independent variables used is the ratio of return on assets, current ratio and growth as a proxy for financial performance, and the dependent variable used is sukuk rating. The analytical method used in this study is Multinomial Logistic Regression.The results of this study showed that simultaneous financial performance significantly affect the rating sukuk, and individually there are two models produced.In the first model, the ROA and CR have positive influenceon the idA rating compared to the idAAA rating. In the second model, the ROA and CR also have positive influence on the idAA rating compared to the idAAA rating.

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Siraj, Mahwish, and Humayun Khan. "Impact of Micro Hydropower Projects on Household Income, Expenditure and Diversification of Livelihood Strategies in Azad Jammu and Kashmir." Pakistan Development Review 58, no. 1 (March 1, 2019): 45–63. http://dx.doi.org/10.30541/v58i1pp.45-63.

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The study examines the impact of Micro Hydropower (MHP) projects on households’ income, consumption and diversification of livelihood strategies in District Hattian Bala, Azad Jammu and Kashmir. A multinomial logistic model is used to investigate the possible role of MHP and other control variables on households’ adoption of livelihood strategies. The Results show that MHP-micro hydropower has a positive significant effect on household’s adoption of non-farm and diversified livelihood strategies. These findings suggest that MHP projects in Northern areas of Pakistan could help in improving household’s income and consumption through adoption of high income livelihood strategies. Keywords: Micro Hydropower (MHP), Livelihood Strategies, Income and Expenditures, Poverty Alleviation, Multinomial Logistic Model

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Okada, Atsushi, Ryosuke Ando, Kazumi Taguchi, Shuzo Hamamoto, Rei Unno, Teruaki Sugino, Yutaro Tanaka, et al. "Identification of new urinary risk markers for urinary stones using a logistic model and multinomial logit model." Clinical and Experimental Nephrology 23, no. 5 (January 18, 2019): 710–16. http://dx.doi.org/10.1007/s10157-019-01693-x.

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Maddumala, Venkata Rao, and Arunkumar R. "Body Mass Index Prediction and Classification Based on Facial Morphological Cues Using Multinomial Logistic Regression." Revue d'Intelligence Artificielle 35, no. 2 (April 30, 2021): 105–13. http://dx.doi.org/10.18280/ria.350201.

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This paper presents a novel method for body mass index prediction and classification based on the multinomial logistic regression model. The facial geometrical features are extracted and the logistic regression model parameters estimated based on the features. Based on the model parameters, the logistic model is fit in to predict the body mass index and classifies. Two different facial datasets are taken into account for the experiments. Each dataset is divided into two sets. One set is used to estimate the parameters while the other is used to fit-in the model and predicts the body mass index and classifies itself. The obtained outcome results show that the performance of the proposed method is comparable to the state-of-the-art techniques.

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Ntzoufras, Ioannis, Vasilis Palaskas, and Sotiris Drikos. "Bayesian models for prediction of the set-difference in volleyball." IMA Journal of Management Mathematics 32, no. 4 (April 12, 2021): 491–518. http://dx.doi.org/10.1093/imaman/dpab007.

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Abstract We study and develop Bayesian models for the analysis of volleyball match outcomes as recorded by the set-difference. Due to the peculiarity of the outcome variable (set-difference) which takes discrete values from $-3$ to $3$, we cannot consider standard models based on the usual Poisson or binomial assumptions used for other sports such as football/soccer. Hence, the first and foremost challenge was to build models appropriate for the set-difference of each volleyball match. Here we consider two major approaches: (a) an ordered multinomial logistic regression model and (b) a model based on a truncated version of the Skellam distribution. For the first model, we consider the set-difference as an ordinal response variable within the framework of multinomial logistic regression models. Concerning the second model, we adjust the Skellam distribution to account for the volleyball rules. We fit and compare both models with the same covariate structure as in Karlis & Ntzoufras (2003). Both models are fitted, illustrated and compared within Bayesian framework using data from both the regular season and the play-offs of the season 2016/17 of the Greek national men’s volleyball league A1.

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Darrah, Abigail J., Jonathan B. Cohen, and Paul M. Castelli. "A Bayesian multinomial logistic exposure model for estimating probabilities of competing sources of nest failure." Ibis 160, no. 1 (August 10, 2017): 23–35. http://dx.doi.org/10.1111/ibi.12510.

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Kuss, Oliver, and Dale McLerran. "A note on the estimation of the multinomial logistic model with correlated responses in SAS." Computer Methods and Programs in Biomedicine 87, no. 3 (September 2007): 262–69. http://dx.doi.org/10.1016/j.cmpb.2007.06.002.

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Pham, Binh Thai, Tran Van Phong, Huu Duy Nguyen, Chongchong Qi, Nadhir Al-Ansari, Ata Amini, Lanh Si Ho, et al. "A Comparative Study of Kernel Logistic Regression, Radial Basis Function Classifier, Multinomial Naïve Bayes, and Logistic Model Tree for Flash Flood Susceptibility Mapping." Water 12, no. 1 (January 15, 2020): 239. http://dx.doi.org/10.3390/w12010239.

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Risk of flash floods is currently an important problem in many parts of Vietnam. In this study, we used four machine-learning methods, namely Kernel Logistic Regression (KLR), Radial Basis Function Classifier (RBFC), Multinomial Naïve Bayes (NBM), and Logistic Model Tree (LMT) to generate flash flood susceptibility maps at the minor part of Nghe An province of the Center region (Vietnam) where recurrent flood problems are being experienced. Performance of these four methods was evaluated to select the best method for flash flood susceptibility mapping. In the model studies, ten flash flood conditioning factors, namely soil, slope, curvature, river density, flow direction, distance from rivers, elevation, aspect, land use, and geology, were chosen based on topography and geo-environmental conditions of the site. For the validation of models, the area under Receiver Operating Characteristic (ROC), Area Under Curve (AUC), and various statistical indices were used. The results indicated that performance of all the models is good for generating flash flood susceptibility maps (AUC = 0.983–0.988). However, performance of LMT model is the best among the four methods (LMT: AUC = 0.988; KLR: AUC = 0.985; RBFC: AUC = 0.984; and NBM: AUC = 0.983). The present study would be useful for the construction of accurate flash flood susceptibility maps with the objectives of identifying flood-susceptible areas/zones for proper flash flood risk management.

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Ani, N. V., T. P. Ogundunmade, D. Daniel, K. A. Raheem, E. O. Odirichukwu, and U. I. Osuagwuh. "Comparative Durability of Common Stains Used for Exfoliative Vaginal ‎Cytology." Sahel Journal of Veterinary Sciences 18, no. 2 (June 30, 2021): 9–16. http://dx.doi.org/10.54058/saheljvs.v18i2.220.

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In a study to compare the durability of commonly used stains (Giemsa, Leishman, Wright, Eosin, Nigrosin and Gentian violet) for exfoliative vaginal cytology, vaginal smear was obtained from eleven apparently healthy West African Dwarf (WAD) female Goats and processed according to standard technique. Scores (0-3) were given on four parameters namely background of smears, overall staining pattern, cytoplasmic staining and nuclear staining. Quality index one (QI-I) was calculated from the ratio of score achieved to the maximum score possible (12), immediately post staining while quality index–II (QI-II) was obtained 35 days after. Calculation for durability index (DI) was self-derived and equalled to ratio of QI-II to QI-I. The data were presented as mean ± SEM. Multinomial logistic regression model was generated for the QI-I and QI-II using durability index as reference category. Giemsa, Leishman and Wright stains were more durable than others with their mean DI values significantly (P < 0.05) higher than Gentian violet, Nigrosin and Eosin.The model showed 89.2% overall model accuracy for the multinomial logistic regression model and 81.5% for the multinomial Bayes Naïve regression model. In conclusion, Giemsa, Leishman and Wright stains were more reliable and durable for exfoliative vaginal cytology compared to the other stains.

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MA-A-LEE, Arinda, Nattakit PIPATJATURON, and Phattrawan TONGKUMCHUM. "Correcting Misreported Multinomial Outcome Data Based on Logistic Regression Model with Application to Stroke Mortality in Thailand." Walailak Journal of Science and Technology (WJST) 15, no. 5 (February 13, 2017): 397–408. http://dx.doi.org/10.48048/wjst.2018.2817.

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Causes of death in Thailand are misreported; about 40 % of deaths have been recorded as “ill-defined”. This study aims to describe statistical methods to correct misreported multinomial outcome by using verbal autopsy (VA) data. Since the outcome is a nominal variable, with 21 levels, the appropriate model for systematic analysis of death by ICD-10 code is multinomial regression. Moreover, it is simpler and more informative to separately fit logistic regression models to the 21 outcome cause groups, and then rescale the results to ensure that the total number of estimated deaths for each group match those reported in the corresponding populations. This method also gives confidence intervals for percentages of deaths in cause groups for levels of each risk factor, adjusted for other risk factors. These confidence intervals are compared with bar charts of sample percentages, to assess evidence of confounding bias. The methods were illustrated using stroke deaths. Area plots are used to show results by gender, age group, and year. The most misclassified stroke deaths were ill-defined, other cardio vascular disease, mental and nerve (outside-hospital), septicemia, and respiratory disease (in-hospital).

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Jeong, Bomi, Hyunjeong Cho, Jieun Kim, Soon Kil Kwon, SeungWoo Hong, ChangSik Lee, TaeYeon Kim, Man Sik Park, Seoksu Hong, and Tae-Young Heo. "Comparison between Statistical Models and Machine Learning Methods on Classification for Highly Imbalanced Multiclass Kidney Data." Diagnostics 10, no. 6 (June 18, 2020): 415. http://dx.doi.org/10.3390/diagnostics10060415.

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This study aims to compare the classification performance of statistical models on highly imbalanced kidney data. The health examination cohort database provided by the National Health Insurance Service in Korea is utilized to build models with various machine learning methods. The glomerular filtration rate (GFR) is used to diagnose chronic kidney disease (CKD). It is calculated using the Modification of Diet in Renal Disease method and classified into five stages (1, 2, 3A and 3B, 4, and 5). Different CKD stages based on the estimated GFR are considered as six classes of the response variable. This study utilizes two representative generalized linear models for classification, namely, multinomial logistic regression (multinomial LR) and ordinal logistic regression (ordinal LR), as well as two machine learning models, namely, random forest (RF) and autoencoder (AE). The classification performance of the four models is compared in terms of accuracy, sensitivity, specificity, precision, and F1-Measure. To find the best model that classifies CKD stages correctly, the data are divided into a 10-fold dataset with the same rate for each CKD stage. Results indicate that RF and AE show better performance in accuracy than the multinomial and ordinal LR models when classifying the response variable. However, when a highly imbalanced dataset is modeled, the accuracy of the model performance can distort the actual performance. This occurs because accuracy is high even if a statistical model classifies a minority class into a majority class. To solve this problem in performance interpretation, we not only consider accuracy from the confusion matrix but also sensitivity, specificity, precision, and F-1 measure for each class. To present classification performance with a single value for each model, we calculate the macro-average and micro-weighted values for each model. We conclude that AE is the best model classifying CKD stages correctly for all performance indices.

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Adwiluvito, Hernanto, and Indonesian Journal of Statistics and Its Applications IJSA. "DETERMINAN PEMILIHAN MODA TRANSPORTASI PEKERJA KOMUTER JABODEBATEK DENGAN MODEL REGRESI LOGISTIK MULTINOMIAL MULTILEVEL." Indonesian Journal of Statistics and Its Applications 3, no. 1 (February 28, 2019): 49–61. http://dx.doi.org/10.29244/ijsa.v3i1.184.

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The BPS noted that commuters in Jabodetabek had increased by 400 thousand people from 2001 to 2014. The BPS also recorded that around 81,3% of the commuters in Jabodetabek were workers. A growing number of commuter workers in Jabodetabek makes transportation is very important to support the connection of suburban area and workplace in Jakarta. The result showed that 73% of the commuter workers used private transportation, 19% used ground public transportation and the rest of commuter workers used train. This research use Jabodetabek Commuter Survey 2014 as the main source data to shed light on how socioeconomic factors and spatial attributes affect the selection of a primary mode of transportation for commuter workers. Using multilevel multinomial logistic regression, the result confirm that the age, sex, marital status, ownership of vehicle, travel distance and time have a significant effect in explaining train choice. Furthermore, the result also showed that the age, sex, marital status, income, ownership of vehicle, travel distance and cost are found to be significant in explaining ground public transportation choice.

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Bergee, Martin J., and Claude R. Westfall. "Stability of a Model Explaining Selected Extramusical Influences on Solo and Small-Ensemble Festival Ratings." Journal of Research in Music Education 53, no. 4 (December 2005): 358–74. http://dx.doi.org/10.1177/002242940505300407.

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This is the third study in a line of inquiry whose purpose has been to develop a theoretical model of selected extramusical variables' influence on solo and small-ensemble festival ratings. Authors of the second of these (Bergee & McWhirter, 2005) had used binomial logistic regression as the basis for their model formulation strategy. Their final model included as statistically significant variables time of day (morning/afternoon), type of event (solo/ensemble), performing medium (vocal/instrumental), school size classification (Larger/smaller), district level of expenditure per average daily attendance (high/middle/low), and type of event by performing medium interaction. For the present study, we examined the stability of their model for a different data set (the following year's ratings) by means of a similar but modified strategy. Among other modifications, we used multinomial instead of binomial logistic regression. Utimately, the present study's model converged strongly on Bergee and Mc Whirter's preliminary one. Time of day, type of event, school size, district expenditure per average daily attendance, geographical district (metropolitan/nonmetropolitan), and the time of day by geographical district interaction contributed significantly to the present study's multinomial model. Theoretical modeling thus far suggests that performing as a soloist later in the day and entering from a large, metropolitan-area, relatively high-expenditure school serve as success influences. The multinomial model showed a gradation of influences from ratings of I through II to < III.

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Catalani, Mauro. "A logistic distribution model of new short sea shipping line along a mutimodal corridor in Italy." European Transport/Trasporti Europei 81, ET.2021 (March 2021): 1–10. http://dx.doi.org/10.48295/et.2021.81.5.

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The purpose of the application focuses on an intermodal model (RPL) to simulate the transport choice for freight sending on the most relevant corridor Naples–Milan. In this, operate a rail- road system with the introduction a new short sea shipping (SSS) intermodal line (Naples Sea Genova road Milan). The paper considers a collaboration with a multimodal transport operator, with many logistic platforms in Italy to analyze the degree of competition inside corridor. An application along this very congested route Milan (Segrate interport) - Nola (Naples interport) was used. The econometric models applied to operator choices are a random parameter logit model vs multinomial logit model with frequency, type of load and cost as main parameters.

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Bakinowska, Ewa, Wiesław Pilarczyk, Agnieszka Osiecka, and Kazimierz Wiatr. "Analysis of Downy Mildew Infection of Field Pea Varieties Using the Logistic Model." Journal of Plant Protection Research 52, no. 2 (April 1, 2012): 240–46. http://dx.doi.org/10.2478/v10045-012-0038-z.

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Analysis of Downy Mildew Infection of Field Pea Varieties Using the Logistic ModelThe logistic model is commonly used for analysis of discrete, multinomial data. Such a model was used for the statistical evaluation of data concerning infection of field pea varieties by downy mildew, in two series of field trials. Each series consisted of experiments performed in locations spread over the whole of Poland in the time period from 2002 to 2005. Varieties cultivated on light soils were compared in the first series, and varieties cultivated on rich soils in the second. The most resistant varieties were identified (Sokolik - light soils, Terno - rich soils) and significant differences among varieties were detected. Estimators of model parameters were found using the Fisher scoring method implemented inlogistic glmprocedure of the SAS system.

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Agasa, Lameck, Anakalo Shitandi, Wycliffe Cheruyout, Wycliff Ombasa, and Onsongo Nyaundi. "Fitting a Multinomial Logistic Regression (MLR) Model to Need Assessment Survey on E-learning in Kenya." Asian Research Journal of Mathematics 3, no. 4 (January 10, 2017): 1–9. http://dx.doi.org/10.9734/arjom/2017/30651.

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Journal articles on the topic 'Multinomial Logistic Model'

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