Relevant bibliographies by topics / Multinomial Logistic Model

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

Academic literature on the topic 'Multinomial Logistic Model'

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Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "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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Dissertations / Theses on the topic "Multinomial Logistic Model"

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Frühwirth-Schnatter, Sylvia, and Rudolf Frühwirth. "Bayesian Inference in the Multinomial Logit Model." Austrian Statistical Society, 2012. http://epub.wu.ac.at/5629/1/186%2D751%2D1%2DSM.pdf.

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The multinomial logit model (MNL) possesses a latent variable representation in terms of random variables following a multivariate logistic distribution. Based on multivariate finite mixture approximations of the multivariate logistic distribution, various data-augmented Metropolis-Hastings algorithms are developed for a Bayesian inference of the MNL model.
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Alfallaj, Ibrahim. "Analysis of crash and survey data to identify young drivers' distractions in Kansas." Diss., Kansas State University, 2018. http://hdl.handle.net/2097/38785.

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Doctor of Philosophy
Department of Civil Engineering
Sunanda Dissanayake
Young drivers are over-represented in crashes when compared to other age group drivers. Distracted driving is one of the major causes of traffic crashes by young drivers. The objective of this study was to assess the hazards of distracted driving among teenage (15–20 year old) and young-adult (21–26 year old) drivers in Kansas. This study used five years of crash data from the Kansas Crash and Analysis Reporting System (KCARS) database from 2011 to 2015. A multinomial logit modeling was used to identify the odds that a driver with a certain type of distraction would be involved in one of the three most common crash types: rear-end, angular, and single-vehicle crashes. Furthermore, ordered logistic modeling was used to analyze the crash data to identify the odds of more severe injuries for teenage and young-adult distracted drivers and their passengers involved in crashes. Survey data was used to develop a structural equation model (SEM) to define the relationship among young drivers’ characteristics (e.g., participants’ socioeconomic and demographic status), attitudes, and behaviors associated with distracted driving and cell phone use while driving. Preliminary analysis showed that more than 12% of the total young drivers’ crashes were distraction-affected crashes. According to the multinomial logit model results, most distraction types for teenage and young-adult drivers are related to rear-end or angular collisions. However, when distracted by cell phones at night, teenage drivers had a greater probability of being involved in single-vehicle crashes. In addition, when teenage drivers drove with their peers as front-seat passengers and were distracted in/on vehicle or by other electronic devices, they were more likely to be involved in single-vehicle crashes. Young-adult drivers distracted in/on vehicle or by cell phones under different conditions such as while driving old or sport utility vehicles, on curved roads, or at intersections, they were more likely to be involved in single-vehicle or angular crashes. Whereas, when they were inattentive during the weekend, rear-end collisions were the most likely collision type. According to the results of the ordered logistic model, teenage and young-adult drivers were more likely to be severely injured in cell phone-related crashes. More specifically, female teenage drivers had a greater probability of being severely injured than male teenage drivers when they were distracted by a cell phone, inside the vehicle, or were inattentive. Young-adult drivers that were distracted on road construction work zones by a cell phone or inside the vehicle, they and their passengers had a greater likelihood of sustaining a severe injury. The SEM results revealed that teenage drivers are more prone than young-adult drivers to drive while distracted and are less likely to support the Kansas laws that ban cell phone use while driving. Also, the model results showed that young drivers who have been involved in crashes or near-crashes during the previous year are more likely to drive while distracted. These results indicate that distractions create threats to the lives of young Kansas drivers, their passengers, and other road users.
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Magalhães, Cloé Leal de. "How bank lending affects firms' lifecycle : a Markov chain approach." Master's thesis, Instituto Superior de Economia e Gestão, 2019. http://hdl.handle.net/10400.5/19182.

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Mestrado em Econometria Aplicada e Previsão
Esta dissertação analisa o impacto da concessão de crédito adicional a empresas não rentáveis sobre a sua probabilidade de se manterem não rentáveis, recuperarem para empresas rentáveis ou para saírem do mercado. Esta avaliação é efetuada através da estimação de um processo de Markov condicional à existência de crédito adicional, usando as estimativas do modelo logit multinomial. A aplicação deste modelo aos dados ao nível da empresa e do banco para Portugal entre 2011 e 2015 mostra que a concessão de crédito adicional teve um impacto positivo nas taxas de sobrevivência e recuperação das empresas não rentáveis, em contradição com alguma investigação recente sobre o tema.
This dissertation analyses how additional loans granted to non-profitable firms affect their probability to remain non-profitable, recover to profitable or exit the market. This assessment is carried out through the estimation of a Markov process conditional to the existence of additional bank loans, using the multinomial logit model estimates. Applying this model to Portuguese firm and bank level data from 2011 to 2015, the results point to a positive effect of additional bank loans over survival and recovery rates of non-profitable firms, contradicting some recent research on this topic.
info:eu-repo/semantics/publishedVersion
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Byrne, Evan. "Inference in Generalized Linear Models with Applications." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1555152640361367.

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Pax, Benjamin M. "Prediction of Bronchopulmonary Dysplasia by a Priori and Longitudinal Risk Factors in Extremely Premature Infants." Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case1522686042230784.

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Allan, Michelle L. "Measuring Skill Importance in Women's Soccer and Volleyball." Diss., CLICK HERE for online access, 2009. http://contentdm.lib.byu.edu/ETD/image/etd2809.pdf.

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Wang, Jie. "Incorporating survey weights into logistic regression models." Digital WPI, 2013. https://digitalcommons.wpi.edu/etd-theses/267.

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Incorporating survey weights into likelihood-based analysis is a controversial issue because the sampling weights are not simply equal to the reciprocal of selection probabilities but they are adjusted for various characteristics such as age, race, etc. Some adjustments are based on nonresponses as well. This adjustment is accomplished using a combination of probability calculations. When we build a logistic regression model to predict categorical outcomes with survey data, the sampling weights should be considered if the sampling design does not give each individual an equal chance of being selected in the sample. We rescale these weights to sum to an equivalent sample size because the variance is too small with the original weights. These new weights are called the adjusted weights. The old method is to apply quasi-likelihood maximization to make estimation with the adjusted weights. We develop a new method based on the correct likelihood for logistic regression to include the adjusted weights. In the new method, the adjusted weights are further used to adjust for both covariates and intercepts. We explore the differences and similarities between the quasi-likelihood and the correct likelihood methods. We use both binary logistic regression model and multinomial logistic regression model to estimate parameters and apply the methods to body mass index data from the Third National Health and Nutrition Examination Survey. The results show some similarities and differences between the old and new methods in parameter estimates, standard errors and statistical p-values.
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Schletze, Matthias. "Eine empirische Analyse des individuellen Verkehrsmittelwahlverhaltens am Beispiel der Stadt Dresden." Bachelor's thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-184848.

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Das Verkehrsmittelwahlverhalten von Menschen ist komplex. So spielen soziodemografische, sozioökonomische sowie raum- und siedlungsstrukturelle Merkmale eine Rolle. In dieser Arbeit wird dieses Verhalten untersucht. Dabei wird eine homogene Grundgesamtheit geschaffen, welche alle Personen beinhaltet, die sowohl über eine Dauerkarte des öffentlichen Personenverkehrs als auch einen Personenkraftwagen verfügen. Anhand derer soll eine deskriptive Analyse und eine multinomiale logistische Regression Aufschluss geben, ob es Unterschiede zwischen den jeweiligen Nutzergruppen gibt. So lässt sich die Gruppe der ÖV-Nutzer durch folgende Charakteristiken beschreiben: der Großteil sind Frauen, sowie Personen, die eine hohe schulische und berufliche Bildung besitzen. Des Weiteren werden eher weniger Wege mit dem ÖV als mit dem PKW zurückgelegt. Erwerbstätige hingegen entscheiden sich eher für den PKW
Human behavior towards the choice of transportation varies in very complex ways such as sociodemographics, socioeconomics as well as settlement structures. For this paper a homogenous population is created from season ticket holders for public transportation and car owners. Based on this population a descriptive analysis followed by a multinomial logistic regression is supposed to generate the differences between the user groups. The group of users of the public transportation system can be characterized as followed: the majority of users are women as well as highly educated people. Within this specific group distances are more likely to be covered by public transportation rather than by car. However the working population prefers to go by passenger car
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Mafra, Ana Carolina Cintra Nunes 1982. "Modelagem multinomial para a distribuição espacial do risco epidemiológico." [s.n.], 2011. http://repositorio.unicamp.br/jspui/handle/REPOSIP/311750.

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Orientador: Ricardo Carlos Cordeiro
Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Ciências Médicas
Made available in DSpace on 2018-08-18T15:08:16Z (GMT). No. of bitstreams: 1 Mafra_AnaCarolinaCintraNunes_D.pdf: 19877794 bytes, checksum: a74a4b2bf9bccffacddd691b458d1fd3 (MD5) Previous issue date: 2011
Resumo: A busca em compreender determinados fenômenos epidemiológicos muitas vezes envolve uma ferramenta denominada análise espacial do risco. O estudo do espaço em que ocorrem determinados desfechos permite ao pesquisador considerar informações não coletadas através de questionários ou prontuários médicos. Também insere questões sobre o que faz com que determinada área dentro da região de estudo se associe com maior risco ou proteção para o desfecho estudado. Existem muitos métodos para obter análises espaciais do risco, como os modelos aditivos generalizados, que permitem incluir nestas análises outras informações de interesse dos indivíduos estudados. Porém, atualmente, os estudos epidemiológicos que consideram a distribuição espacial do risco são analisados apenas com desfechos dicotômicos como, por exemplo, quando se classifica o indivíduo em doente ou não-doente. Esta é uma limitação que este trabalho visa superar ao apresentar um processo analítico da distribuição espacial do risco quando se tem uma variável resposta multinomial. Além de apresentar esta nova ferramenta, este trabalho analisou dois desfechos epidemiológicos: o primeiro é proveniente de um estudo caso-controle sobre acidentes de trabalhado na cidade de Piracicaba em que a resposta foi: casos graves, casos leves ou controles; outra ilustração provém de um estudo transversal sobre criadouros de mosquitos no Distrito Sul de Campinas, onde se encontrou muitos criadouros, poucos criadouros ou nenhum criadouro. Primeiramente, faz-se necessária uma discussão sobre a adequação de cada modelo multinomial a alguns estudos epidemiológicos. Também se discute a escolha de um entre diversos modelos multinomiais e apresenta-se a maneira de interpretar os resultados da análise. Para tornar este método acessível a outros pesquisadores, são apresentadas funções computacionais para o processo analítico
Abstract: The search for understanding some epidemiological phenomena often involves an tool called spatial analysis of risk. The study of space in which certain outcomes occur allows the researcher to consider information that can not be collected through questionnaires or medical records. It also puts questions about what makes a certain area within the study region was associated with greater risk or protection for the outcome studied. Many techniques are used for this kind of study as the generalized additive models that fit the spatial analysis of the risk with others informations of interest. But now, epidemiological studies that consider the spatial distribution of risk are analyzed only with dichotomous outcomes, such as when it classifies the individual in case or control. This is a limitation that this study aims to overcome when presenting an analytical process of the spatial distribution of risk when you have a multinomial response variable. In addition to presenting this new tool, this study analyzed two outcomes: first, from a case-control study of precarious workers in the city of Piracicaba in which the response was: severe cases, mild cases or controls. Another illustration comes from a cross-sectional study on mosquito breeding sites in the Southern District of Campinas, where we met many breeding sites, few or no breeding sites. First, it is necessary a discussion on the appropriateness of each multinomial model to some epidemiological studies. It also discusses the choice of one among several multinomial models and shows the way to interpret the results of the analysis. We present the computational functions for the analytical process to make this method accessible to other researchers
Doutorado
Epidemiologia
Doutor em Saude Coletiva
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Lundberg, Gustav. "Automatic map generation from nation-wide data sources using deep learning." Thesis, Linköpings universitet, Statistik och maskininlärning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-170759.

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The last decade has seen great advances within the field of artificial intelligence. One of the most noteworthy areas is that of deep learning, which is nowadays used in everything from self driving cars to automated cancer screening. During the same time, the amount of spatial data encompassing not only two but three dimensions has also grown and whole cities and countries are being scanned. Combining these two technological advances enables the creation of detailed maps with a multitude of applications, civilian as well as military.This thesis aims at combining two data sources covering most of Sweden; laser data from LiDAR scans and surface model from aerial images, with deep learning to create maps of the terrain. The target is to learn a simplified version of orienteering maps as these are created with high precision by experienced map makers, and are a representation of how easy or hard it would be to traverse a given area on foot. The performance on different types of terrain are measured and it is found that open land and larger bodies of water is identified at a high rate, while trails are hard to recognize.It is further researched how the different densities found in the source data affect the performance of the models, and found that some terrain types, trails for instance, benefit from higher density data, Other features of the terrain, like roads and buildings are predicted with higher accuracy by lower density data.Finally, the certainty of the predictions is discussed and visualised by measuring the average entropy of predictions in an area. These visualisations highlight that although the predictions are far from perfect, the models are more certain about their predictions when they are correct than when they are not.
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Books on the topic "Multinomial Logistic Model"

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Oran, Ahmad. Intermetropolitan Brazilian migration: Estimates of a multinomial logistic model. [Urbana, Ill.]: College of Commerce and Business Administration, University of Illinois at Urbana-Champaign, 1991.

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Book chapters on the topic "Multinomial Logistic Model"

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Madarshahian, Ramin, and Juan M. Caicedo. "Human Activity Recognition Using Multinomial Logistic Regression." In Model Validation and Uncertainty Quantification, Volume 3, 363–72. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15224-0_38.

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Braga, Ana Cristina, Vanda Urzal, and A. Pinhão Ferreira. "Orthodontics Diagnostic Based on Multinomial Logistic Regression Model." In Lecture Notes in Computer Science, 585–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39637-3_46.

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

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Cho, Wanhyun, Soonyoung Park, and Sangkyoon Kim. "Multiclass Data Classification Using Multinomial Logistic Gaussian Process Model." In Advances in Computer Science and Ubiquitous Computing, 126–30. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-7605-3_21.

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Murata, Atsuo, Yukio Ohta, and Makoto Moriwaka. "Multinomial Logistic Regression Model by Stepwise Method for Predicting Subjective Drowsiness Using Performance and Behavioral Measures." In Advances in Intelligent Systems and Computing, 665–74. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41694-6_64.

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Kumari, Dipti, and Kumar Rajnish. "Comparing Efficiency of Software Fault Prediction Models Developed Through Binary and Multinomial Logistic Regression Techniques." In Advances in Intelligent Systems and Computing, 187–97. New Delhi: Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2250-7_19.

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Karakara, Alhassan Abdul-Wakeel, and Evans S. C. Osabuohien. "Categorical Dependent Variables Estimations With Some Empirical Applications." In Applied Econometric Analysis, 164–89. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1093-3.ch008.

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Microeconomic datasets are usually large, mainly survey data. These data are samples of hundreds of respondents or group of respondents (e.g., households). The distributions of such data are mostly not normal because some responses/variables are discrete. Handling such datasets poses some problems of summarizing/reporting the important features of the data in estimations. This study concentrates on how to handle categorical variables in estimation/reporting based on theoretical and empirical knacks. This study used Ghana Demographic and Health Survey data for 2014 for illustration and elaborates on how to interpret results of binary and multinomial outcome regressions. Comparison is made on the different binary models, and binary logit is found to be weighted over the other binary models. Multinomial logistic model is best handled when the odds of one outcome versus the other outcome are independent of other outcome alternatives as verified by the Independent of Irrelevant Alternatives (IIA). Conclusions and suggestions for handling categorical models are discussed in the study.
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Lu, Kang Shou, John Morgan, and Jeffery Allen. "A Neural Network for Modeling Multicategorical Parcel Use Change." In Geographic Information Systems, 1297–308. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2038-4.ch078.

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This paper presents an artificial neural network (ANN) for modeling multicategorical land use changes. Compared to conventional statistical models and cellular automata models, ANNs have both the architecture appropriate for addressing complex problems and the power for spatio-temporal prediction. The model consists of two layers with multiple input and output units. Bayesian regularization was used for network training in order to select an optimal model that avoids over-fitting problem. When trained and applied to predict changes in parcel use in a coastal county from 1990 to 2008, the ANN model performed well as measured by high prediction accuracy (82.0-98.5%) and high Kappa coefficient (81.4-97.5%) with only slight variation across five different land use categories. ANN also outperformed the benchmark multinomial logistic regression by average 17.5 percentage points in categorical accuracy and by 9.2 percentage points in overall accuracy. The authors used the ANN model to predict future parcel use change from 2007 to 2030.
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"Multinomial Logistic Regression." In Logistic Regression Models, 403–28. Chapman and Hall/CRC, 2009. http://dx.doi.org/10.1201/9781420075779-13.

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Muñoz, Karla, Paul Mc Kevitt, Tom Lunney, Julieta Noguez, and Luis Neri. "An Emotional Student Model for Game-Based Learning." In Technologies for Inclusive Education, 175–97. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2530-3.ch009.

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Students’ performance and motivation are influenced by their emotions. Game-based learning (GBL) environments comprise elements that facilitate learning and the creation of an emotional connection with students. GBL environments include Intelligent Tutoring Systems (ITSs) to ensure personalized learning. ITSs reason about students’ needs and characteristics (student modeling) to provide suitable instruction (tutor modeling). The authors’ research is focused on the design and implementation of an emotional student model for GBL environments based on the Control-Value Theory of achievement emotions by Pekrun et al. (2007). The model reasons about answers to questions in game dialogues and contextual variables related to student behavior acquired through students’ interaction with PlayPhysics. The authors’ model is implemented using Dynamic Bayesian Networks (DBNs), which are derived using Probabilistic Relational Models (PRMs), machine learning techniques, and statistical methods. This work compares an earlier approach that uses Multinomial Logistic Regression (MLR) and cross-tabulation for learning the structure and conditional probability tables with an approach that employs Necessary Path Condition and Expectation Maximization algorithms. Results showed that the latter approach is more effective at classifying the control of outcome-prospective emotions. Future work will focus on applying this approach to classification of activity and outcome-retrospective emotions.
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Conference papers on the topic "Multinomial Logistic Model"

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Liu, Zezhao, and Q. Zhang. "A multinomial logistic model for ranking technical efficiency of public project." In 2014 11th International Conference on Service Systems and Service Management (ICSSSM). IEEE, 2014. http://dx.doi.org/10.1109/icsssm.2014.6943378.

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Barros, Alberto Pereira de, Francisco de Assis Tenorio de Carvalho, and Eufrasio de Andrade Lima Neto. "A pattern classifier for interval-valued data based on multinomial logistic regression model." In 2012 IEEE International Conference on Systems, Man and Cybernetics - SMC. IEEE, 2012. http://dx.doi.org/10.1109/icsmc.2012.6377781.

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Alrajeh, Abdullah, and Mahesan Niranjan. "Large-scale Reordering Model for Statistical Machine Translation using Dual Multinomial Logistic Regression." In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Stroudsburg, PA, USA: Association for Computational Linguistics, 2014. http://dx.doi.org/10.3115/v1/d14-1183.

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Li, Jun, Jose M. Bioucas-Dias, and Antonio Plaza. "Semi-supervised hyperspectral image classification using a new (soft) sparse multinomial logistic regression model." In 2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE, 2011. http://dx.doi.org/10.1109/whispers.2011.6080879.

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Efendi, Achmad, and Hafidz Wahyu Ramadhan. "Parameter estimation of multinomial logistic regression model using least absolute shrinkage and selection operator (LASSO)." In THE 8TH ANNUAL BASIC SCIENCE INTERNATIONAL CONFERENCE: Coverage of Basic Sciences toward the World’s Sustainability Challanges. Author(s), 2018. http://dx.doi.org/10.1063/1.5062766.

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Rekha, S. N., P. Aruna Jeyanthy, and D. Devaraj. "Multinomial Logistic Regression for Fault Type Detection in Bench Mark Fault Model of Wind Energy Conversion System." In 2019 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS). IEEE, 2019. http://dx.doi.org/10.1109/incos45849.2019.8951389.

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Ma, Yu-chun, Bing Wang, and Sheng-rui Zhang. "Identification of Risk Factors Associated with Motorcycle-Related Accidents Based on the Multinomial Logistic Model: A Case Study of a County in Xinjiang." In 15th COTA International Conference of Transportation Professionals. Reston, VA: American Society of Civil Engineers, 2015. http://dx.doi.org/10.1061/9780784479292.276.

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Tevdovski, Dragan. "Extreme Coexceedances in South Eastern European Stock Markets with Focus on EU Accession Countries." In International Conference on Eurasian Economies. Eurasian Economists Association, 2014. http://dx.doi.org/10.36880/c05.01034.

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Abstract:
The aim of this paper is to investigate and document the financial contagion of the South Eastern Europe (SEE) stock markets. Using a modification of the Bae et al. (2003) coexceedance approach based on multinomial logistic regressions we model the occurrence of the large negative or positive stock returns on a given day across the SEE stock markets. Specifically, we divide the SEE stock markets on two groups based on country’s EU membership in order to allow for transmission mechanism from major EU economies stock markets to EU member countries from SEE, and in addition, transitory effect from EU member countries from SEE to accession countries from SEE region. We test the persistence, asset class and volatility effects on the likelihood of the coexceedances in both SEE groups. We find that effects differ: (i) between negative and positive coexceedances and (ii) between the EU member countries and EU accession countries stock markets from SEE. The empirical evidence for the persistence effects, asset class and volatility effects in the SEE region should draw the attention of both investors and policy makers.
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Dong, Yiming, Conglin Pan, and Yaping Wei. "Influence of Land-Use on Travel Pattern of Shopping-Mall: A Subdivided Method of Multinomial Logistic Model and Case Study in Nine Sub-Districts of Hangzhou, China." In International Conference On Civil Engineering And Urban Planning 2012. Reston, VA: American Society of Civil Engineers, 2012. http://dx.doi.org/10.1061/9780784412435.058.

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Yu, Lijun, and Qiuyan Xie. "Bayesian estimation of multinomial probit model for commuter mode choice." In 2011 IEEE International Conference on Service Operations and Logistics and Informatics (SOLI). IEEE, 2011. http://dx.doi.org/10.1109/soli.2011.5986520.

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