Gotowe bibliografie tematyczne / Multinomial logit regression model

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Gotowa bibliografia na temat „Multinomial logit regression model”

Autor: Grafiati
Data publikacji: 2 czerwca 2025
Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "Multinomial logit regression model"

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Jakaitienė, Audronė. "Multinomial logit death forecasting model." Lietuvos matematikos rinkinys, no. III (December 17, 1999): 367–69. http://dx.doi.org/10.15388/lmd.1999.35662.

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The multinomial regression logit model is analyzed. The algorithms and software are made for this model in order to get estimation of parameters. Calculations are made using generated population of 1000 cases.
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Amalahu, Christian Chinenye, Joy Chioma Nwabueze, and Chibueze Barnabas Ekeadinotu. "EGG QUALITY ASSESSMENT: A MODEL COMPARISON APPROACH USING BAYESIAN MIXED LOGIT, MIXED LOGIT, LOGISTIC REGRESSION AND MULTINOMIAL REGRESSION MODELS." FUDMA JOURNAL OF SCIENCES 9, no. 5 (2025): 110–13. https://doi.org/10.33003/fjs-2025-0905-3656.

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This study compares the performance of Bayesian mixed logit, mixed logit, logistic regression, and multinomial regression models in analyzing egg quality. The results show that the Bayesian mixed logit model outperforms traditional models, with egg weights, shell thickness, and shape index emerging as significant determinants of egg quality. The Bayesian mixed logit model's superior performance is evident in its lower AIC, DIC, RMSE, and MAE values. These findings have implications for the poultry industry, highlighting the importance of considering complex relationships between egg quality tr
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McFadden, Daniel. "Regression-based specification tests for the multinomial logit model." Journal of Econometrics 34, no. 1-2 (1987): 63–82. http://dx.doi.org/10.1016/0304-4076(87)90067-4.

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Kalina, J. "Model choice for regression models with a categorical response." Journal of Applied Mathematics, Statistics and Informatics 18, no. 1 (2022): 59–71. http://dx.doi.org/10.2478/jamsi-2022-0005.

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Abstract The multinomial logit model and the cumulative logit model represent two important tools for regression modeling with a categorical response with numerous applications in various fields. First, this paper presents a systematic review of these two models including available tools for model choice (model selection). Then, numerical experiments are presented for two real datasets with an ordinal categorical response. These experiments reveal that a backward model choice procedure by means of hypothesis testing is more effective compared to a procedure based on Akaike information criterio
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Månsson, Kristofer, Ghazi Shukur, and B. M. Golam Kibria. "Performance of some ridge regression estimators for the multinomial logit model." Communications in Statistics - Theory and Methods 47, no. 12 (2018): 2795–804. http://dx.doi.org/10.1080/03610926.2013.784996.

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Głuszak, Michał. "Multinomial Logit Model Of Housing Demand In Poland." Real Estate Management and Valuation 23, no. 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 inc
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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 (2001): 991–1006. http://dx.doi.org/10.1046/j.1365-2664.2001.00653.x.

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Jakaitienė, Audronė. "Fixed time competing risk model." Lietuvos matematikos rinkinys 40 (December 18, 2000): 389–91. http://dx.doi.org/10.15388/lmr.2000.35185.

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The competing risks under multinomial regression logit model is analyzed. The algorithms and software are made for this model in order to get estimation of parameters. Calculations are made using data of Cardiology Institute about health of 45-60 years old men, which were collected from 1972 till 1977.
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Li, Xiaowei, Yuting Wang, Yao Wu, Jun Chen, and Jibiao Zhou. "Modeling Intercity Travel Mode Choice with Data Balance Changes: A Comparative Analysis of Bayesian Logit Model and Artificial Neural Networks." Journal of Advanced Transportation 2021 (September 14, 2021): 1–22. http://dx.doi.org/10.1155/2021/9219176.

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This study conducts a comprehensive comparative analysis of regression-based multinomial models and artificial neural network models in intercity travel mode choices. The four intercity travel modes of airplane, high-speed rail (HSR), train, and express bus were used for analysis. Passengers’ activity data over the process of intercity travel were collected to develop the models. The standard multinomial logit (MNL) regression and Bayesian multinomial logit (BMNL) regression were compared with the radial basis function (RBF) and multilayer perceptron (MLP). The results show that MLP performs b
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Thrane, Christer. "Examining tourists' long-distance transportation mode choices using a Multinomial Logit regression model." Tourism Management Perspectives 15 (July 2015): 115–21. http://dx.doi.org/10.1016/j.tmp.2014.10.004.

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Rozprawy doktorskie na temat "Multinomial logit regression model"

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Albaqshi, Amani Mohammed H. "Generalized Partial Least Squares Approach for Nominal Multinomial Logit Regression Models with a Functional Covariate." Thesis, University of Northern Colorado, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10599676.

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<p> Functional Data Analysis (FDA) has attracted substantial attention for the last two decades. Within FDA, classifying curves into two or more categories is consistently of interest to scientists, but multi-class prediction within FDA is challenged in that most classification tools have been limited to binary response applications. The functional logistic regression (FLR) model was developed to forecast a binary response variable in the functional case. In this study, a functional nominal multinomial logit regression (F-NM-LR) model was developed that shifts the FLR model into a multiple log
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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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Klockare, Mikael. "Logit, oddskvot och sannolikhet : En analys av multinomial logistisk regression." Thesis, Karlstads universitet, Avdelningen för nationalekonomi och statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-74575.

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Den här uppsatsen inleds med att studera de moment som används för multinomial logistisk regression och hur resultaten mäts. Teorin tar sin avsats i den binomiala logistiska regression, för att stegvis ta sig vidare till den multinomiala logistiska regressionen. Begreppen logit, oddskvoten och sannolikheterna förtydligas, effekterna av de oberoende variablerna diskuteras och kopplingen till vanlig linjär regression åskådliggörs. Det blir även en fördjupning av matematiken bakom den logistiska funktionen. Därefter tillämpas den multinomial logistisk regressionsanalysen med ett praktiskt exempel
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Hendricks, Nathan. "Estimating irrigation water demand with a multinomial logit selectivity model." Thesis, Manhattan, Kan. : Kansas State University, 2007. http://hdl.handle.net/2097/326.

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Nagel, Herbert, and Reinhold Hatzinger. "Diagnostics in some Discrete Choice Models." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 1990. http://epub.wu.ac.at/506/1/document.pdf.

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Discrete choice models form a class of models widely used in econometrics for modelling the individual choice from a finite set of alternatives. The most widely used model is the multinomial logit model, implicitly assuming independence of irrelevant alternatives. A generalization is the nested multinomial logit model, relaxing this strong assurnp tion. Viewing both models as nonlinear regression models a set of diagnostics is derived. This includes a hat matrix, measures of leverage, influence and residuals and an approximation to the parameters for case deletion. In an example for the multin
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POLYMEROPOULOS, ALESSIO. "Objective Variable Selection in Multinomial Logistic Regression: a Conditional Latent Approach." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2020. http://hdl.handle.net/10281/271146.

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Mixtures of g-priors are well established in linear regression models by \cite{Liang2008} and generalized linear models by \cite{Bove2011} and \cite{Li2013} for variable selection. This approach enables us to overcome the problem of specifying the dispersion parameter by imposing a hyper-prior on it. By this way we allow for our model to "learn" about the shrinkage from the data. In this work, we implement Bayesian variable selection methods based on g-priors and their mixtures in multinomial logistic regression models. More precisely, we follow two approaches: (a) the traditional implementati
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Sikder, Sujan. "An Analysis of the Travel Patterns and Preferences of the Elderly." Scholar Commons, 2010. http://scholarcommons.usf.edu/etd/3469.

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The number of elderly is increasing; to meet their transportation needs, it is important to clearly understand their travel patterns and preferences. Since travel patterns and preferences depend on socio-demographic and other factors, it is essential to identify these factors first to understand the travel behavior of the elderly. The main purpose of this thesis is to analyze the travel patterns and preferences of the elderly age 65 and above using 2009 National Household Travel Survey (NHTS) data. This thesis presents a detailed descriptive analysis of 2009 NHTS data to understand the travel
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Choi, Eugene. "Adaptive Reuse of Religious Buildings in the U.S: Determinants of Project Outcomes and the Role of Tax Credits." Cleveland State University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=csu1276711021.

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ZHANG, LEI, and XI YOU. "The Choice of STIGA Table Tennis Blades : Evidence from China." Thesis, Högskolan Dalarna, Nationalekonomi, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:du-4879.

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The purpose of this paper is to investigate how individuals with different characteristics make their choice-decisions when consuming STIGA table tennis blades, which are combinations of various attributes, such as price, control, attack, etc. It is expected that the general trend of choice behavior on this special commodity can be, at least to some extent, revealed. Data were collected using questionnaires sent to registered members of a table tennis club in China. The questionnaires included information and questions about individuals’ monthly income levels, ages, technique styles, etc. A mu
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WANG, Yanan. "Exploring online brand choice at the SKU level : the effects of internet-specific attributes." Digital Commons @ Lingnan University, 2004. https://commons.ln.edu.hk/mkt_etd/13.

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E-Commerce research shows that existing studies on online consumer choice behavior has focused on comparative studies of channel or store choice (online or offline), or online store choice (different e-tailers). Relatively less effort has been devoted to consumers’ online brand choice behavior within a single e-tailer. The goal of this research is to model online brand choice, including generating loyalty variables, setting up base model, and exploring the effects of Internet-specific attributes, i.e., order delivery, webpage display and order confirmation, on online brand choice at the SKU le
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Książki na temat "Multinomial logit regression model"

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Vanhonacker, Wilfried R. What does the multinomial logit model really measure. INSEAD, 1993.

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Stratton, Leslie S. A multinomial logit model of college stopout and dropout behavior. IZA, 2005.

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Wright, Peter. Union membership and coverage: A study using the nested multinomial logit model. University of Nottingham, 1994.

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Shi, Feng. Learn About Multinomial Logit Regression in R With Data From the General Social Survey (2016). SAGE Publications Ltd., 2019. http://dx.doi.org/10.4135/9781526473479.

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Poterba, James M. Unemployment benefits, labor market transitions, and spurious flows: A multinomial logit model with errors in classification. National Bureau of Economic Research, 1993.

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Murphy, Anthony. A simple artificial regression based LM test of asymmetry in the logit model. University College Dublin, Department of Economics, 1994.

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Otok, Bambang Widjanarko. Multinomial logit model pada faktor-faktor yang mempengaruhi tingkat hidup pekerja di sektor industri pengolahan Propinsi Jawa Tengah: Laporan penelitian. Jurusan Statistik, Fakultas Matematika dan Ilmu Pengatahuan [i.e. Pengetahuan] Alam, Lembaga Penelitian, Institut Teknologi Sepuluh Nopember, 1996.

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An Introduction to THE LOGIT MODEL FOR ECXONOMISTS. Timberlake Consultants, 2001.

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Alexopoulou, Maria. The use and evaluation of mathematical models for predicting consumer behaviour: Multinomial logit model versus conjoint analysis. Manchester Business School, MBA, 1994.

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NANDE-VÁZQUEZ, Edgard Alfredo, Teodoro REYES-FONG, and Omar Alejandro PÉREZ-CRUZ. The Generalized Least Squares Method (GMM) as a tool for causal analysis of spending, budget management and electoral results. ECORFAN, 2021. http://dx.doi.org/10.35429/b.2021.8.1.130.

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In the different fields of science, many times, there is a need to estimate the associations between variables, as an approach to understanding the interaction of one as a function of the others. It is usually done by applying restrictive models, such as analysis of variance and linear regression. This type of analysis requires that the dependent variable be continuous, have a normal and constant distribution of the mean and variance. However, when the dependent variable is discrete or categorical, the linear model is not viable. Faced with this impediment, the theory of linear models arises a
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Części książek na temat "Multinomial logit regression model"

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Frühwirth-Schnatter, Sylvia, and Rudolf Frühwirth. "Data Augmentation and MCMC for Binary and Multinomial Logit Models." In Statistical Modelling and Regression Structures. Physica-Verlag HD, 2009. http://dx.doi.org/10.1007/978-3-7908-2413-1_7.

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Tassinari, Giorgio, and Demetrio Panarello. "The effectiveness of marketing tools in a consumer goods market in Italy during the Great Recession (2010-2015)." In Proceedings e report. Firenze University Press, 2021. http://dx.doi.org/10.36253/978-88-5518-461-8.20.

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In the case of markets characterized by a stationary primary demand, the relevant dimension for measuring a company’s success is represented by market shares. The paper aims to build and comment on a model that gauges the competitive effects of marketing maneuvers on market shares, with reference to tea-based beverages in Italy in the period November 2010 – October 2015. This analysis will be instrumental in establishing the effectiveness of marketing policies based on promotions or advertising. We estimate such a model on weekly data provided by IRI Infoscan and Nielsen, involving the top fiv
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Bartels, K., Y. Boztug, and M. Müller. "Testing the Multinomial Logit Model." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-57280-7_32.

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Börsch-Supan, Axel. "The Nested Multinomial Logit Model." In Lecture Notes in Economics and Mathematical Systems. Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-45633-6_4.

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Christ, Steffen. "Multinomial Logit Model for Low-Cost Travel Choice." In Operationalizing Dynamic Pricing Models. Gabler, 2011. http://dx.doi.org/10.1007/978-3-8349-6184-6_11.

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Theil, Henri. "A Multinomial Extension of the Linear Logit Model." In Advanced Studies in Theoretical and Applied Econometrics. Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-2546-8_11.

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Anh, Tran-Thi P., Phan Cao Tho, and Fumihiko Nakamura. "Determinants of Bus Passengers’ Loyalty: A Multinomial Logit Regression Approach." In Lecture Notes in Civil Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-7160-9_161.

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Street, Deborah J., and Leonie Burgess. "Designs for Choice Experiments for the Multinomial Logit Model." In Design and Analysis of Experiments. John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118147634.ch10.

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Kasper, Daniel, Ali Ünlü, and Bernhard Gschrey. "Sensitivity Analyses for the Mixed Coefficients Multinomial Logit Model." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-01595-8_42.

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Mariel, Petr, Danny Campbell, Erlend Dancke Sandorf, Jürgen Meyerhoff, Ainhoa Vega-Bayo, and Rebecca Blevins. "Random Utility Models: Theoretical Background." In The Economics of Non-Market Goods and Resources. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-89338-4_3.

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Abstract This chapter provides an overview of the random utility maximisation (RUM) model, reviewing its assumptions and delving into its theoretical foundations. We explore the multinomial logit (MNL) model, which is widely used in DCE literature due to its many advantages. These include its robustness, ease of estimation, and straightforward interpretation, with closed-form choice probabilities that simplify calculations. We also review advanced specifications of the mixed logit model, including the random parameters logit (RP-MXL) and latent class (LC-MXL) models, and walk you through the m
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Streszczenia konferencji na temat "Multinomial logit regression model"

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Fang, Zhehao, and Siyuan Meng. "Estimating travel mode choices of residents in Stockholm region based on multinomial logit model and mixed logit model." In International Conference on Smart Transportation and City Engineering (STCE 2024), edited by Zhengang Feng and Miroslava Mikusova. SPIE, 2025. https://doi.org/10.1117/12.3060925.

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López Flores, Walter Jeremías. "Evaluation of Neural Network and Logit Models for Classification of Default in Banking Loans." In I Conferencia Internacional de Ciencia, Tecnología e Innovación. Trans Tech Publications Ltd, 2024. http://dx.doi.org/10.4028/p-dxrv7c.

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The purpose of the study was to evaluate the performance of neural networks as modern techniques to classify the risk of default against the traditional Logit statistical method, taking a Honduran bank as a case study. The data was obtained from its credit portfolio made up of 38,156 personal loans and 9 available characteristics, choosing the most representative independent variables to design a Multilayer Perceptron type base model and its Logit equivalent to which characteristics were added to analyze their impact on the classification of the dependent variable Default, leaving in the end a
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Amin, Ahmed, Khaled Hamad, and Mohsin Balwan. "Modeling Sharjah's Travel Mode Choice Using Multinomial Logit Regression Model." In 2022 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS). IEEE, 2022. http://dx.doi.org/10.1109/icetsis55481.2022.9888851.

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Sha, Zhenghui, and Jitesh H. Panchal. "Estimating the Node-Level Behaviors in Complex Networks From Structural Datasets." In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-12063.

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There is an emerging class of networks that evolve endogenously based on the local characteristics and behaviors of nodes. Examples of such networks include social, economic, and peer-to-peer communication networks. The node-level behaviors determine the overall structure and performance of these networks. This is in contrast to exogenously designed networks whose structures are directly determined by network designers. To influence the performance of endogenous networks, it is crucial to understand a) what kinds of local behaviors result in the observed network structures and b) how these loc
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Meng, Jie. "Multinomial logit PLS regression of compositional data." In 2010 Second International Conference on Communication Systems, Networks and Applications (ICCSNA). IEEE, 2010. http://dx.doi.org/10.1109/iccsna.2010.5588855.

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Li, Hua-Min, and Hai-Jun Huang. "The Multinomial Logit Model with Last Choice Feedback." In 2009 Second International Conference on Intelligent Computation Technology and Automation. IEEE, 2009. http://dx.doi.org/10.1109/icicta.2009.308.

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Lee, Dahye, Jeffery Warner, and Curtis Morgan. "Discovering Crash Severity Factors of Grade Crossing With a Machine Learning Approach." In 2019 Joint Rail Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/jrc2019-1231.

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According to the Federal Railroad Administration (FRA) Highway-Rail Grade Crossing Accident/Incident database, more than 12,000 accidents occurred between 2012 and 2017 in the United States with casualties of around 3900. Despite repeated efforts to fully understand the risk factors that contribute to highway-rail grade crossing collisions, there still remain many uncertainties. A machine learning approach is proposed in this paper to find out significant factors, along with their individual impacts of crash severities at grade crossings. One of the most efficient and accurate machine learning
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Suhua, Chen, Zhang Xiaojun, and Ding Jianming. "Feasibility on HOV lanes based on multinomial logit model." In 2010 2nd IEEE International Conference on Information and Financial Engineering (ICIFE). IEEE, 2010. http://dx.doi.org/10.1109/icife.2010.5609269.

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Wen, Chengwei, Junhong Hu, Wenjie Zhang, and Rui Tang. "Research on shared parking intention based on multinomial logit model." In ICIT 2021: IoT and Smart City. ACM, 2021. http://dx.doi.org/10.1145/3512576.3512673.

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Ou, Mingdong, Nan Li, Shenghuo Zhu, and Rong Jin. "Multinomial Logit Bandit with Linear Utility Functions." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/361.

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Multinomial logit bandit is a sequential subset selection problem which arises in many applications. In each round, the player selects a K-cardinality subset from N candidate items, and receives a reward which is governed by a multinomial logit (MNL) choice model considering both item utility and substitution property among items. The player's objective is to dynamically learn the parameters of MNL model and maximize cumulative reward over a finite horizon T. This problem faces the exploration-exploitation dilemma, and the involved combinatorial nature makes it non-trivial. In recent years, th
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Raporty organizacyjne na temat "Multinomial logit regression model"

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Zhang, Yongping, Carol Kachadoorian, Wen Cheng, and Edward Clay. Enhancing Older Adults’ Mobility in Active Living and Tiered Living Communities. Mineta Transportation Institute, 2023. http://dx.doi.org/10.31979/mti.2023.2159.

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The U.S. population is aging rapidly. As people get older, they increasingly face issues such as increased susceptibility to injuries and the need to be assisted with many day-to-day activities. Older adults have the opportunity to opt-in to live in an older adult community (OAC) based on their needs and capabilities. This study comprehensively reviews existing governing development regulations and design criteria related to the older adults’ communities, conducts surveys among people involved with some of these communities in California, and recommends improvements to community design for act
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Burda, Martin, Matthew C. Harding, and Jerry Hausman. A Bayesian mixed logit-probit model for multinomial choice. Institute for Fiscal Studies, 2008. http://dx.doi.org/10.1920/wp.cem.2008.2308.

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Nesheim, Lars, and Joel L. Horowitz. Using penalized likelihood to select parameters in a random coefficients multinomial logit model. The IFS, 2019. http://dx.doi.org/10.1920/wp.cem.2019.5019.

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Nesheim, Lars, and Joel L. Horowitz. Using penalized likelihood to select parameters in a random coefficients multinomial logit model. The IFS, 2018. http://dx.doi.org/10.1920/wp.cem.2018.2918.

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Garabato, Natalia, and Magdalena Ramada. Housing Markets in Uruguay: Determinants of Housing Demand and Its Interaction with Public Policies. Inter-American Development Bank, 2011. http://dx.doi.org/10.18235/0011348.

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This paper analyzes the determinants of housing demand for Uruguay and the extent to which housing policies have an impact on their target population. The paper first analyzes the determinants of housing demand, following an approach based on Rosen's (1974) two-step procedure consisting of fitting a hedonic price regression in 34 different geographical units (or markets) to estimate a housing demand function. The determinants of formality and ownership choices were examined using a multinomial logit framework. Determinants of these choices include both household demographic attributes and acce
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Tirapat, Sunti. An investigation of default probability in Thailand. Chulalongkorn University, 2001. https://doi.org/10.58837/chula.res.2001.21.

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Using the sample of 100 most liquid companies listed in the Stock Exchange of Thailand during 1992-1999, the default probabilities from two approaches, the logit model and the KMV model, are calculated and compared. The results from the KMV model suggest that the default probabilities of financial institutions are higher than the probabilities of industrial companies. Moreover, the results from the KMV model confirm that the average default probabilities of the financial distressed firms in the 1997 financial crisis are higher than the average default probabilities of non-distressed firms. Com
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de Luis, Mercedes, Emilio Rodríguez, and Diego Torres. Machine learning applied to active fixed-income portfolio management: a Lasso logit approach. Banco de España, 2023. http://dx.doi.org/10.53479/33560.

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The use of quantitative methods constitutes a standard component of the institutional investors’ portfolio management toolkit. In the last decade, several empirical studies have employed probabilistic or classification models to predict stock market excess returns, model bond ratings and default probabilities, as well as to forecast yield curves. To the authors’ knowledge, little research exists into their application to active fixed-income management. This paper contributes to filling this gap by comparing a machine learning algorithm, the Lasso logit regression, with a passive (buy-and-hold)
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