Results 71 to 80 of about 13,900,957 (301)
Comparing discrete choice and machine learning models in predicting destination choice
European Transport Research ReviewDestination choice modeling has long been dominated by theory-based discrete choice models. Simultaneously, machine learning has demonstrated improved predictive performance to other fields of discrete choice modeling.
Ilona Rahnasto, Martijn Hollestelle
doaj +1 more source
Semiparametric Multinomial Logit Models for Analysing Consumer Choice Behaviour [PDF]
, 2006 The multinomial logit model (MNL) is one of the most frequently used statistical models in marketing applications. It allows to relate an unordered categorical response variable, for example representing the choice of a brand, to a vector of covariates ...
Steiner, Winfried J. +2 more
core +1 more source
Exploiting Edge Semantics in Job Shop Scheduling Problem With Heterogeneous Graph Transformers
Advanced Intelligent Systems, EarlyView.A heterogeneous graph transformer (HGT) is introduced for reinforcement learning‐based job shop scheduling by explicitly distinguishing precedence and machine‐contention relations through edge‐type‐specific attention. The proposed framework learns richer scheduling representations, improves decision quality over homogeneous graph models, and highlights
Bulent Soykan, Fatih Kasimoglu
wiley +1 more source
Russian Journal of Agricultural and Socio-Economic Sciences, 2021 This study used multinomial logit regression to determine the factors that drive the choice of major adaptation measures by famers in Birnin-Kudu, Jigawa State. The study used multistage sampling procedure to select 120 food crop famers in the area. Data
Mailumo S.S., Onuwa G.C., Oyewole S.
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Transportation Research Part B: MethodologicalIn this paper, we revisit McFadden (1978)'s correction factor for sampling of alternatives in multinomial logit (MNL) and mixed multinomial logit (MMNL) models. McFadden (1978) proved that consistent parameter estimates are obtained when estimating MNL models using a sampled subset of alternatives, including the chosen alternative, in combination with ...
Dekker, T., Bansal, P., Huo, J.
openaire +3 more sources
Airport and Access Mode Choice in Germany: A Generalized Neural Logit Model Approach [PDF]
, 2006 The purpose of the paper is to present a novel approach of a general airport and access mode choice model. Based on data of the German Air Traveller Survey 2003 with a sample size of about 210.000 passengers interviewed at 21 airports a three-stage ...
Gelhausen, Marc +1 more
core
Long-term absenteeism due to sickness in Sweden. How long does it take and what happens after? [PDF]
, 2007 Long-term sickness, Returns to work, Full and partial disability, Competing-risks model, Multinomial-logit model, I12, J21, J28,
Daniela Andrén +2 more
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American Journal of Agricultural Economics, EarlyView.Abstract Discrete choice experiments are increasingly being used to estimate land managers' willingness to accept participation in incentive‐based environmental programs. This is a specific application of discrete choice experiments: the estimation of willingness to accept for a private good (program participation) where respondents have to make trade ...
Anastasio J. Villanueva +2 more
wiley +1 more source
Multinomial Logit Models for the Austrian Labor Market
Austrian Journal of Statistics, 2016 In this paper we analyze the selection of industry branches by employees in the Austrian labor market. For this purpose we use the standard logit model and the heteroscedastic extreme value model.
Ivan Pryanishnikov, Katarina Zigova
doaj +1 more source
Multinomial logit bias reduction via Poisson log-linear model [PDF]
, 2010 It is shown how to obtain the bias-reducing penalized maximum likelihood estimator for the parameters of a multinomial logistic regression by using the equivalent Poisson log-linear model.
Firth, David +5 more
core +1 more source