Relevant bibliographies by topics / Integrated Choice and Latent Variable

Contents

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

Academic literature on the topic 'Integrated Choice and Latent Variable'

Author: Grafiati
Published: 6 September 2023

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Journal articles on the topic "Integrated Choice and Latent Variable"

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Chen, Jian, and Shoujie Li. "Mode Choice Model for Public Transport with Categorized Latent Variables." Mathematical Problems in Engineering 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/7861945.

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Mode choice model for public transport, which integrates structural equation model (SEM) and discrete choice model (DCM) with categorized latent variables, was presented in this paper. Apart from identifying those important latent variables that affect mode choice for public transport, the objective of this study was also to develop an improved disaggregative model that better explains travel behavior of those decision-makers in choosing public transport. After extensive observations, selective latent variable sets which consist of latent variable components were chosen together with explicit variables in formulating the utility functions. Data collected in Chengdu city, China, were used to calibrate and validate the model. Results showed that the impact of fare on mode choice of public transport escalated in the SEM-DCM integrated model compared with the traditional logit model. The goodness of fit for the integrated model with latent variable sets is 0.201 higher than that of the traditional logit model, which proves that latent variables have an obvious impact on mode choice behavior, and the SEM-DCM integrated model has higher accuracy and stronger explanatory ability. The results are especially helpful for public transport operators to achieve higher mode share split by improving the service quality of public transport in terms of providing more convenience and better service environment for public transport users.
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Mahpour, Alireza, Amirreza Mamdoohi, Taha HosseinRashidi, Basil Schmid, and Kay W. Axhausen. "Shopping destination choice in Tehran: An integrated choice and latent variable approach." Transportation Research Part F: Traffic Psychology and Behaviour 58 (October 2018): 566–80. http://dx.doi.org/10.1016/j.trf.2018.06.045.

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Politis, Ioannis, Panagiotis Papaioannou, and Socrates Basbas. "Integrated Choice and Latent Variable Models for evaluating Flexible Transport Mode choice." Research in Transportation Business & Management 3 (August 2012): 24–38. http://dx.doi.org/10.1016/j.rtbm.2012.06.007.

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Soto, Jose J., Luis Márquez, and Luis F. Macea. "Accounting for attitudes on parking choice: An integrated choice and latent variable approach." Transportation Research Part A: Policy and Practice 111 (May 2018): 65–77. http://dx.doi.org/10.1016/j.tra.2018.03.003.

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Chen, Ching-Fu, Chiang Fu, and Pei-Ya Siao. "Exploring electric moped sharing preferences with integrated choice and latent variable approach." Transportation Research Part D: Transport and Environment 121 (August 2023): 103837. http://dx.doi.org/10.1016/j.trd.2023.103837.

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Sohn, Keemin. "An Expectation-Maximization Algorithm to Estimate the Integrated Choice and Latent Variable Model." Transportation Science 51, no. 3 (August 2017): 946–67. http://dx.doi.org/10.1287/trsc.2016.0696.

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Vij, Akshay, and Joan L. Walker. "How, when and why integrated choice and latent variable models are latently useful." Transportation Research Part B: Methodological 90 (August 2016): 192–217. http://dx.doi.org/10.1016/j.trb.2016.04.021.

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Mohiuddin, Hossain, Md Musfiqur Rahman Bhuiya, Shaila Jamal, and Zhi Chen. "Exploring the Choice of Bicycling and Walking in Rajshahi, Bangladesh: An Application of Integrated Choice and Latent Variable (ICLV) Models." Sustainability 14, no. 22 (November 9, 2022): 14784. http://dx.doi.org/10.3390/su142214784.

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Bangladesh has emphasized active transportation in its transportation policies and has encouraged its population, especially the youth and students, towards bicycling. However, there is a scarcity of studies that have examined the factors important to the choice of active transportation that can be referenced to support the initiative. To address this research gap, in this study, we explore the influence of sociodemographics and latent perceptions of a built environment on the choice to walk and bicycle among students and nonstudents in Rajshahi, Bangladesh. In Rajshahi, we conducted a household survey between July and August, 2017. We used a modeling framework that integrated choice and latent variable (ICLV) models to effectively incorporate the latent perception variables in the choice model, addressing measurement error and endogeneity bias. Our models show that students are influenced by perceptions of safety from crime, while nonstudents are influenced by their perceptions of the walkability of a built environment when choosing a bicycle for commuting trips. For recreational bicycle trips, students are more concerned about the perceptions of road safety, whereas nonstudents are concerned about safety from crime. We find that road safety perception significantly and positively influences walking behavior among nonstudents. Structural equation models of the latent perception variables show that females are more likely to provide lower perceptions of neighborhood walkability, road safety, and safety from crime. Regarding active transportation decisions, overall, we find there is a difference between student and nonstudent groups and also within these groups. The findings of this study can assist in developing a sustainable active transportation system by addressing the needs of different segments of the population. In this study, we also provide recommendations regarding promoting active transportation in Rajshahi.
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Chae, Dasol, Jaeyoung Jung, and Keemin Sohn. "Facilitating an expectation-maximization (EM) algorithm to solve an integrated choice and latent variable (ICLV) model with fully correlated latent variables." Journal of Choice Modelling 26 (March 2018): 64–79. http://dx.doi.org/10.1016/j.jocm.2017.08.001.

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Yeh, Ching-Hua, Monika Hartmann, Matthew Gorton, Barbara Tocco, Virginie Amilien, and Kamilla Knutsen Steinnes. "Looking behind the choice of organic: A cross-country analysis applying Integrated Choice and Latent Variable Models." Appetite 167 (December 2021): 105591. http://dx.doi.org/10.1016/j.appet.2021.105591.

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Dissertations / Theses on the topic "Integrated Choice and Latent Variable"

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Walker, Joan Leslie. "Extended discrete choice models : integrated framework, flexible error structures, and latent variables." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/32704.

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Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2001.
Includes bibliographical references (leaves 199-209).
Discrete choice methods model a decision-maker's choice among a set of mutually exclusive and collectively exhaustive alternatives. They are used in a variety of disciplines (transportation, economics, psychology, public policy, etc.) in order to inform policy and marketing decisions and to better understand and test hypotheses of behavior. This dissertation is concerned with the enhancement of discrete choice methods. The workhorses of discrete choice are the multinomial and nested logit models. These models rely on simplistic assumptions, and there has been much debate regarding their validity. Behavioral researchers have emphasized the importance of amorphous influences on behavior such as context, knowledge, and attitudes. Cognitive scientists have uncovered anomalies that appear to violate the microeconomic underpinnings that are the basis of discrete choice analysis. To address these criticisms, researchers have for some time been working on enhancing discrete choice models. While there have been numerous advances, typically these extensions are examined and applied in isolation. In this dissertation, we present, empirically demonstrate, and test a generalized methodological framework that integrates the extensions of discrete choice. The basic technique for integrating the methods is to start with the multinomial logit formulation, and then add extensions that relax simplifying assumptions and enrich the capabilities of the basic model. The extensions include: - Specifying factor analytic (probit-like) disturbances i order to provide a flexible covariance structure, thereby relaxing the IIA condition and enabling estimation of unobserved heterogeneity through techniques such as random parameters. - Combining revealed and stated preferences in order to draw on the advantages of both types of data, thereby reducing bias and improving efficiency of the parameter estimates. - Incorporating latent variables in order to provide a richer explanation of behavior by explicitly representing the formation and effects of latent constructs such as attitudes and perceptions. - Stipulating latent classes in order to capture latent segmentation, for example. in terms of taste parameters, choice sets, and decision protocols. The guiding philosophy is that the generalized framework allows for a more realistic representation of the behavior inherent in the choice process, and consequently a better understanding of behavior, improvements in forecasts, and valuable information regarding the validity of simpler model structures. These generalized models often result in functional forms composed of complex multidimensional integrals. Therefore a key aspect of the framework is its 'logit kernel' formulation in which the disturbance of the choice model includes an additive i.i.d Gumbel term. This formulation can replicate all known error structures (as we show here) and it leads to a straightforward probability simulator (of a multinomial logit form) for use in maximum simulated likelihood estimation. The proposed framework and suggested implementation leads to a flexible, tractable, theoretically grounded, empirically verifiable. and intuitive method for incorporating and integrating complex behavioral processes in the choice model. In addition to the generalized framework, contributions are also made to two of the key methodologies hat make up the framework. First, we present new results regarding identification and normalization of he disturbance parameters of a logit kernel model. n particular, we show that identification is not always intuitive, it is not always analogous to the systematic portion. and it is not necessarily like probit. Second. we present a general framework and methodology for incorporating latent variables into choice models via the integration of choice and latent variable models and the use of psychometric data (for example. responses to attitudinal survey questions). Throughout the dissertation, empirical results are presented to highlight findings and to empirically demonstrate and test the generalized framework. The impact of the extensions cannot be known a priori. and the only way to test their value (as well as the validity of a simpler model structure) is to estimate the complex models. Sometimes the extensions result in large improvements in fit as well as in more satisfying behavioral representations. Conversely, sometimes the extensions have marginal impact. thereby showing that the more parsimonious structures are robust. All methods are often not necessary. and the generalized framework provides an approach for developing the best model specification that makes use of available data and is reflective of behavioral hypotheses.
by Joan Leslie Walker.
Ph.D.
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Bouscasse, Hélène. "Essays on travel mode choice modeling : a discrete choice approach of the interactions between economic and behavioral theories." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSE2106/document.

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Cette thèse a pour objectif d’incorporer des éléments de théories de psychologie et d’économie comportementale dans des modèles de choix discret afin d’améliorer la compréhension du choix modal réalisé à l’échelle régionale. Les estimations se basent sur une enquête de type choice experiment présentée en première partie. Une deuxième partie s’intéresse à l’incorporation de variables latentes pour expliquer le choix modal. Après une revue de littérature sur les modèles de choix hybrides, c’est-à-dire des modèles combinant modèle d’équations structurelles et modèle de choix discret, un tel modèle est estimé pour montrer comment l’hétérogénéité d’outputs économiques (ici, la valeur du temps) peut être expliquée à l’aide de variables latentes (ici, le confort perçu dans les transports en commun) et de variables observables (ici, la garantie d’une place assise). La simulation de scénarios montre cependant que le gain économique (diminution de la valeur du temps) est plus élevé lorsque les politiques agissent sur des dimensions palpables que sur des dimensions latentes. S’appuyant sur un modèle de médiation, l’estimation d’un modèle d’équations structurelles montre par ailleurs que l’effet de la conscience environnementale sur les habitudes de choix modal est partiellement médié par l’utilité indirecte retirée de l’usage des transports en commun. Une troisième partie s’intéresse à deux formalisations de l’utilité issues de l’économie comportementale : 1) l’utilité dépendante au rang en situation de risque et 2) l’utilité dépendante à la référence. Dans un premier temps, un modèle d’utilité dépendante au rang est inséré dans des modèles de choix discret et, en particulier, un modèle à classes latentes, afin d’analyser l’hétérogénéité intra- et inter-individuelle lorsque le temps de déplacement n’est pas fiable. La probabilité de survenue d’un retard est sur-évaluée pour les déplacements en train et sous-évaluée pour les déplacements en voiture, en particulier pour les automobilistes, les usagers du train prenant d’avantage en compte l’espérance du temps de déplacement. Dans les modèles prenant en compte l’aversion au risque, les fonctions d’utilité sont convexes, ce qui implique une décroissance,de la valeur du temps. Dans un deuxième temps, une nouvelle famille de modèles de choix discret généralisant le modèle logit multinomial, les modèles de référence, est estimée. Sur mes données, ces modèles permettent une meilleure sélection des variables explicatives que le logit multinomial et l’estimation d’outputs économiques plus robustes, notamment en cas de forte hétérogénéité inobservée. La traduction économique des modèles de référence montre que les meilleurs modèles empiriques sont également les plus compatibles avec le modèle de dépendance à la référence de Tversky et Kahneman
The objective of this thesis is to incorporate aspects of psychology and behavioral economics theories in discrete choice models to promote a better understanding of mode choice at regional level. Part II examines the inclusion of latent variables to explain mode choice. A literature review of integrated choice and latent variable models – that is, models combining a structural equation model and a discrete choice model – is followed by the estimation of an integrated choice and latent variable model to show how the heterogeneity of economic outputs (here, value of time) can be explained with latent variables (here, perceived comfort in public transport) and observable variables (here, the guarantee of a seat). The simulation of scenarios shows, however, that the economic gain (decrease in value of time) is higher when policies address tangible factors than when they address latent factors. On the basis of a mediation model, the estimation of a structural equation model furthermore implies that the influence of environmental concern on mode choice habits is partially mediated by the indirect utility derived frompublic transport use. Part III examines two utility formulations taken from behavioral economics: 1) rankdependent utility to model risky choices, and 2) reference-dependent utility. Firstly, a rank-dependent utility model is included in discrete choice models and, in particular, a latent-class model, in order to analyze intra- and inter-individual heterogeneity when the travel time is subject to variability. The results show that the probability of a delay is over-estimated for train travel and under-estimated for car travel, especially for car users, as train users are more likely to take into account the expected travel time. In the models that account for risk aversion, the utility functions are convex, which implies a decrease in value of time. Secondly, a new family of discrete choice models generalizing the multinomial logit model, the reference models, is estimated. On my data, these models allow for a better selection of explanatory variables than the multinomial logit model and a more robust estimation of economic outputs, particularly in cases of high unobserved heterogeneity. The economic formulation of reference models shows thatthe best empirical models are also more compatible with Tversky et Kahneman’s reference-dependent model
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Lawson, Jordan L. "Strengthening Causal Inferences: Examining Instrument-Free Approaches to Addressing Endogeneity Bias in the Evaluation of an Integrated Student Support Program." Thesis, Boston College, 2019. http://hdl.handle.net/2345/bc-ir:108595.

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Thesis advisor: Laura M. O'Dwyer
Education researchers are frequently interested in examining the causal impact of academic services and interventions; however, it is often not feasible to randomly assign study elements to treatment conditions in the field of education (Adelson, 2013). When assignment to treatment conditions is non-random, the omission of any variables relevant to treatment selection creates a correlation between the treatment variable and the error in regression models. This is termed endogeneity (Ebbes, 2004). In the presence of endogeneity, treatment effect estimates from traditionally used regression approaches may be biased. The purpose of this study was to investigate the causal impact of an integrated student support model, namely City Connects, on student academic achievement. Given that students are not randomly assigned to the City Connects intervention, endogeneity bias may be present. To address this issue, two novel and underused statistical approaches were used with school admissions lottery data, namely Gaussian copula regression developed by Park and Gupta (2012), and Latent Instrumental Variable (LIV) regression developed by Peter Ebbes (2004). The use of real-world school admissions lottery data allowed the first-ever comparison of the two proposed methods with Instrumental Variable (IV) regression under a large-scale randomized control (RCT) trial. Additionally, the researcher used simulation data to investigate both the performance and boundaries of the two proposed methods compared with that of OLS and IV regression. Simulation study findings suggest that both Gaussian copula and LIV regression are useful approaches for addressing endogeneity bias across a range of research conditions. Furthermore, simulation findings suggest that the two proposed methods have important differences in their set of identifying assumptions, and that some assumptions are more crucial than others. Results from the application of the Gaussian copula and LIV regression in the City Connects school lottery admissions study demonstrated that receiving the City Connects model of integrated student support during elementary school has a positive impact on mathematics achievement. Such findings underscore the importance of addressing out-of-school barriers to learning
Thesis (PhD) — Boston College, 2019
Submitted to: Boston College. Lynch School of Education
Discipline: Educational Research, Measurement and Evaluation
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Chaudhary, Ankita. "Impact of range anxiety on driver route choices using a panel-integrated choice latent variable model." Thesis, 2014. http://hdl.handle.net/2152/28254.

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There has been a significant increase in private vehicle ownership in the last decade leading to substantial increase in air pollution, depleting fuel reserves, etc. One of the alternatives known as battery operated electric vehicles (BEVs) has the potential to reduce carbon footprints due to lesser or no emissions and thus the focus on shifting people from gasoline operated vehicles (GVs) to BEVs has increased considerably recently. However, BEVs have a limited ‘range’ and takes considerable time to completely recharge its battery. In addition, charging stations are not as pervasive as gasoline stations. As a result a new fear of getting stranded is observed in BEV drivers, known as range anxiety. Range anxiety has the potential to substantially affect the route choice of a BEV user. It has also been a major cause of lower market shares of BEVs. Range anxiety is a latent feeling which cannot be measured directly. It is not homogenous either and varies among different socio-economic groups. Thus, a better understanding of BEV users’ behavior may shed light on some potential solutions that can then be used to improve their market shares and help in developing new network models which can realistically capture effects of varying EV adoptions. Thus, in this study, we analyze the factors that may impact BEV users’ range anxiety in addition to their route choice behavior using the integrated choice latent variable model (ICLV) proposed by Bhat and Dubey (2014). Our results indicate that an individual’s range anxiety is significantly affected by their age, gender, income, awareness of charging stations, BEV ownership and other category vehicle ownership. Further, it also highlights the importance of including disutility caused by distance while considering network flow models with combined GV and BEV assignment. Finally, a more concentrated effort can be directed towards increasing the awareness of charging station locations in the neighborhood to help reduce the psychological barrier associated with range anxiety. Overcoming this barrier may help increase consumer confidence, resulting in increased BEV adoption and ultimately will lead towards a potentially pollution-free environment.
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Biswas, Mehek. "An Integrated Choice and Latent Variable Framework to Incorporate the Influence of Travel Time Variability on Truck Route Choice." Thesis, 2018. https://etd.iisc.ac.in/handle/2005/4735.

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Route choice models (or path choice models) are useful for quantifying travellers’ preferences for or sensitivity to route attributes, predicting network-level traffic flows, examining the influence of information provided to travellers, and studying travellers’ adaptation to uncertainty in travel conditions. Among the various factors influencing route choice, variability in travel conditions is an influential one. Day-to-day and within-day variations in travel conditions influence route choice decisions in many geographical contexts. Empirical studies on values of time and reliability have concluded that travellers, besides being interested in minimizing their travel times, also wish to minimize their travel time variability. The influence of travel time variability on route choice becomes more important in the context of freight transportation and logistics where delays due to uncertainty translate to large financial losses. Therefore, it is useful to quantify variability in travel conditions and to understand the influence of such variability on freight route choice decisions. This thesis proposes an Integrated Choice and Latent Variable (ICLV) modelling framework that allows simultaneous estimation of route-level travel time variability and incorporation of the influence of such variability on travel route choice of freight-trucks. The proposed framework considers the travel time on a route as a latent (unobserved) variable and uses GPS data measurements of route-level travel time to identify the parameters of its statistical distribution. Since such measurements are not always available for all routes, the latent variable component of the ICLV framework helps impute or inform the travel time distribution for routes without travel time measurements. In this regard, simultaneous estimation of the measurement and choice components of the proposed model allows the use of partial measurement data for estimation of travel time variability as well as incorporation of the influence of travel time variability on route choice. Further, route-level travel time variability is viewed as a result of variability in travel conditions (e.g., variability of travel speeds on links, etc.) and is captured through random coefficients on the route attributes specified in the latent variable model. The proposed model is applied to an empirical data set on truck route choice using truck-GPS data collected in the Tampa Bay region of Florida, USA. The empirical parameter estimates suggest that the variability of travel time on a route depends on the network structure along the route, such as the lengths of different roadway types, largely due to differences in variability of travel speeds among different types of roadways. The empirical findings indicate a superior statistical model fit of the proposed ICLV model than the traditional choice models that do not consider the influence of travel time variability on route choice. Although the ICLV model in this study was applied to the empirical context of freight-truck route choice, the proposed framework is applicable to accommodate the influence of variability in travel conditions on other travel choices such as transit route choice and travel mode choice; thanks to the increasing ubiquity of passively collected data on travel time (such as GPS data).
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Lai, Hzu-hao, and 賴思豪. "Modeling the Choice Behaviors of Freeway Electronic Toll Collection with an Integrated Choice and Latent Variables Approach." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/34846367174406603624.

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Wan-PeiHu and 胡琬珮. "Latent Variable Choice Models Considering Heterogeneity, Correlation, and Endogeneity." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/05768734120036738214.

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碩士
國立成功大學
交通管理學系碩博士班
96
In the past, the way that the discrete choice models treated the unobservable variables such as perceived and attitude variables caused some problems. For example, the estimated parameters were not consistent and efficient, the index could not be used directly to affect the policies, and the index may not be suitable in the prediction, and the unobservable variables are unchangeable as individuals vary. It results in some disadvantages for policies analysis and applications. Now the most widely used models is Maximum Simulated Likelihood to overcome the model with multi dimension parameter integration problems. There exists unstable situation in parameter calibrating as the number of simulated draws is not enough. The earlier reference seldom explored the influence of the model calibrating by simulating the number of draws. The number of draws is quite important. The purpose of this research is to use the simultaneous equations concepts for integrating latent variables and individual choice models, and decide the best number of the random draws. The parameters estimation will be more consistent, efficient, and stable. In addition, this study also discusses the heterogeneity, correlation, and hierarchy cause-and-effect of service quality perceived variables to increase the explanatory power in the models and to know the travelers’ riding behaviors. It will be implementing to policy analysis, to understand every policy variables on direct and indirect effect of choice behaviors, and to solve the problem which came about in the previous researches. The scope of this research was the intercity bus travelers of Taipei-Kaohsiung route in Taiwan. For the part of dealing with the latent variable choice models, the evidence shows that as the service satisfaction factors add in the model, the explanatory power will be improved. It also presented the methodology is efficient. For the part of the number of random draws, simulating the log-likelihood function of different latent variable choice models will boost as the simulating number increase. While the Halton random number reached to 4000, the improvement of the simulation to simulating the log-likelihood function was getting smooth. So this research picks 4,000 as the number of random draws. In this paper, if the latent variables are assumed as exogenous variables, the I II - - integrating latent variables heterogeneity and correlation of the choice model has better explanatory power. It presents that there are individual heterogeneity and correlation among the three service quality variables-convenience, commoditization and entertainment, the passengers’ interaction. In addition, there is positive influence on the travelers’ choices to the intercity bus companies by these three service quality satisfaction variables. The convenience and commoditization and entertainment satisfaction of the travelers to the bus companies will influence positively on travelers’ choice behaviors, and the convenience satisfaction is stronger than commoditization and entertainment satisfaction. Moreover, the travelers’ interaction satisfaction influence indirectly on the travelers’ choice behaviors to the bus companies through the convenience and commoditization and entertainment satisfaction of the travelers.
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Yun-JuLin and 林韻如. "Consumer Preferences for Purchasing Electric Vehicles-An Application of Latent Variable Choice Model." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/93289658802899284704.

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Drill, Esther. "Statistical Methods for Integrated Cancer Genomic Data Using a Joint Latent Variable Model." Thesis, 2018. https://doi.org/10.7916/D85M7P7V.

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Inspired by the TCGA (The Cancer Genome Atlas), we explore multimodal genomic datasets with integrative methods using a joint latent variable approach. We use iCluster+, an existing clustering method for integrative data, to identify potential subtypes within TCGA sarcoma and mesothelioma tumors, and across a large cohort of 33 dierent TCGA cancer datasets. For classication, motivated to improve the prediction of platinum resistance in high grade serous ovarian cancer (HGSOC) treatment, we propose novel integrative methods, iClassify to perform classication using a joint latent variable model. iClassify provides eective data integration and classication while handling heterogeneous data types, while providing a natural framework to incorporate covariate risk factors and examine genomic driver by covariate risk factor interaction. Feature selection is performed through a thresholding parameter that combines both latent variable and feature coecients. We demonstrate increased accuracy in classication over methods that assume homogeneous data type, such as linear discriminant analysis and penalized logistic regression, and improved feature selection. We apply iClassify to a TCGA cohort of HGSOC patients with three types of genomic data and platinum response data. This methodology has broad applications beyond predicting treatment outcomes and disease progression in cancer, including predicting prognosis and diagnosis in other diseases with major public health implications.
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Books on the topic "Integrated Choice and Latent Variable"

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Statistical Methods for Integrated Cancer Genomic Data Using a Joint Latent Variable Model. [New York, N.Y.?]: [publisher not identified], 2018.

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Book chapters on the topic "Integrated Choice and Latent Variable"

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Levin-Schwartz, Yuri, Vince D. Calhoun, and Tülay Adalı. "Multivariate Fusion of EEG and Functional MRI Data Using ICA: Algorithm Choice and Performance Analysis." In Latent Variable Analysis and Signal Separation, 489–96. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22482-4_57.

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Hernandez-Garcia, Miguel R., and Sami F. Masri. "Multivariate Statistical Analysis for Detection and Identification of Faulty Sensors Using Latent Variable Methods." In Emboding Intelligence in Structures and Integrated Systems, 501–7. Stafa: Trans Tech Publications Ltd., 2008. http://dx.doi.org/10.4028/3-908158-13-3.501.

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Prato, Carlo G. "Data Analysis: Integrated Choice and Latent Variable Models." In International Encyclopedia of Transportation, 102–6. Elsevier, 2021. http://dx.doi.org/10.1016/b978-0-08-102671-7.10667-0.

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Ilahi, Anugrah, Prawira F. Belgiawan, and Kay W. Axhausen. "Influence of pricing on mode choice decision integrated with latent variable." In Mapping the Travel Behavior Genome, 125–43. Elsevier, 2020. http://dx.doi.org/10.1016/b978-0-12-817340-4.00008-5.

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Kitrinou, Eleni, Amalia Polydoropoulou, and Denis Bolduc. "Development of Integrated Choice and Latent Variable (ICLV) Models for the Residential Relocation Decision in Island Areas." In Choice Modelling: The State-of-the-art and The State-of-practice, 593–618. Emerald Group Publishing Limited, 2010. http://dx.doi.org/10.1108/9781849507738-027.

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Tsirimpa, Athena, and Amalia Polydoropoulou. "The Impact of Traffic Information Acquisition on the Traffic Conditions of the Athens Greater Area." In Transportation Systems and Engineering, 174–91. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-8473-7.ch009.

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The main objective of this article is to gain fundamental understanding on the effect of real time information acquisition, on the traffic conditions of the Athens greater area. Activity scheduling is a dynamic process, where individuals often need to modify their schedule, as a result of new insights. Research so far hasn't analyzed the effect of traffic information acquisition, in activity scheduling, although several studies have been conducted to capture the factors that influence the rescheduling of activities. An integrated latent variable model has been estimated, that predicts the probability of rescheduling activities as a function of flexibility, mode choice constraints and travel information. The analysis of the results indicates that one of the biggest impacts of traffic information acquisition is reflected in the rescheduling of activities. Therefore, traffic information not only can significantly improve the travel experience of individuals but may directly affect the performance of the transportation system.
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Ben-Akiva, Moshe, Joan Walker, Adriana T. Bernardino, Dinesh A. Gopinath, Taka Morikawa, and Amalia Polydoropoulou. "Integration of Choice and Latent Variable Models." In In Perpetual Motion, 431–70. Elsevier, 2002. http://dx.doi.org/10.1016/b978-008044044-6/50022-x.

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Guevara, Cristian Angelo, and Moshe Ben-Akiva. "Addressing Endogeneity in Discrete Choice Models: Assessing Control-Function and Latent-Variable Methods." In Choice Modelling: The State-of-the-art and The State-of-practice, 353–70. Emerald Group Publishing Limited, 2010. http://dx.doi.org/10.1108/9781849507738-016.

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Emmerink, Richard H. M., Peter Nijkamp, Piet Rietveld, and Jos N. Van Ommeren. "Variable Message Signs and Radio Traffic Information: An Integrated Empirical Analysis of Drivers’ Route Choice Behaviour." In Location, Travel and Information Technology, 343–61. Edward Elgar Publishing, 2004. http://dx.doi.org/10.4337/9781035304929.00026.

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Nounou, Mohamed N., Hazem N. Nounou, and Muddu Madakyaru. "Multiscale Filtering and Applications to Chemical and Biological Systems." In Handbook of Research on Novel Soft Computing Intelligent Algorithms, 749–86. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-4450-2.ch025.

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Measured process data are a valuable source of information about the processes they are collected from. Unfortunately, measurements are usually contaminated with errors that mask the important features in the data and degrade the quality of any related operation. Wavelet-based multiscale filtering is known to provide effective noise-feature separation. Here, the effectiveness of multiscale filtering over conventional low pass filters is illustrated though their application to chemical and biological systems. For biological systems, various online and batch multiscale filtering techniques are used to enhance the quality of metabolic and copy number data. Dynamic metabolic data are usually used to develop genetic regulatory network models that can describe the interactions among different genes inside the cell in order to design intervention techniques to cure/manage certain diseases. Copy number data, however, are usually used in the diagnosis of diseases by determining the locations and extent of variations in DNA sequences. Two case studies are presented, one involving simulated metabolic data and the other using real copy number data. For chemical processes it is shown that multiscale filtering can greatly enhance the prediction accuracy of inferential models, which are commonly used to estimate key process variables that are hard to measure. In this chapter, we present a multiscale inferential modeling technique that integrates the advantages of latent variable regression methods with the advantages of multiscale filtering, and is called Integrated Multiscale Latent Variable Regression (IMSLVR). IMSLVR performance is illustrated via a case study using synthetic data and another using simulated distillation column data.
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Conference papers on the topic "Integrated Choice and Latent Variable"

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Wassenaar, Henk Jan, Wei Chen, Jie Cheng, and Agus Sudjianto. "An Integrated Latent Variable Choice Modeling Approach for Enhancing Product Demand Modeling." In ASME 2004 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASME, 2004. http://dx.doi.org/10.1115/detc2004-57487.

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Ghosh, Dipanjan D., Andrew Olewnik, and Kemper E. Lewis. "An Integrated Framework for Predicting Consumer Choice Through Modeling of Preference and Product Use Data." In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-68010.

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A critical task in product design is mapping information from consumer space to design space. Currently, this process is largely dependent on the designer to identify and map how psychological and consumer level factors relate to engineered product attributes. In this way, current methodologies lack provision to test a designer’s cognitive reasoning, which could introduce bias while mapping from consumer to design space. Cyber-Empathic Design is a novel framework where user-product interaction data is acquired using embedded sensors. To understand consumer perceptions about a particular product, a network of latent psychological constructs is used to form a causal model allowing designers to better understand user preferences. In this work, we extend this framework by integrating choice-based preference modeling to develop a Discrete Choice Analysis integrated Cyber-Empathic design framework (DCA-CED). We model user preferences and ultimately consumer choice by considering perceptions estimated by psychological latent variables and user-product interaction data. To demonstrate the effectiveness of the framework, a case study using a sensor integrated shoe design is presented where data to represent user demographics, sensor readings, and product choice is simulated. Using the DCA-CED method, the model parameters are recovered and compared with the original parameter values in the simulator. In addition, the ability of the framework to predict choice based on user product-interaction data is tested. The results show that the analytical method effectively captures the underlying data generation process thereby validating the proposed framework and the analytical method.
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Yang, Chen, Wei Wang, Zhibin Li, and Jian Lu. "Travel Mode Choice Based on Latent Variable Enriched Discrete Choice Model." In Second International Conference on Transportation Engineering. Reston, VA: American Society of Civil Engineers, 2009. http://dx.doi.org/10.1061/41039(345)720.

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Salah, Aghiles, and Hady W. Lauw. "A Bayesian Latent Variable Model of User Preferences with Item Context." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/370.

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Personalized recommendation has proven to be very promising in modeling the preference of users over items. However, most existing work in this context focuses primarily on modeling user-item interactions, which tend to be very sparse. We propose to further leverage the item-item relationships that may reflect various aspects of items that guide users' choices. Intuitively, items that occur within the same "context" (e.g., browsed in the same session, purchased in the same basket) are likely related in some latent aspect. Therefore, accounting for the item's context would complement the sparse user-item interactions by extending a user's preference to other items of similar aspects. To realize this intuition, we develop Collaborative Context Poisson Factorization (C2PF), a new Bayesian latent variable model that seamlessly integrates contextual relationships among items into a personalized recommendation approach. We further derive a scalable variational inference algorithm to fit C2PF to preference data. Empirical results on real-world datasets show evident performance improvements over strong factorization models.
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Zhang, Rong, Jiaqi Shen, and Hao Liu. "Quantitative Analysis of Latent Variable and Its Integration with Freight Mode Choice Behavior Model." In 22nd COTA International Conference of Transportation Professionals. Reston, VA: American Society of Civil Engineers, 2022. http://dx.doi.org/10.1061/9780784484265.257.

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Madakyaru, Muddu, Mohamed N. Nounou, and Hazem N. Nounou. "Enhanced modeling of distillation columns using integrated multiscale latent variable regression." In 2013 IEEE Symposium on Computational Intelligence in Control and Automation (CICA). IEEE, 2013. http://dx.doi.org/10.1109/cica.2013.6611666.

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Li, Xiufeng, Xiang Liu, Ning Wang, Decun Dong, and Wenjian Zhang. "Investigating the Key Factors Influencing Travelers’ Carsharing Choice – An Inclusion of Latent Variable Nested Logit Model." In 2019 IEEE Intelligent Transportation Systems Conference - ITSC. IEEE, 2019. http://dx.doi.org/10.1109/itsc.2019.8916949.

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Luo, Yin-Jyun, Sebastian Ewert, and Simon Dixon. "Towards Robust Unsupervised Disentanglement of Sequential Data — A Case Study Using Music Audio." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/458.

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Disentangled sequential autoencoders (DSAEs) represent a class of probabilistic graphical models that describes an observed sequence with dynamic latent variables and a static latent variable. The former encode information at a frame rate identical to the observation, while the latter globally governs the entire sequence. This introduces an inductive bias and facilitates unsupervised disentanglement of the underlying local and global factors. In this paper, we show that the vanilla DSAE suffers from being sensitive to the choice of model architecture and capacity of the dynamic latent variables, and is prone to collapse the static latent variable. As a countermeasure, we propose TS-DSAE, a two-stage training framework that first learns sequence-level prior distributions, which are subsequently employed to regularise the model and facilitate auxiliary objectives to promote disentanglement. The proposed framework is fully unsupervised and robust against the global factor collapse problem across a wide range of model configurations. It also avoids typical solutions such as adversarial training which usually involves laborious parameter tuning, and domain-specific data augmentation. We conduct quantitative and qualitative evaluations to demonstrate its robustness in terms of disentanglement on both artificial and real-world music audio datasets.
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Yannou, Bernard, Jiliang Wang, Ndrianarilala Rianantsoa, Chris Hoyle, Mark Drayer, Wei Chen, Fabrice Alizon, and Jean-Pierre Mathieu. "Usage Coverage Model for Choice Modeling: Principles." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-87534.

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Accurately capturing the future demand for a given product is a hard task in today’s new product development initiatives. As customers become more market-savvy and markets continue fragment, current demand models could greatly benefit from exploiting the rich contextual information that exists in customers’ product usage. As such, we propose a Usage Coverage Model (UCM) as a more thorough means to quantify and capture customer demand by utilizing factors of usage context in order to inform an integrated engineering design and choice modeling approach. We start by presenting the principles of the UCM model: terms, definitions, variable classes and relation classes so as to obtain a common usage language. The usage model exhibits the ability to differentiate between individuals’ product performance experiences. With Discrete Choice Analysis, individuals’ performance with a given product is compared against that of competitive products, capturing individual customers’ choice behavior and thereby creating an effective model of product demand. As a demonstration of our methods, we apply our model in a case study regarding the general task of cutting a wood board with a jigsaw tool. We conclude by presenting the scope of future work for the case study and the contribution of the entire current and future work to the field as a whole.
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Roy, Rajkumar, Ian C. Parmee, and Graham Purchase. "Qualitative Evaluation of Engineering Designs Using Fuzzy Logic." In ASME 1996 Design Engineering Technical Conferences and Computers in Engineering Conference. American Society of Mechanical Engineers, 1996. http://dx.doi.org/10.1115/96-detc/dac-1449.

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Abstract The paper describes a Qualitative Evaluation System developed using a fuzzy expert system. The evaluation system gives a qualitative rating to design solutions by considering manufacturability aspects, choice of materials and some special preferences. The information is used in decision support for engineering design. The system is an integrated part of a decision support tool for engineering design called the ‘Adaptive Search Manager’ (ASM). ASM uses an adaptive search technique to identify multiple design solutions for a 12 dimensional Turbine Blade Cooling System design problem. Thus the task has been to develop a fuzzy expert system that can qualitatively evaluate any design solution from a design space using a realistically small number of fuzzy rules. The developed system utilises a knowledge separation and then a knowledge integration technique. The design knowledge is first separated into three categories: inter variable knowledge, intra variable knowledge and heuristics. Inter variable knowledge and intra variable knowledge are integrated using a concept of “compromise”. The qualitative evaluation system can evaluate any design solution within the 12 dimensional design space, but uses only 44 fuzzy rules and one function that implements the inter variable knowledge.
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