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Publication Date1994, Quantitative applications in the social sciences, ISBN 0803949995, Volume no. 07-101, vii, 88
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1984, Sage University papers, ISBN 0803921330, Volume 45., 95
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1999, Wiley series in probability and statistics. Texts and reference section, ISBN 9780471154105, xiii, 386
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Marketing Science, ISSN 0732-2399, 5/2010, Volume 29, Issue 3, pp. 393 - 421
The mixed or heterogeneous multinomial logit (MIXL) model has become popular in a number of fields, especially marketing, health economics, and industrial...
choice experiments | choice models | mixture models | consumer heterogeneity | Datasets | Economic models | Utility models | Cell phones | Credit cards | Modeling | Parametric models | Scale modeling | Pizzas | Marketing | Choice experiments | Consumer heterogeneity | Choice models | Mixture models | BEHAVIOR | EXPERTS | BUSINESS | INFERENCE | PANEL-DATA | PREFERENCES | COMPLEXITY | PROBIT MODEL | CONSUMER CHOICE | PARTICIPATION | choice models; mixture models; consumer heterogeneity; choice experiments | Monte Carlo method | Consumer behavior | Usage | Consumer preferences | Logits | Models | Testing
choice experiments | choice models | mixture models | consumer heterogeneity | Datasets | Economic models | Utility models | Cell phones | Credit cards | Modeling | Parametric models | Scale modeling | Pizzas | Marketing | Choice experiments | Consumer heterogeneity | Choice models | Mixture models | BEHAVIOR | EXPERTS | BUSINESS | INFERENCE | PANEL-DATA | PREFERENCES | COMPLEXITY | PROBIT MODEL | CONSUMER CHOICE | PARTICIPATION | choice models; mixture models; consumer heterogeneity; choice experiments | Monte Carlo method | Consumer behavior | Usage | Consumer preferences | Logits | Models | Testing
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Loading Rights2002, Quantitative applications in the social sciences, ISBN 0761922423, Volume 138, vi, 97
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2001, Quantitative applications in the social sciences, ISBN 0761920552, Volume 134, viii, 101
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1979, ISBN 9780839113867, vi, 82
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1992, Quantitative applications in the social sciences, ISBN 0803943776, Volume no. 07-086, vi, 87
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2006, Quantitative applications in the social sciences, ISBN 0761929894, Volume no. 146, xi, 107
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Social Networks, ISSN 0378-8733, 2007, Volume 29, Issue 2, pp. 173 - 191
This article provides an introductory summary to the formulation and application of exponential random graph models for social networks. The possible ties...
Exponential random graph models | p models | Statistical models for social networks | models | exponential random graph models | DIRECTED-GRAPHS | LOGIT-MODELS | LOGISTIC REGRESSIONS | ANTHROPOLOGY | statistical models for social networks | MARKOV GRAPHS | FAMILY | SOCIOLOGY | Social networks | Analysis | Models | Sociology
Exponential random graph models | p models | Statistical models for social networks | models | exponential random graph models | DIRECTED-GRAPHS | LOGIT-MODELS | LOGISTIC REGRESSIONS | ANTHROPOLOGY | statistical models for social networks | MARKOV GRAPHS | FAMILY | SOCIOLOGY | Social networks | Analysis | Models | Sociology
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Loading RightsSocial Networks, ISSN 0378-8733, 2009, Volume 31, Issue 1, pp. 12 - 25
Recent advances in Exponential Random Graph Models (ERGMs), or models, include new specifications that give a much better chance of model convergence for large...
MCMC MLE | Partial conditional dependence assumption | Exponential random graph ( [formula omitted]) models | Affiliation networks | Exponential random graph (p ) models | LOGIT-MODELS | Exponential random graph (p) models | LOGISTIC REGRESSIONS | SOCIAL NETWORKS | ANTHROPOLOGY | MARKOV GRAPHS | FAMILY MODELS | SOCIOLOGY | Markov processes | Analysis | Models
MCMC MLE | Partial conditional dependence assumption | Exponential random graph ( [formula omitted]) models | Affiliation networks | Exponential random graph (p ) models | LOGIT-MODELS | Exponential random graph (p) models | LOGISTIC REGRESSIONS | SOCIAL NETWORKS | ANTHROPOLOGY | MARKOV GRAPHS | FAMILY MODELS | SOCIOLOGY | Markov processes | Analysis | Models
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Loading RightsManufacturing & Service Operations Management, ISSN 1523-4614, 09/2008, Volume 10, Issue 4, pp. 566 - 589
Many theoretical models adopt a normative approach and assume that decision makers are perfect optimizers. In contrast, this paper takes a descriptive approach...
bounded rationality | logit choice | supply chain | pooling | random utility | bullwhip effect | quantal response | inventory | newsvendor | Supply chain | Logit choice | Bullwhip effect | Newsvendor | Pooling | Inventory | Random utility | Bounded rationality | Quantal response | MANAGEMENT | CONTRACTS | INFORMATION | SUPPLY CHAIN COORDINATION | JUDGMENT | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | COSTS | EQUILIBRIUM | UNCERTAINTY | bounded rationality; newsvendor; logit choice; random utility; quantal response; supply chain; bullwhip effect; inventory; pooling | Decision-making | Studies | Inventory management | Decision making models | Optimization
bounded rationality | logit choice | supply chain | pooling | random utility | bullwhip effect | quantal response | inventory | newsvendor | Supply chain | Logit choice | Bullwhip effect | Newsvendor | Pooling | Inventory | Random utility | Bounded rationality | Quantal response | MANAGEMENT | CONTRACTS | INFORMATION | SUPPLY CHAIN COORDINATION | JUDGMENT | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | COSTS | EQUILIBRIUM | UNCERTAINTY | bounded rationality; newsvendor; logit choice; random utility; quantal response; supply chain; bullwhip effect; inventory; pooling | Decision-making | Studies | Inventory management | Decision making models | Optimization
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Loading RightsTransportation Research Part B, ISSN 0191-2615, 2011, Volume 45, Issue 3, pp. 461 - 473
â–º The assumption of the Gumbel distribution in the multinomial logit model is substantially relaxed to include a large class of distributions. â–º The new...
Gumbel distribution | Discrete choice model | Semi-parametric model | Variance stabilization | Multinomial logit model | ENGINEERING, CIVIL | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | TRANSPORTATION | SPLINES | TRANSPORTATION SCIENCE & TECHNOLOGY | ECONOMICS | TRAVEL-TIME SAVINGS | Discrete choice model Gumbel distribution Multinomial logit model Semi-parametric model Variance stabilization | Analysis | Algorithms
Gumbel distribution | Discrete choice model | Semi-parametric model | Variance stabilization | Multinomial logit model | ENGINEERING, CIVIL | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | TRANSPORTATION | SPLINES | TRANSPORTATION SCIENCE & TECHNOLOGY | ECONOMICS | TRAVEL-TIME SAVINGS | Discrete choice model Gumbel distribution Multinomial logit model Semi-parametric model Variance stabilization | Analysis | Algorithms
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Loading RightsJournal of Memory and Language, ISSN 0749-596X, 2008, Volume 59, Issue 4, pp. 434 - 446
This paper identifies several serious problems with the widespread use of ANOVAs for the analysis of categorical outcome variables such as forced-choice...
Logistic regression | Mixed logit models | Categorical data analysis | Arcsine-square-root transformation | LINGUISTICS | LANGUAGE PRODUCTION | FIXED-EFFECT FALLACY | VARIANCE | PSYCHOLOGY, EXPERIMENTAL | LEAST-SQUARES | PSYCHOLOGY | language comprehension | language | mixed-effects model | logistic regression | psycholinguistics | Comparative Analysis | Geometric Concepts | Data Analysis | Priming | Statistical Analysis | School Choice | Mathematical Models | Linguistics | Statistical methods | Research | Variance analysis | Psychology
Logistic regression | Mixed logit models | Categorical data analysis | Arcsine-square-root transformation | LINGUISTICS | LANGUAGE PRODUCTION | FIXED-EFFECT FALLACY | VARIANCE | PSYCHOLOGY, EXPERIMENTAL | LEAST-SQUARES | PSYCHOLOGY | language comprehension | language | mixed-effects model | logistic regression | psycholinguistics | Comparative Analysis | Geometric Concepts | Data Analysis | Priming | Statistical Analysis | School Choice | Mathematical Models | Linguistics | Statistical methods | Research | Variance analysis | Psychology
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Loading RightsEconomics Letters, ISSN 0165-1765, 04/2012, Volume 115, Issue 1, pp. 85 - 87
In any nonlinear "difference-in-differences" model with strictly monotonic transformation function, the treatment effect is the cross difference of the...
Tobit | Identification | Potential outcomes | Logit | Probit | ECONOMICS
Tobit | Identification | Potential outcomes | Logit | Probit | ECONOMICS
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Loading RightsThe Journal of Mathematical Sociology, ISSN 0022-250X, 01/2016, Volume 40, Issue 1, pp. 7 - 20
When outcome variables are ordinal rather than continuous, the ordered logit model, aka the proportional odds model (ologit/po), is a popular analytical...
ordered logit model | partial proportional odds | proportional odds model | proportional odds assumption | Generalized ordered logit model | Ordered logit model | Proportional odds model | Partial proportional odds | Proportional odds assumption | LOGISTIC-REGRESSION | OLDER-ADULTS | MATHEMATICS, INTERDISCIPLINARY APPLICATIONS | HETEROGENEOUS CHOICE MODELS | SOCIAL SCIENCES, MATHEMATICAL METHODS | VARIABLES | PROPORTIONAL ODDS MODELS | SOCIOLOGY | Economic models | Mathematical models | Regression analysis | Logit models | Research methodology
ordered logit model | partial proportional odds | proportional odds model | proportional odds assumption | Generalized ordered logit model | Ordered logit model | Proportional odds model | Partial proportional odds | Proportional odds assumption | LOGISTIC-REGRESSION | OLDER-ADULTS | MATHEMATICS, INTERDISCIPLINARY APPLICATIONS | HETEROGENEOUS CHOICE MODELS | SOCIAL SCIENCES, MATHEMATICAL METHODS | VARIABLES | PROPORTIONAL ODDS MODELS | SOCIOLOGY | Economic models | Mathematical models | Regression analysis | Logit models | Research methodology
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Loading RightsPolitical Analysis, ISSN 1047-1987, 1/2006, Volume 14, Issue 1, pp. 63 - 82
Multiplicative interaction models are common in the quantitative political science literature. This is so for good reason. Institutional arguments frequently...
Presidential candidates | Economic models | Presidential elections | Political science | Variable coefficients | Standard error | Coefficients | Modeling | Parametric models | Political parties | POLITICAL SCIENCE | MULTIPLE-REGRESSION | INTERACTION TERMS | SYSTEMS | VARIABLES | ERROR | ELECTORAL INSTITUTIONS | DEMOCRACIES | PROBIT | LOGIT | RULES | Social interaction | Models
Presidential candidates | Economic models | Presidential elections | Political science | Variable coefficients | Standard error | Coefficients | Modeling | Parametric models | Political parties | POLITICAL SCIENCE | MULTIPLE-REGRESSION | INTERACTION TERMS | SYSTEMS | VARIABLES | ERROR | ELECTORAL INSTITUTIONS | DEMOCRACIES | PROBIT | LOGIT | RULES | Social interaction | Models
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Loading RightsSocial Networks, ISSN 0378-8733, 2007, Volume 29, Issue 2, pp. 192 - 215
This article reviews new specifications for exponential random graph models proposed by Snijders et al. [Snijders, T.A.B., Pattison, P., Robins, G.L.,...
Exponential random graph models | p models | Statistical models for social networks | exponential random graph models | LOGIT-MODELS | LOGISTIC REGRESSIONS | ANTHROPOLOGY | statistical models for social networks | MARKOV GRAPHS | FAMILY MODELS | SOCIOLOGY | Social networks | Sociology
Exponential random graph models | p models | Statistical models for social networks | exponential random graph models | LOGIT-MODELS | LOGISTIC REGRESSIONS | ANTHROPOLOGY | statistical models for social networks | MARKOV GRAPHS | FAMILY MODELS | SOCIOLOGY | Social networks | Sociology
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Loading RightsTransportation Research Part B, ISSN 0191-2615, 2011, Volume 45, Issue 7, pp. 923 - 939
â–º Proposes a new maximum approximated composite marginal likelihood (MACML) approach. â–º The approach is simulation-free and easy to implement. â–º Can be used to...
Spatial econometrics | Panel data | Discrete choice models | Multinomial probit | Mixed models | Composite marginal likelihood | TRANSPORTATION | DISCRETE RESPONSE | FORMULATION | INFERENCE | LOGIT MODEL | PAIRWISE LIKELIHOOD | ENGINEERING, CIVIL | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | SIMULATED LIKELIHOOD | SPARSE GRIDS | PREFERENCES | TRANSPORTATION SCIENCE & TECHNOLOGY | ECONOMICS | TRAVEL-TIME RELIABILITY | PROBABILITIES | Multinomial probit Mixed models Composite marginal likelihood Discrete choice models Spatial econometrics Panel data | Econometric models | Models | Social aspects | Analysis
Spatial econometrics | Panel data | Discrete choice models | Multinomial probit | Mixed models | Composite marginal likelihood | TRANSPORTATION | DISCRETE RESPONSE | FORMULATION | INFERENCE | LOGIT MODEL | PAIRWISE LIKELIHOOD | ENGINEERING, CIVIL | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | SIMULATED LIKELIHOOD | SPARSE GRIDS | PREFERENCES | TRANSPORTATION SCIENCE & TECHNOLOGY | ECONOMICS | TRAVEL-TIME RELIABILITY | PROBABILITIES | Multinomial probit Mixed models Composite marginal likelihood Discrete choice models Spatial econometrics Panel data | Econometric models | Models | Social aspects | Analysis
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Loading RightsStata Journal, ISSN 1536-867X, 2011, Volume 11, Issue 3, pp. 420 - 438