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1.

図書

図書
K.E. トレイン著 ; 山本哲三, 金沢哲雄監訳
出版情報: 東京 : 文眞堂, 1998.6  xiii, 386p ; 21cm
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2.

図書

図書
Kenneth E. Train
出版情報: New York : Cambridge University Press, 2009  x, 388 p. ; 23 cm
所蔵情報: loading…
目次情報: 続きを見る
Introduction / 1:
Motivation / 1.1:
Choice Probabilities and Integration / 1.2:
Outline of Book / 1.3:
A Couple of Notes / 1.4:
Behavioral Models / Part I:
Properties of Discrete Choice Models / 2:
Overview / 2.1:
The Choice Set / 2.2:
Derivation of Choice Probabilities / 2.3:
Specific Models / 2.4:
Identification of Choice Models / 2.5:
Aggregation / 2.6:
Forecasting / 2.7:
Recalibration of Constants / 2.8:
Logit / 3:
Choice Probabilities / 3.1:
The Scale Parameter / 3.2:
Power and Limitations of Logit / 3.3:
Nonlinear Representative Utility / 3.4:
Consumer Surplus / 3.5:
Derivatives and Elasticities / 3.6:
Estimation / 3.7:
Goodness of Fit and Hypothesis Testing / 3.8:
Case Study: Forecasting for a New Transit System / 3.9:
Derivation of Logit Probabilities / 3.10:
GEV / 4:
Nested Logit / 4.1:
Three-Level Nested Logit / 4.3:
Overlapping Nests / 4.4:
Heteroskedastic Logit / 4.5:
The GEV Family / 4.6:
Probit / 5:
Identification / 5.1:
Taste Variation / 5.3:
Substitution Patterns and Failure of IIA / 5.4:
Panel Data / 5.5:
Simulation of the Choice Probabilities / 5.6:
Mixed Logit / 6:
Random Coefficients / 6.1:
Error Components / 6.3:
Substitution Patterns / 6.4:
Approximation to Any Random Utility Model / 6.5:
Simulation / 6.6:
Case Study / 6.7:
Variations on a Theme / 7:
Stated-Preference and Revealed-Preference Data / 7.1:
Ranked Data / 7.3:
Ordered Responses / 7.4:
Contingent Valuation / 7.5:
Mixed Models / 7.6:
Dynamic Optimization / 7.7:
Numerical Maximization / Part II:
Notation / 8.1:
Algorithms / 8.3:
Convergence Criterion / 8.4:
Local versus Global Maximum / 8.5:
Variance of the Estimates / 8.6:
Information Identity / 8.7:
Drawing from Densities / 9:
Random Draws / 9.1:
Variance Reduction / 9.3:
Simulation-Assisted Estimation / 10:
Definition of Estimators / 10.1:
The Central Limit Theorem / 10.3:
Properties of Traditional Estimators / 10.4:
Properties of Simulation-Based Estimators / 10.5:
Numerical Solution / 10.6:
Individual-Level Parameters / 11:
Derivation of Conditional Distribution / 11.1:
Implications of Estimation of $$ / 11.3:
Monte Carlo Illustration / 11.4:
Average Conditional Distribution / 11.5:
Case Study: Choice of Energy Supplier / 11.6:
Discussion / 11.7:
Bayesian Procedures / 12:
Overview of Bayesian Concepts / 12.1:
Simulation of the Posterior Mean / 12.3:
Drawing from the Posterior / 12.4:
Posteriors for the Mean and Variance of a Normal Distribution / 12.5:
Hierarchical Bayes for Mixed Logit / 12.6:
Bayesian Procedures for Probit Models / 12.7:
Endogeneity / 13:
The BLP Approach / 13.1:
Supply Side / 13.3:
Control Functions / 13.4:
Maximum Likelihood Approach / 13.5:
Case Study: Consumers' Choice among New Vehicles / 13.6:
EM Algorithms / 14:
General Procedure / 14.1:
Examples of EM Algorithms / 14.3:
Case Study: Demand for Hydrogen Cars / 14.4:
Bibliography
Index
Properties
Mixed logit
Variations on a theme
Numerical maximization
Drawing from densities
Simulation-assisted estimation
Individual-level parameters
Bayesian procedures
EM algorithms
Introduction / 1:
Motivation / 1.1:
Choice Probabilities and Integration / 1.2:
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