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Full Description
The complexity, diversity, and random nature of transportation problems necessitates a broad analytical toolbox. Describing tools commonly used in the field, Statistical and Econometric Methods for Transportation Data Analysis, Second Edition provides an understanding of a broad range of analytical tools required to solve transportation problems. It includes a wide breadth of examples and case studies covering applications in various aspects of transportation planning, engineering, safety, and economics.After a solid refresher on statistical fundamentals, the book focuses on continuous dependent variable models and count and discrete dependent variable models. Along with an entirely new section on other statistical methods, this edition offers a wealth of new material.New to the Second EditionA subsection on Tobit and censored regressions An explicit treatment of frequency domain time series analysis, including Fourier and wavelets analysis methodsNew chapter that presents logistic regression commonly used to model binary outcomesNew chapter on ordered probability modelsNew chapters on random-parameter models and Bayesian statistical modelingNew examples and data setsEach chapter clearly presents fundamental concepts and principles and includes numerous references for those seeking additional technical details and applications. To reinforce a practical understanding of the modeling techniques, the data sets used in the text are offered on the book's CRC Press web page. PowerPoint and Word presentations for each chapter are also available for download.
Contents
FUNDAMENTALSStatistical Inference I: Descriptive StatisticsMeasures of Relative Standing Measures of Central Tendency Measures of Variability Skewness and Kurtosis Measures of Association Properties of Estimators Methods of Displaying DataStatistical Inference II: Interval Estimation, Hypothesis Testing, and Population ComparisonsConfidence IntervalsHypothesis Testing Inferences Regarding a Single PopulationComparing Two PopulationsNonparametric MethodsCONTINUOUS DEPENDENT VARIABLE MODELSLinear RegressionAssumptions of the Linear Regression ModelRegression FundamentalsManipulating Variables in RegressionEstimate a Single Beta Parameter Estimate Beta Parameter for Ranges of a Variable Estimate a Single Beta Parameter for m - 1 of the m Levels of a Variable Checking Regression AssumptionsRegression OutliersRegression Model GOF Measures Multicollinearity in the Regression Regression Model-Building Strategies Estimating Elasticities Censored Dependent Variables-Tobit Model Box-Cox RegressionViolations of Regression Assumptions Zero Mean of the Disturbances Assumption Normality of the Disturbances Assumption Uncorrelatedness of Regressors and Disturbances AssumptionHomoscedasticity of the Disturbances Assumption No Serial Correlation in the Disturbances Assumption Model Specification ErrorsSimultaneous-Equation ModelsOverview of the Simultaneous-Equations ProblemReduced Form and the Identification ProblemSimultaneous-Equation Estimation Seemingly Unrelated Equations Applications of Simultaneous Equations to Transportation DataPanel Data AnalysisIssues in Panel Data AnalysisOne-Way Error Component Models Two-Way Error Component Models Variable-Parameter Models Additional Topics and ExtensionsBackground and Exploration in Time SeriesExploring a Time SeriesBasic Concepts: Stationarity and DependenceTime Series in RegressionForecasting in Time Series: Autoregressive Integrated Moving Average (ARIMA) Models and ExtensionsAutoregressive Integrated Moving Average Models The Box-Jenkins ApproachAutoregressive Integrated Moving Average Model ExtensionsMultivariate Models Nonlinear ModelsLatent Variable ModelsPrincipal Components Analysis Factor Analysis Structural Equation ModelingDuration ModelsHazard-Based Duration Models Characteristics of Duration Data Nonparametric Models Semiparametric Models Fully Parametric Models Comparisons of Nonparametric, Semiparametric, and Fully Parametric Models Heterogeneity State Dependence Time-Varying Covariates Discrete-Time Hazard Models Competing Risk ModelsCOUNT AND DISCRETE DEPENDENT VARIABLE MODELSCount Data ModelsPoisson Regression Model Interpretation of Variables in the Poisson Regression Model Poisson Regression Model Goodness-of-Fit Measures Truncated Poisson Regression Model Negative Binomial Regression Model Zero-Inflated Poisson and Negative Binomial Regression Models Random-Effects Count ModelsLogistic RegressionPrinciples of Logistic Regression The Logistic Regression ModelDiscrete Outcome ModelsModels of Discrete Data Binary and Multinomial Probit ModelsMultinomial Logit Model Discrete Data and Utility Theory Properties and Estimation of MNL ModelsThe Nested Logit Model (Generalized Extreme Value Models) Special Properties of Logit ModelsOrdered Probability ModelsModels for Ordered Discrete Data Ordered Probability Models with Random Effects Limitations of Ordered Probability ModelsDiscrete/Continuous ModelsOverview of the Discrete/Continuous Modeling Problem Econometric Corrections: Instrumental Variables and Expected Value Method Econometric Corrections: Selectivity-Bias Correction TermDiscrete/Continuous Model Structures Transportation Application of Discrete/Continuous Model StructuresOTHER STATISTICAL METHODSRandom-Parameter ModelsRandom-Parameters Multinomial Logit Model (Mixed Logit Model) Random-Parameter Count Models Random-Parameter Duration ModelsBayesian ModelsBayes' Theorem MCMC Sampling-Based Estimation Flexibility of Bayesian Statistical Models via MCMC Sampling-Based Estimation Convergence and Identifi ability Issues with MCMC Bayesian Models Goodness-of-Fit, Sensitivity Analysis, and Model Selection Criterion using MCMC Bayesian ModelsAppendix A: Statistical FundamentalsAppendix B: Glossary of Terms Appendix C: Statistical Tables Appendix D: Variable TransformationsReferencesIndex
