Statistical and econometric methods for transportation data analysis:
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton, Fla. [u.a.]
CRC Press
2011
|
| Ausgabe: | 2. ed. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XVII, 526 S. graph. Darst. |
| ISBN: | 9781420082852 142008285X |
Internformat
MARC
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| 245 | 1 | 0 | |a Statistical and econometric methods for transportation data analysis |c Simon P. Washington ; Matthew G. Karlaftis ; Fred L. Mannering |
| 250 | |a 2. ed. | ||
| 264 | 1 | |a Boca Raton, Fla. [u.a.] |b CRC Press |c 2011 | |
| 300 | |a XVII, 526 S. |b graph. Darst. | ||
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Datensatz im Suchindex
| _version_ | 1804145655832117248 |
|---|---|
| adam_text | Titel: Statistical and econometric methods for transportation data analysis
Autor: Washington, Simon
Jahr: 2011
Contents
Preface.....................................................................................................................xv
Part I Fundamentals
1. Statistical Inference I: Descriptive Statistics............................................3
1.1 Measures of Relative Standing............................................................3
1.2 Measures of Central Tendency............................................................4
1.3 Measures of Variability........................................................................5
1.4 Skewness and Kurtosis........................................................................9
1.5 Measures of Association....................................................................11
1.6 Properties of Estimators.....................................................................14
1.6.1 Unbiasedness..........................................................................14
1.6.2 Efficiency.................................................................................15
1.6.3 Consistency.............................................................................16
1.6.4 Sufficiency...............................................................................16
1.7 Methods of Displaying Data.............................................................17
1.7.1 Histograms.............................................................................17
1.7.2 Ogives......................................................................................18
1.7.3 Box Plots..................................................................................19
1.7.4 Scatter Diagrams....................................................................19
1.7.5 Bar and Line Charts...............................................................20
2. Statistical Inference II: Interval Estimation, Hypothesis Testing
and Population Comparisons.....................................................................25
2.1 Confidence Intervals...........................................................................25
2.1.1 Confidence Interval for /u with Known a1.........................26
2.1.2 Confidence Interval for the Mean with Unknown
Variance...................................................................................28
2.1.3 Confidence Interval for a Population Proportion..............28
2.1.4 Confidence Interval for the Population Variance..............29
2.2 Hypothesis Testing.............................................................................30
2.2.1 Mechanics of Hypothesis Testing........................................31
2.2.2 Formulating One- and Two-Tailed Hypothesis Tests.....33
2.2.3 The p-Value of a Hypothesis Test........................................36
2.3 Inferences Regarding a Single Population......................................36
2.3.1 Testing the Population Mean with Unknown
Variance...................................................................................37
2.3.2 Testing the Population Variance..........................................38
2.3.3 Testing for a Population Proportion....................................38
2.4 Comparing Two Populations.............................................................39
2.4.1 Testing Differences between Two Means:
Independent Samples............................................................39
2.4.2 Testing Differences between Two Means:
Paired Observations..............................................................42
2.4.3 Testing Differences between Two Population
Proportions.............................................................................43
2.4.4 Testing the Equality of Two Population Variances...........45
2.5 Nonparametric Methods....................................................................46
2.5.1 Sign Test..................................................................................47
2.5.2 Median Test.............................................................................52
2.5.3 Mann-Whitney LI Test..........................................................52
2.5.4 Wilcoxon Signed-Rank Test for Matched Pairs.................55
2.5.5 Kruskal-Wallis Test...............................................................56
2.5.6 Chi-Square Goodness-of-Fit Test.........................................58
Part II Continuous Dependent Variable Models
3. Linear Regression.........................................................................................63
3.1 Assumptions of the Linear Regression Model...............................63
3.1.1 Continuous Dependent Variable Y......................................64
3.1.2 Linear-in-Parameters Relationship between Y and X......64
3.1.3 Observations Independently and Randomly
Sampled...................................................................................65
3.1.4 Uncertain Relationship between Variables........................65
3.1.5 Disturbance Term Independent of X and Expected
Value Zero...............................................................................65
3.1.6 Disturbance Terms Not Autocorrelated.............................66
3.1.7 Regressors and Disturbances Uncorrelated.......................66
3.1.8 Disturbances Approximately Normally Distributed.......66
3.1.9 Summary.................................................................................67
3.2 Regression Fundamentals..................................................................67
3.2.1 Least Squares Estimation......................................................69
3.2.2 Maximum Likelihood Estimation.......................................73
3.2.3 Properties of OLS and MLE Estimators..............................74
3.2.4 Inference in Regression Analysis........................................75
3.3 Manipulating Variables in Regression.............................................79
3.3.1 Standardized Regression Models........................................79
3.3.2 Transformations.....................................................................80
3.3.3 Indicator Variables.................................................................82
3.4 Estimate a Single Beta Parameter.....................................................83
3.5 Estimate Beta Parameter for Ranges of a Variable.........................83
3.6 Estimate a Single Beta Parameter for m -1 of the m Levels of
a Variable..............................................................................................84
3.6.1 Interactions in Regression Models......................................84
3.7 Checking Regression Assumptions.................................................87
3.7.1 Linearity..................................................................................88
3.7.2 Homoscedastic Disturbances...............................................90
3.7.3 Uncorrelated Disturbances...................................................93
3.7.4 Exogenous Independent Variables......................................93
3.7.5 Normally Distributed Disturbances...................................95
3.8 Regression Outliers.............................................................................98
3.8.1 The Hat Matrix for Identifying Outlying
Observations...........................................................................99
3.8.2 Standard Measures for Quantifying Outlier
Influence................................................................................101
3.8.3 Removing Influential Data Points from the
Regression.............................................................................101
3.9 Regression Model GOF Measures..................................................106
3.10 Multicollinearity in the Regression................................................110
3.11 Regression Model-Building Strategies...........................................112
3.11.1 Stepwise Regression............................................................112
3.11.2 Best Subsets Regression......................................................113
3.11.3 Iteratively Specified Tree-Based Regression....................113
3.12 Estimating Elasticities......................................................................113
3.13 Censored Dependent Variables-Tobit Model..............................114
3.14 Box-Cox Regression..........................................................................116
4. Violations of Regression Assumptions..................................................123
4.1 Zero Mean of the Disturbances Assumption................................123
4.2 Normality of the Disturbances Assumption................................124
4.3 Uncorrelatedness of Regressors and Disturbances
Assumption........................................................................................125
4.4 Homoscedasticity of the Disturbances Assumption...................127
4.4.1 Detecting Heteroscedasticity.............................................129
4.4.2 Correcting for Heteroscedasticity.....................................131
4.5 No Serial Correlation in the Disturbances Assumption.............135
4.5.1 Detecting Serial Correlation...............................................137
4.5.2 Correcting for Serial Correlation.......................................139
4.6 Model Specification Errors..............................................................142
5. Simultaneous-Equation Models..............................................................145
5.1 Overview of the Simultaneous-Equations Problem.....................145
5.2 Reduced Form and the Identification Problem.............................146
5.3 Simultaneous-Equation Estimation................................................148
5.3.1 Single-Equation Methods...................................................148
5.3.2 System-Equation Methods..................................................149
5.4 Seemingly Unrelated Equations.....................................................155
5.5 Applications of Simultaneous Equations to Transportation
Data.....................................................................................................156
Appendix 5A. A Note on GLS Estimation...............................................159
6. Panel Data Analysis...................................................................................161
6.1 Issues in Panel Data Analysis..........................................................161
6.2 One-Way Error Component Models...............................................163
6.2.1 Heteroscedasticity and Serial Correlation.......................166
6.3 Two-Way Error Component Models...............................................167
6.4 Variable-Parameter Models.............................................................172
6.5 Additional Topics and Extensions..................................................173
7. Background and Exploration in Time Series........................................175
7.1 Exploring a Time Series...................................................................176
7.1.1 Trend Component................................................................176
7.1.2 Seasonal Component...........................................................176
7.1.3 Irregular (Random) Component........................................179
7.1.4 Filtering of Time Series.......................................................179
7.1.5 Curve Fitting.........................................................................179
7.1.6 Linear Filters and Simple Moving Averages....................179
7.1.7 Exponential Smoothing Filters..........................................180
7.1.8 Difference Filter....................................................................185
7.2 Basic Concepts: Stationarity and Dependence..............................188
7.2.1 Stationarity............................................................................188
7.2.2 Dependence..........................................................................188
7.2.3 Addressing Nonstationarity..............................................190
7.2.4 Differencing and Unit-Root Testing..................................191
7.2.5 Fractional Integration and Long Memory........................194
7.3 Time Series in Regression................................................................197
7.3.1 Serial Correlation.................................................................197
7.3.2 Dynamic Dependence.........................................................197
7.3.3 Volatility................................................................................198
7.3.4 Spurious Regression and Cointegration...........................200
7.3.5 Causality...............................................................................202
8. Forecasting in Time Series: Autoregressive Integrated Moving
Average (ARIMA) Models and Extensions..........................................207
8.1 Autoregressive Integrated Moving Average Models...................207
8.2 Box-Jenkins Approach.....................................................................210
8.2.1 Order Selection.....................................................................210
8.2.2 Parameter Estimation..........................................................212
8.2.3 Diagnostic Checking...........................................................213
8.2.4 Forecasting............................................................................214
8.3 Autoregressive Integrated Moving Average Model
Extensions..........................................................................................218
8.3.1 Random Parameter Autoregressive Models....................219
8.3.2 Stochastic Volatility Models...............................................222
8.3.3 Autoregressive Conditional Duration Models................224
8.3.4 Integer-Valued ARMA Models..........................................224
8.4 Multivariate Models.........................................................................225
8.5 Nonlinear Models.............................................................................227
8.5.1 Testing for Nonlinearity.....................................................227
8.5.2 Bilinear Models....................................................................228
8.5.3 Threshold Autoregressive Models....................................229
8.5.4 Functional Parameter Autoregressive Models................230
8.5.5 Neural Networks.................................................................231
9. Latent Variable Models.............................................................................235
9.1 Principal Components Analysis.....................................................235
9.2 Factor Analysis..................................................................................241
9.3 Structural Equation Modeling........................................................244
9.3.1 Basic Concepts in Structural Equation Modeling...........246
9.3.2 Fundamentals of Structural Equation Modeling............249
9.3.3 Nonideal Conditions in the Structural Equation
Model.....................................................................................251
9.3.4 Model Goodness-of-Fit Measures......................................252
9.3.5 Guidelines for Structural Equation Modeling.................255
10. Duration Models.........................................................................................259
10.1 Hazard-Based Duration Models.....................................................259
10.2 Characteristics of Duration Data....................................................263
10.3 Nonparametric Models....................................................................264
10.4 Semiparametric Models...................................................................265
10.5 Fully Parametric Models..................................................................268
10.6 Comparisons of Nonparametric, Semiparametric, and Fully
Parametric Models............................................................................272
10.7 Heterogeneity....................................................................................274
10.8 State Dependence..............................................................................276
10.9 Time-Varying Covariates.................................................................277
10.10 Discrete-Time Hazard Models........................................................277
10.11 Competing Risk Models...................................................................279
Part III Count and Discrete Dependent Variable
Models
11. Count Data Models....................................................................................283
11.1 Poisson Regression Model...............................................................283
11.2 Interpretation of Variables in the Poisson Regression Model.... 284
11.3 Poisson Regression Model Goodness-of-Fit Measures................286
11.4 Truncated Poisson Regression Model............................................290
11.5 Negative Binomial Regression Model............................................292
11.6 Zero-Inflated Poisson and Negative Binomial Regression
Models................................................................................................295
11.7 Random-Effects Count Models.......................................................300
12. Logistic Regression....................................................................................303
12.1 Principles of Logistic Regression....................................................303
12.2 Logistic Regression Model...............................................................304
13. Discrete Outcome Models........................................................................309
13.1 Models of Discrete Data...................................................................309
13.2 Binary and Multinomial Probit Models.........................................310
13.3 Multinomial Logit Model................................................................312
13.4 Discrete Data and Utility Theory...................................................316
13.5 Properties and Estimation of MNL Models..................................318
13.5.1 Statistical Evaluation...........................................................321
13.5.2 Interpretation of Findings..................................................323
13.5.3 Specification Errors..............................................................325
13.5.4 Data Sampling......................................................................330
13.5.5 Forecasting and Aggregation Bias....................................331
13.5.6 Transferability......................................................................333
13.6 Nested Logit Model (Generalized Extreme Value Models)......334
13.7 Special Properties of Logit Models.................................................342
14. Ordered Probability Models....................................................................345
14.1 Models for Ordered Discrete Data.................................................345
14.2 Ordered Probability Models with Random Effects.....................352
14.3 Limitations of Ordered Probability Models..................................358
15. Discrete/Continuous Models...................................................................361
15.1 Overview of the Discrete/Continuous Modeling Problem........361
15.2 Econometric Corrections: Instrumental Variables and
Expected Value Method...................................................................363
15.3 Econometric Corrections: Selectivity-Bias Correction Term.......366
15.4 Discrete/Continuous Model Structures........................................368
15.5 Transportation Application of Discrete/Continuous Model
Structures...........................................................................................372
Part IV Other Statistical Methods
16. Random-Parameter Models......................................................................375
16.1 Random-Parameter Multinomial Logit Model (Mixed Logit
Model).................................................................................................375
16.2 Random-Parameter Count Models.................................................381
16.3 Random-Parameter Duration Models............................................384
17. Bayesian Models.........................................................................................387
17.1 Bayes Theorem.................................................................................387
17.2 MCMC Sampling-Based Estimation..............................................389
17.3 Flexibility of Bayesian Statistical Models via MCMC
Sampling-Based Estimation............................................................395
17.4 Convergence and Identifiability Issues with MCMC
Bayesian Models................................................................................396
17.5 Goodness-of-Fit, Sensitivity Analysis, and Model Selection
Criterion Using MCMC Bayesian Models.....................................399
Appendix A Statistical Fundamentals.......................................................403
A.l Matrix Algebra Review....................................................................403
A.l.l Matrix Multiplication..........................................................404
A.1.2 Linear Dependence and Rank of a Matrix.......................406
A.1.3 Matrix Inversion (Division)................................................406
A.1.4 Eigenvalues and Eigenvectors............................................408
A.1.5 Useful Matrices and Properties of Matrices....................409
A.1.6 Matrix Algebra and Random Variables............................410
A.2 Probability, Conditional Probability, and Statistical
Independence.....................................................................................412
A.3 Estimating Parameters in Statistical Models-Least Squares
and Maximum Likelihood...............................................................413
A.4 Useful Probability Distributions.....................................................415
A.4.1 The Z Distribution...............................................................416
A.4.2 The t Distribution................................................................417
A.4.3 The ^Distribution..............................................................418
A.4.4 The F Distribution................................................................419
Appendix B Glossary of Terms....................................................................421
Appendix C Statistical Tables......................................................................459
Appendix D Variable Transformations......................................................483
D.l Purpose of Variable Transformations............................................483
D.2 Commonly Used Variable Transformations..................................484
D.2.1 Parabolic Transformations..................................................484
D.2.2 Hyperbolic Transformations..............................................485
D.2.3 Exponential Functions........................................................485
D.2.4 Inverse Exponential Functions..........................................487
D.2.5 Power Functions...................................................................488
References...........................................................................................................489
Index.....................................................................................................................511
|
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| author | Washington, Simon Karlaftis, Matthew G. Mannering, Fred L. |
| author_GND | (DE-588)135845092 (DE-588)171689208 |
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| discipline | Mathematik Wirtschaftswissenschaften Verkehrstechnik |
| edition | 2. ed. |
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| id | DE-604.BV037373557 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T23:22:54Z |
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| isbn | 9781420082852 142008285X |
| language | English |
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| physical | XVII, 526 S. graph. Darst. |
| publishDate | 2011 |
| publishDateSearch | 2011 |
| publishDateSort | 2011 |
| publisher | CRC Press |
| record_format | marc |
| spelling | Washington, Simon Verfasser (DE-588)135845092 aut Statistical and econometric methods for transportation data analysis Simon P. Washington ; Matthew G. Karlaftis ; Fred L. Mannering 2. ed. Boca Raton, Fla. [u.a.] CRC Press 2011 XVII, 526 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Ökonometrisches Modell Transport (DE-588)4060680-6 gnd rswk-swf Entscheidungsmodell (DE-588)4121201-0 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Transportation / Econometric models Transportation / Statistical methods Transport (DE-588)4060680-6 s Statistik (DE-588)4056995-0 s DE-604 Entscheidungsmodell (DE-588)4121201-0 s Karlaftis, Matthew G. Verfasser (DE-588)171689208 aut Mannering, Fred L. Verfasser aut HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=022526847&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Washington, Simon Karlaftis, Matthew G. Mannering, Fred L. Statistical and econometric methods for transportation data analysis Ökonometrisches Modell Transport (DE-588)4060680-6 gnd Entscheidungsmodell (DE-588)4121201-0 gnd Statistik (DE-588)4056995-0 gnd |
| subject_GND | (DE-588)4060680-6 (DE-588)4121201-0 (DE-588)4056995-0 |
| title | Statistical and econometric methods for transportation data analysis |
| title_auth | Statistical and econometric methods for transportation data analysis |
| title_exact_search | Statistical and econometric methods for transportation data analysis |
| title_full | Statistical and econometric methods for transportation data analysis Simon P. Washington ; Matthew G. Karlaftis ; Fred L. Mannering |
| title_fullStr | Statistical and econometric methods for transportation data analysis Simon P. Washington ; Matthew G. Karlaftis ; Fred L. Mannering |
| title_full_unstemmed | Statistical and econometric methods for transportation data analysis Simon P. Washington ; Matthew G. Karlaftis ; Fred L. Mannering |
| title_short | Statistical and econometric methods for transportation data analysis |
| title_sort | statistical and econometric methods for transportation data analysis |
| topic | Ökonometrisches Modell Transport (DE-588)4060680-6 gnd Entscheidungsmodell (DE-588)4121201-0 gnd Statistik (DE-588)4056995-0 gnd |
| topic_facet | Ökonometrisches Modell Transport Entscheidungsmodell Statistik |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=022526847&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT washingtonsimon statisticalandeconometricmethodsfortransportationdataanalysis AT karlaftismatthewg statisticalandeconometricmethodsfortransportationdataanalysis AT manneringfredl statisticalandeconometricmethodsfortransportationdataanalysis |