发帖
雷达卡
Title: Multilevel statistical models
Publ Info: London : E. Arnold ; New York : Halsted Press, 1995
Multilevel statistical models 经典教材
Chapter 1
1.1 Multilevel data
1.2 School effectiveness
1.3 Sample survey methods
1.4 Repeated measures data
1.5 Event history models
1.6 Discrete response data
1.7 Multivariate models
1.8 Nonlinear models
1.9 Measurement errors
1.10 Random cross classifications
1.11 Structural equation models
1.12 Levels of aggregation and ecological fallacies
1.13 Causality
1.14 A caveat
Chapter 2
2.1 The 2-level model and basic notation
2.2 The 2-level model
2.3 Parameter estimation for the variance components model
2.4 The general 2-level model including random coefficients
2.5 Estimation for the multilevel model
2.6 Other estimation procedures
2.7 Residuals
2.8 The adequacy of Ordinary Least Squares estimates.
2.9 A 2-level example using longitudinal educational achievement data
2.9.1 Checking model assumptions
2.9.2 Checking for influential units
2.10 Higher level explanatory variables and compositional effects
2.11 Hypothesis testing and confidence intervals
2.11.1 Fixed parameters
2.11.2 Random parameters
2.11.3 Residuals
Appendix 2.1 The general structure and estimation for a multilevel model
Appendix 2.2 Multilevel residuals estimation
Appendix 2.3 The EM algorithm
Appendix 2.4 Markov Chain Monte Carlo (MCMC) estimation
Chapter 3
3.1 Complex variance structures
3.1.1 Variances for subgroups defined at level 1
3.1.2 Variance as a function of predicted value
3.1.3 Variances for subgroups defined at higher levels
3.2 A 3-level complex variation model
3.3 Parameter Constraints
3.4 Weighting units
3.5 Robust, Jacknife and Bootstrap Uncertainty Estimates
3.6 Aggregate level analyses
3.7 Meta Analysis
Chapter 4
4.1 Multivariate Multilevel models
4.2 The basic 2-level multivariate model
4.3 Rotation Designs
4.4 A rotation design example using Science test scores
4.5 Principal Components analysis
4.6 Multiple Discriminant analysis
4.7 Other Procedures
Chapter 5
5.1 Nonlinear models
5.2Nonlinear functions of linear components
5.8 Estimating population means
5.4 Nonlinear functions for variances and covariances
5.5 Examples of nonlinear growth and nonlinear level 1 variance
5.6 Multivariate Nonlinear Models
Appendix 5.1 Nonlinear model estimation
5.1.1 Modelling linear components
5.1.2 Modelling variances and covariances as nonlinear functions
5.1.3 Likelihood values
Chapter 6
6.1 Models for repeated measures
6.2 A 2-level repeated measures model
6.3 A polynomial model example for adolescent growth and the prediction of
adult height
6.4 Modelling an autocorrelation structure at level 1
6.5 A growth model with autocorrelated residuals
6.6 Multivariate repeated measures models
6.7 Scaling across time
6.8 Cross-over designs
Chapter 7
7.1 Models for discrete response data
7.2 Proportions as responses
7.3 An example from a survey of voting behaviour
7.4 Models for multiple response categories
7.5 An example of voting behaviour with multiple responses
7.6 Models for counts
7.7 Ordered responses
7.8 Mixed discrete - continuous response models
Appendix 7.1 Differentials for some discrete response models
Chapter 8
8.1 Random cross classifications
8.2 A basic cross classified model
8.3 Examination results for a cross classification of schools
8.4 Computational considerations
8.5 Interactions in cross classifications
8.6 Level 1 cross classifications
8.7 Cross-unit membership models
8.8 Multivariate cross classified models
Appendix 8.1 Random cross classified data structures
Chapter 9
9.9 Event history models
9.2 Censoring
9.3 Hazard based models in continuous time
9.4 Parametric proportional hazard models
9.5 The semiparametric Cox model
9.6 Tied observations
9.7 Repeated measures proportional hazard models
9.8 Example using birth interval data
9.9 The discrete time (piecewise) proportional hazards model
9.10 Log duration models
Chapter 10
10.1 Errors of measurement
10.2 Measurement errors in level 1 variables
10.3 Measurement errors in higher level variables
10. 4 A 2-level example with measurement error at both levels.
10.5 Multivariate responses
10. 6 Nonlinear models
10.7 Measurement errors for discrete explanatory variables
Appendix 10.1 Measurement errors
10.1.1 The Basic 2-level Model
10.1.2 Parameter estimation
10.1.3 Random coefficients for explanatory variables measured with error
10.1.4 Nonlinear models
Chapter 11
11.1 Software for multilevel analysis
11.2 Design issues
11.3 Missing data
11.4 Creating a completed data set
11. 5 Multiple imputation and error corrections
11.6 Discrete variables with missing data
11.7 An example with missing data
11.8 Multilevel structural equation models
11.9 A factor analysis example using Science test scores
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