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Author: Bo E. Honoré Publisher: ISBN: Category : Languages : en Pages : 7
Book Description
This note illustrates that the typical parameter, beta, in a censored regression model can be used to calculate an interesting marginal effect even when the errors in the model and the explanatory variables are not independent. The result is relevant for cross sectional models such at the ones considered in Powell (1984), Powell (1986) and Chen and Khan (2000), as well as for panel data models such as the ones in Honore (1992) and Alan and Leth-Petersen (2006), and it applies with fixed as well as with random censoring.
Author: Bo E. Honoré Publisher: ISBN: Category : Languages : en Pages : 7
Book Description
This note illustrates that the typical parameter, beta, in a censored regression model can be used to calculate an interesting marginal effect even when the errors in the model and the explanatory variables are not independent. The result is relevant for cross sectional models such at the ones considered in Powell (1984), Powell (1986) and Chen and Khan (2000), as well as for panel data models such as the ones in Honore (1992) and Alan and Leth-Petersen (2006), and it applies with fixed as well as with random censoring.
Author: Richard Breen Publisher: SAGE ISBN: 9780803957107 Category : Mathematics Languages : en Pages : 92
Book Description
This book provides an introduction to the regression models needed, where an outcome variable for a sample is not representative of the population from which a generalized result is sought.
Author: Lang Wu Publisher: CRC Press ISBN: 9781420074086 Category : Mathematics Languages : en Pages : 431
Book Description
Although standard mixed effects models are useful in a range of studies, other approaches must often be used in correlation with them when studying complex or incomplete data. Mixed Effects Models for Complex Data discusses commonly used mixed effects models and presents appropriate approaches to address dropouts, missing data, measurement errors, censoring, and outliers. For each class of mixed effects model, the author reviews the corresponding class of regression model for cross-sectional data. An overview of general models and methods, along with motivating examples After presenting real data examples and outlining general approaches to the analysis of longitudinal/clustered data and incomplete data, the book introduces linear mixed effects (LME) models, generalized linear mixed models (GLMMs), nonlinear mixed effects (NLME) models, and semiparametric and nonparametric mixed effects models. It also includes general approaches for the analysis of complex data with missing values, measurement errors, censoring, and outliers. Self-contained coverage of specific topics Subsequent chapters delve more deeply into missing data problems, covariate measurement errors, and censored responses in mixed effects models. Focusing on incomplete data, the book also covers survival and frailty models, joint models of survival and longitudinal data, robust methods for mixed effects models, marginal generalized estimating equation (GEE) models for longitudinal or clustered data, and Bayesian methods for mixed effects models. Background material In the appendix, the author provides background information, such as likelihood theory, the Gibbs sampler, rejection and importance sampling methods, numerical integration methods, optimization methods, bootstrap, and matrix algebra. Failure to properly address missing data, measurement errors, and other issues in statistical analyses can lead to severely biased or misleading results. This book explores the biases that arise when naïve methods are used and shows which approaches should be used to achieve accurate results in longitudinal data analysis.
Author: Bo E. Honoré
Author: Bo E. Honoré
Author: William H. Greene
Author: Lei Pang
Author: Tue Gørgens
Author: Hui-Lin Lin
Author: Richard Breen
Author: Alfred Hamerle
Author: Lang Wu
Author: Myoung-Jae Lee
Author: Pavel Čížek