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Author: Paulo M.M Rodrigues Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
This article discusses the asymptotic and finite-sample properties of the CUSUM tests for detecting structural breaks in volatility when the data are perturbed with (additive) outliers and/or measurement errors. The special focus is on the parametric and non-parametric tests in Inclán and Tiao (1994) and Kokoszka and Leipus (2000). Whereas the asymptotic distribution of the former can be largely affected, the distribution of the latter remains invariant and renders consistent break-point estimates. In small samples, however, large additive outliers are able to generate sizeable distortions in both tests, which explains some of the contradictory findings in previous literature.
Author: Paulo M.M Rodrigues Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
This article discusses the asymptotic and finite-sample properties of the CUSUM tests for detecting structural breaks in volatility when the data are perturbed with (additive) outliers and/or measurement errors. The special focus is on the parametric and non-parametric tests in Inclán and Tiao (1994) and Kokoszka and Leipus (2000). Whereas the asymptotic distribution of the former can be largely affected, the distribution of the latter remains invariant and renders consistent break-point estimates. In small samples, however, large additive outliers are able to generate sizeable distortions in both tests, which explains some of the contradictory findings in previous literature.
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: D. Hawkins Publisher: Springer Science & Business Media ISBN: 9401539944 Category : Science Languages : en Pages : 194
Book Description
The problem of outliers is one of the oldest in statistics, and during the last century and a half interest in it has waxed and waned several times. Currently it is once again an active research area after some years of relative neglect, and recent work has solved a number of old problems in outlier theory, and identified new ones. The major results are, however, scattered amongst many journal articles, and for some time there has been a clear need to bring them together in one place. That was the original intention of this monograph: but during execution it became clear that the existing theory of outliers was deficient in several areas, and so the monograph also contains a number of new results and conjectures. In view of the enormous volume ofliterature on the outlier problem and its cousins, no attempt has been made to make the coverage exhaustive. The material is concerned almost entirely with the use of outlier tests that are known (or may reasonably be expected) to be optimal in some way. Such topics as robust estimation are largely ignored, being covered more adequately in other sources. The numerous ad hoc statistics proposed in the early work on the grounds of intuitive appeal or computational simplicity also are not discussed in any detail.
Author: Paulo M.M Rodrigues
Author: Paulo M.M Rodrigues
Author: G. S. Maddala
Author: Tai-leung Terence Chong
Author: Philip Hans Franses
Author: Lawrence R. Ernst
Author: 
Author: Niels Haldrup
Author: Matthias Fengler (Wirtschaftswissenschaftler Univ. St. Gallen.)
Author: Lang Wu
Author: D. Hawkins