Download Advances in Regression, Survival Analysis, Extreme Values, by Stanisław Franciszek Mejza, Shinji Kuriki (auth.), João Lita PDF

By Stanisław Franciszek Mejza, Shinji Kuriki (auth.), João Lita da Silva, Frederico Caeiro, Isabel Natário, Carlos A. Braumann (eds.)

This quantity of the chosen Papers from Portugal is a made from the 17th Congress of the Portuguese Statistical Society, held on the attractive inn beach urban of Sesimbra, Portugal, from September 30 to October three, 2009. It covers a wide scope of theoretical, methodological in addition to application-oriented articles in domain names akin to: Linear versions and Regression, Survival research, severe worth concept, records of Diffusions, Markov techniques and different Statistical Applications.

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Extra info for Advances in Regression, Survival Analysis, Extreme Values, Markov Processes and Other Statistical Applications

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289–307. Springer Handbooks of Computational Statistics, Springer (2010) 17. : A Satisfac¸a˜ o e Lealdade do Cliente - Metodologias de Avaliac¸a˜ o, Gest˜ao e An´alise, 2ł edic¸a˜ o, Escolar Editora (2011) Part II Communications A Parametric Cure Model with Covariates Ana M. Abreu and Cristina S. Rocha Abstract Cure models were developed to deal with situations where it is plausible to assume that there are non-susceptible (or cured) individuals within the study population. Usually, in a cure model, the aim is to estimate the proportion of nonsusceptible individuals, the survival function of the susceptible individuals and the effect of the covariates, if they have been included in the model.

3 Detailed simulation results regarding the symmetric data model are shown in Tables 2 and 3 and results referring to asymmetric data appear in Tables 4 and 5 in appendix. Tables 2 and 4 show the bias of model parameters (loadings and inner model coefficients) both for the PLS and ML techniques. The dispersion of the estimators is accessed through the standard deviations which are presented in Tables 3 and 5. J. S. 25 0 0 0 200 400 600 800 1000 1200 1400 1600 1800 2000 0 200 400 600 800 1000 1200 1400 1600 1800 2000 Sample Size PLS_Sym PLS_Asym ML_Sym Sample Size ML_Asym PLS_Sym PLS_Asym ML_Sym ML_Asym Fig.

Finally, the structural model (1) is estimated, using Ordinary Least Squares for each equation and each latent variable replaced by its estimate. The adoption of OLS is possible since model (1) is recursive and as a consequence the matrix of the parameters of endogenous variables is triangular. The case values of the latent variables are inconsistent due to the fact that they are estimated as aggregates of the observed or manifest variables (cf. outside approximation), which include a measurement error.

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