Achcar, Jorge A., et al. creator text xxu New York Magnum Publishing LLC c2016 monographic eng

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xiv, 200 pages : illustrations ; 31 cm. Contents: Robust linear regression models: use of a stable distribution for the response data. -- On diagnostics in stochastic restricted linear regression models. -- Sequential variable selection as Bayesian Pragmatism in linear factor models. -- A Bayesian Quantile regression analysis of potential risk factors for violent crimes in USA. -- The validity analysis of regression: combining uniform experiment design with nonlinear regression. -- The correlation and linear regression analysis between annual GDP growth rate and money laundering in Albania during the period 2007-2011*. -- Estimators of linear regression model and prediction under some assumptions violation. -- Robust regression diagnostics of influential observations in linear regression model. -- Cross- validation Shrinkage and variable selection in linear regression revisited. -- Composite Quantile regression for nonparametric model with random censored data. -- Optimal generalized biased estimator in linear regression model. -- Performance of some Stochastic restricted ridge estimator in linear regression model. -- Latent structure linear regression. -- A Gauss- Newton- based Broyden's class algorithm for parameters of regression analysis. -- Adjusted empirical likelihood for varying coefficient partially linear models with censored data. -- Maximum likelihood estimators in linear regression models with Ornstein- Uhlenbeck process. -- The consistency of estimator under fixed design regression model with NQD error. -- Asymptotic properties of wavelet-based estimator in nonparametric regression model with weakly dependent process. -- The use of cognitive ability measures as explanatory variables in regression analysis. -- Berry- Esseen bounds for wavelet estimator in semiparametric regression model with linear process errors. Jorge A. Achcar, et al. Includes index. Linear algebra Mathematical analysis c2016. 9781682501122 170127