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Linear models and regression analysis / Jorge A. Achcar, et al.

By: Material type: TextTextPublication details: New York : Magnum Publishing LLC, c2016.Description: xiv, 200 pages : illustrations ; 31 cmISBN:
  • 9781682501122
Subject(s): DDC classification:
  • c2016.
Summary: 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.
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Holdings
Item type Current library Collection Call number Status Date due Barcode Item holds
Book Book DSSC LEARNING RESOURCE CENTER Circulation Cir./512/L645/c2016 (Browse shelf(Opens below)) Available 007897
Total holds: 0

Includes index.

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.

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