Semiparametric regression is a fusion between parametric regression and nonparametric regression that integrates low-rank penalized splines, mixed model and hierarchical Bayesian methodology – thus allowing more streamlined handling of longitudinal and spatial correlation. We review progress in the field over the five-year period between 2003 and 2007. We find semiparametric regression to be a vibrant field with substantial involvement and activity, continual enhancement and widespread application. © 2009, Institute of Mathematical Statistics. All rights reserved
In this thesis, we investigate new methods, extending the marginal and mixed effects models to deal ...
Kriging and nonparametric regression are described and prediction intervals using GCV smoothing spli...
© 2018, American Statistical Association. All rights reserved. We provide several examples of Bayesi...
During the last two decades, many areas of statistical inference have experienced phenomenal growth....
The semiparametric linear model is an important alternative to the Cox proportional hazards model fo...
The semiparametric linear model is an important alternative to the Cox proportional hazards model fo...
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appe...
Penalized spline estimators have received considerable attention in recent years because of their go...
Penalized spline estimators have received considerable attention in recent years because of their go...
We study the marginal longitudinal nonparametric regression problem and some of its semiparametric e...
This dissertation focuses on non- and semiparametric specification of regression models for the cond...
We provide several illustrations of Bayesian semiparametric regression analyses in the BRugs package...
Motivated by the need to analyze the National Longitudinal Surveys data, we propose a new semiparame...
This dissertation focuses on non- and semiparametric specification of regression models for the cond...
In this thesis, we investigate new methods, extending the marginal and mixed effects models to deal ...
In this thesis, we investigate new methods, extending the marginal and mixed effects models to deal ...
Kriging and nonparametric regression are described and prediction intervals using GCV smoothing spli...
© 2018, American Statistical Association. All rights reserved. We provide several examples of Bayesi...
During the last two decades, many areas of statistical inference have experienced phenomenal growth....
The semiparametric linear model is an important alternative to the Cox proportional hazards model fo...
The semiparametric linear model is an important alternative to the Cox proportional hazards model fo...
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appe...
Penalized spline estimators have received considerable attention in recent years because of their go...
Penalized spline estimators have received considerable attention in recent years because of their go...
We study the marginal longitudinal nonparametric regression problem and some of its semiparametric e...
This dissertation focuses on non- and semiparametric specification of regression models for the cond...
We provide several illustrations of Bayesian semiparametric regression analyses in the BRugs package...
Motivated by the need to analyze the National Longitudinal Surveys data, we propose a new semiparame...
This dissertation focuses on non- and semiparametric specification of regression models for the cond...
In this thesis, we investigate new methods, extending the marginal and mixed effects models to deal ...
In this thesis, we investigate new methods, extending the marginal and mixed effects models to deal ...
Kriging and nonparametric regression are described and prediction intervals using GCV smoothing spli...
© 2018, American Statistical Association. All rights reserved. We provide several examples of Bayesi...