Smoothing Spline Models for the Analysis of Nested and Crossed Samples of Curves: Rejoinder

1998 ◽  
Vol 93 (443) ◽  
pp. 991 ◽  
Author(s):  
Babette Brumback ◽  
John Rice
Keyword(s):  
Smoothing Spline ◽  
Spline Models

Bayesian smoothing spline models and their application in estimating yield curves

2015 ◽  
Author(s):  
◽  
Xiaojun Tong
Keyword(s):  
Interest Rates ◽  
Term Structure ◽  
Yield Curve ◽  
Smoothing Spline ◽  
Calendar Time ◽  
Yield Data ◽  
Yield Curves ◽  
Adaptive Smoothing ◽  
Spline Models ◽  
Bayesian Smoothing

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] The term structure of interest rates, also called the yield curve, is the series of interest rates ordered by term to maturity at a given time. The smoothing spline as a nonparametric regression method has been used widely for fitting a smooth curve due to its flexibility and smoothing properties. In this dissertation, a class of Bayesian smoothing spline models is developed for the yield curve estimation under different scenarios. These include the Bayesian smoothing spline model for estimating the Treasury yield curves, the Bayesian multivariate smoothing spline model for estimating multiple yield curves jointly, the Bayesian adaptive smoothing spline model for dealing with the yield data in which the smoothness varies significantly, the Bayesian smoothing spline model for extracting the zero-coupon yield curve from coupon bond prices, and the Bayesian thin-plate splines for modeling the yield curves on both the calendar time and the maturity. In addition, the Bayesian model selection in the smoothing spline models is developed to test the nonlinearity of the yield curves.

Partially informative normal and partial spline models

2015 ◽  
Author(s):  
◽  
Sifan Liu
Keyword(s):  
Yield Curve ◽  
Smoothing Splines ◽  
Smoothing Spline ◽  
Linear Component ◽  
Function Estimation ◽  
Regression Problem ◽  
Adaptive Smoothing ◽  
University Of Missouri ◽  
Spline Models ◽  
Large P Small N

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] There is a well-known Bayesian interpretation of function estimation by spline smoothing using a limit of proper normal priors. This limiting prior has the same form with Partially Informative Normal (PIN), which was introduced in Sun et al. (1999). In this dissertation, we first discuss some properties of PIN. In terms of improper priors, we consider q-vague convergence as the convergence mode. Then, we apply the properties to several extensions of smoothing spline problems. Partial spline model, which contains a non-parametric part as regular smoothing spline together with a linear parametric part, is discussed. We perform simulation studies and applications on yield curves. Specifically, Nelson-Siege (NS) model is considered to construct the linear component. NS partial spline model is used for fitting single yield curve, while partial parallel and non-parallel spline models are used for multiple curves. Then, large p, small n regression problem associated with the generalized univariate smoothing spline, some studies on bin smoothing splines, adaptive smoothing splines and correlated smoothing splines are discussed.

scholarly journals Local and global asymptotic inference in smoothing spline models

2013 ◽  
Vol 41 (5) ◽  
pp. 2608-2638 ◽  
Author(s):  
Zuofeng Shang ◽  
Guang Cheng
Keyword(s):  
Smoothing Spline ◽  
Asymptotic Inference ◽  
Spline Models
Sign in / Sign up

Export Citation Format

Share Document