Recursive methods for statistical prediction with applications

Recursive methods for solving the nonparametric regression problem in the GLIMs and computing the Best Linear Unbiased Predictors are discussed here. An iterated state space algorithm is introduced to compute the generalized smoothing spline estimate, and it is especially useful in calculating the leave-one-out estimates. Two cross validation functions (Kullback-Leibler and least squares cross validation) for estimating the smoothing parameter in the generalized smoothing splines are discussed. The simulation results showed that these two cross validation functions performed quite well.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Statistics

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2011

Author dc:creator

• Chang, Yue-Fang

Contributors dc:contributor

• Cox, Dennis D.

• Statistics

Statement dc:rights

• Copyright 1991 Chang, Yue-Fang

Language dc:language

eng

Identifier

AAI9124392
(UMI)AAI9124392

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/23742