Files in this item



application/pdf9124392.pdf (7MB)Restricted to U of Illinois
(no description provided)PDF


Title:Recursive methods for statistical prediction with applications
Author(s):Chang, Yue-Fang
Doctoral Committee Chair(s):Cox, Dennis D.
Department / Program:Statistics
Degree Granting Institution:University of Illinois at Urbana-Champaign
Abstract: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.
Best Linear Unbiased Predictors can be used to predict the values of a random function from the observed values. It is of interest to have an efficient algorithm to compute the predictions when observations are taken sequentially, rather than performing the linear algebra from the beginning. An updatable algorithm, which is based on combining the updating procedures of Cholesky and Q-R decomposition, is introduced to compute the predictions and mean squared error of the predictions when more observations are taken.
Issue Date:1991
Rights Information:Copyright 1991 Chang, Yue-Fang
Date Available in IDEALS:2011-05-07
Identifier in Online Catalog:AAI9124392
OCLC Identifier:(UMI)AAI9124392

This item appears in the following Collection(s)

Item Statistics