University of Illinois Urbana-Champaign

Recursive methods for statistical prediction with applications

Chang, Yue-Fang

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https://hdl.handle.net/2142/23742

Description

Title
Recursive methods for statistical prediction with applications
Author(s)
Chang, Yue-Fang
Issue Date
1991
Doctoral Committee Chair(s)
Cox, Dennis D.
Department of Study
Statistics
Discipline
Statistics
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Statistics
Language
eng
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.
Type of Resource
text
Permalink
http://hdl.handle.net/2142/23742
Copyright and License Information
Copyright 1991 Chang, Yue-Fang

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