Measurement error models for time series

dc.contributor.advisor Wayne A. Fuller
dc.contributor.author Eltinge, John
dc.contributor.department Statistics
dc.date 2018-08-16T22:11:48.000
dc.date.accessioned 2020-07-02T06:08:12Z
dc.date.available 2020-07-02T06:08:12Z
dc.date.copyright Thu Jan 01 00:00:00 UTC 1987
dc.date.issued 1987
dc.description.abstract <p>Estimation for multivariate linear measurement error models with serially correlated observations is addressed;The asymptotic properties of some standard linear errors-in-variables regression parameter estimators are developed under an ultrastructural model in which the random components of the model follow a linear process. Under the same assumptions, the asymptotic properties of weighted method-of-moments estimators are derived. The large-sample results rest on the asymptotic properties of the sum of a linear function and a quadratic function of a sequence of serially correlated random vectors;Maximum likelihood estimation for the normal structural and functional models is addressed. For each model, first- and second-derivative matrices of the log-likelihood functions are given and Newton-Raphson maximum likelihood estimation procedures are considered. For the structural model, the assumption that the random components follow a multivariate autoregressive moving average process is used to develop autoregressive moving average and state-space models for the observation sequence. The state-space representation of the structural model leads to innovation sequences and associated derivative sequences that provide the basis for a Newton-Raphson procedure for the estimation of regression parameters and autocovariance parameters of the structural model. A modified state-space approach leads to a similar procedure for the estimation for the functional model. An extension of the state-space approach to maximum likelihood estimation for a structural model with combined time series and cross-sectional data is given.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/rtd/8639/
dc.identifier.articleid 9638
dc.identifier.contextkey 6342986
dc.identifier.doi https://doi.org/10.31274/rtd-180813-8663
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath rtd/8639
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/81649
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/rtd/8639/r_8805069.pdf|||Sat Jan 15 02:14:42 UTC 2022
dc.subject.disciplines Statistics and Probability
dc.subject.keywords Statistics
dc.title Measurement error models for time series
dc.type article
dc.type.genre dissertation
dspace.entity.type Publication
relation.isOrgUnitOfPublication 264904d9-9e66-4169-8e11-034e537ddbca
thesis.degree.level dissertation
thesis.degree.name Doctor of Philosophy
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