A nonparametric regression algorithm for time series forecasting applied to daily maximum urban ozone concentrations

dc.contributor.advisorThompson, James R.
dc.creatorSanchez, Rolando Pena
dc.date.accessioned2009-06-04T00:44:53Z
dc.date.available2009-06-04T00:44:53Z
dc.date.issued1989
dc.description.abstractUsing techniques of nonparametric regression, we develop a nonparametric approach in the context of kernel estimation to realize short-term forecastings of time series. This procedure is applied to an OZONE ($O\sb3)$ daily maximum series, whose values were filtered according to the Tukey (biweight) kernel function: $K(x) = {15\over 16}(1 - x\sp2)\sp2 I\sb{(-1,1)}(x)$. Some parametric approaches such as multivariate regression and autoregressive integrated moving average (ARIMA) models (under assumptions of normality, stationarity, invertibility, etc.) are also shown and compared with the nonparametric approach, which is an attractive alternative. Moreover a procedure for the estimation of missing observations in time series, and a method to improve the optimal "bandwidth" selection for the nonparametric regression kernel estimator are proposed.
dc.format.extent158 p.en_US
dc.format.mimetypeapplication/pdf
dc.identifier.callnoThesis Stat. 1990 Sanchez
dc.identifier.citationSanchez, Rolando Pena. "A nonparametric regression algorithm for time series forecasting applied to daily maximum urban ozone concentrations." (1989) Diss., Rice University. <a href="https://hdl.handle.net/1911/16388">https://hdl.handle.net/1911/16388</a>.
dc.identifier.urihttps://hdl.handle.net/1911/16388
dc.language.isoeng
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.
dc.subjectStatistics
dc.subjectEnvironmental science
dc.titleA nonparametric regression algorithm for time series forecasting applied to daily maximum urban ozone concentrations
dc.typeThesis
dc.type.materialText
thesis.degree.departmentStatistics
thesis.degree.disciplineEngineering
thesis.degree.grantorRice University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy
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