A Nonstationary Negative Binomial Time Series With Time-Dependent Covariates: Enterococcus Counts in Boston Harbor
In: Journal of the American Statistical Association, Jg. 101 (2006), S. 1365-1376
Online
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Zugriff:
Boston Harbor has a history of poor water quality, including contamination by enteric pathogens. We conduct a statistical analysis of data collected by the Massachusetts Water Resources Authority (MWRA) between 1996 and 2002 to evaluate the effects of court-mandated improvements in sewage treatment. Motivated by the ineffectiveness of standard Poisson mixture models and their zero-inflated counterparts, we propose a new negative binomial model for time series of Enterococcus counts in Boston Harbor, where nonstationarity and autocorrelation are modeled using a nonparametric smooth function of time in the predictor. Without further restrictions, this function is not identifiable in the presence of time-dependent covariates; consequently, we use a basis orthogonal to the space spanned by the covariates and use penalized quasi-likelihood (PQL) for estimation. We conclude that Enterococcus counts were greatly reduced near the Nut Island Treatment Plant (NITP) outfalls following the transfer of wastewaters fro...
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A Nonstationary Negative Binomial Time Series With Time-Dependent Covariates: Enterococcus Counts in Boston Harbor
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Autor/in / Beteiligte Person: | E. Andres Houseman ; Coull, Brent A. ; Shine, James P. |
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Zeitschrift: | Journal of the American Statistical Association, Jg. 101 (2006), S. 1365-1376 |
Veröffentlichung: | 2006 |
Medientyp: | unknown |
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