@article { ISI:000246559600002,
	title = {Seasonal confounding and residual correlation in analyses of health effects of air pollution},
	journal = {ENVIRONMETRICS},
	volume = {18},
	number = {4},
	year = {2007},
	month = {JUN},
	pages = {375-394},
	publisher = {JOHN WILEY \& SONS LTD},
	type = {Article},
	address = {THE ATRIUM, SOUTHERN GATE, CHICHESTER PO19 8SQ, W SUSSEX, ENGLAND},
	abstract = {To investigate the health effects of air pollution via a partially linear model, one must choose an appropriate amount of smoothing for accurate estimation of the linear pollution effects. This choice is complicated by the dependencies between the various covariates and by the potential residual correlation. Most existing approaches to making this choice are inadequate, as they neither target accurate estimation of the linear pollutant effects nor handle residual correlation. In this paper, we illustrate two new adaptive and objective methods for determining an appropriate amount of smoothing. We construct valid confidence intervals for the linear pollutant effects, intervals that account for residual correlation. We use our inferential methods to investigate the same-day effects of PM10 on daily mortality in two data sets for the period 1994 to 1996: one collected in Mexico City, an urban area with high levels of air pollution, and the other collected in Vancouver, British Columbia, an urban area with low levels of air pollution. For Mexico City, our methodology does not detect a PM10 effect. In contrast, for Vancouver, a PM10 effect corresponding to an expected 2.4\% increase (95\% confidence interval ranging from 0.0\% to 4.7\%) in daily mortality for every 10 mu g/m(3) increase in PM10 is identified. Copyright (c) 2006 John Wiley \& Sons, Ltd.},
	keywords = {air pollution, bandwidth selection, generalized additive model, mortality, partially linear model, particulate matter, residual correlation, seasonal confounding, Smoothing},
	issn = {1180-4009},
	doi = {10.1002/env.814},
	author = {Ghement, Isabella R. and Heckman, Nancy E. and Petkau, A. John}
}
@article { ISI:000186756300013,
	title = {Local linear extrapolation},
	journal = {JOURNAL OF NONPARAMETRIC STATISTICS},
	volume = {15},
	number = {4-5},
	year = {2003},
	month = {AUG-OCT},
	pages = {565-578},
	publisher = {TAYLOR \& FRANCIS LTD},
	type = {Article},
	address = {4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND},
	abstract = {A local linear estimator is proposed for extrapolating beyond the range of independent regression data. The asymptotic bias and variance of the local linear extrapolant are presented and used to calculate the asymptotic mean squared error. Three procedures are considered for choice of the bandwidth: a plug-in procedure. a forward cross-validation procedure and a procedure that combines the two. All of these are, in some sense. based on minimizing the asymptotic mean squared error. A simulation study shows that the forward cross-validation procedure outperforms the other two procedures both in terms of choosing a bandwidth and in terms of accurate extrapolation. The techniques are applied to two data sets.},
	keywords = {bandwidth selection, local linear estimation, prediction},
	issn = {1048-5252},
	doi = {10.1080/10485250310001605432},
	author = {Li, XC and Heckman, NE}
}
