Identifying meteorological drivers of PM2.5 levels via a Bayesian spatial quantile regression
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DOI: 10.1002/env.2669
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References listed on IDEAS
- Brook T. Russell & Dewei Wang & Christopher S. McMahan, 2017. "Spatially modeling the effects of meteorological drivers of PM2.5 in the Eastern United States via a local linear penalized quantile regression estimator," Environmetrics, John Wiley & Sons, Ltd., vol. 28(5), August.
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- Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
- Korobilis, Dimitris, 2017. "Quantile regression forecasts of inflation under model uncertainty," International Journal of Forecasting, Elsevier, vol. 33(1), pages 11-20.
- Yu, Keming & Moyeed, Rana A., 2001. "Bayesian quantile regression," Statistics & Probability Letters, Elsevier, vol. 54(4), pages 437-447, October.
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- Silius M. Vandeskog & Thordis L. Thorarinsdottir & Ingelin Steinsland & Finn Lindgren, 2022. "Quantile based modeling of diurnal temperature range with the five‐parameter lambda distribution," Environmetrics, John Wiley & Sons, Ltd., vol. 33(4), June.
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