@article{Smith:267938,
      recid = {267938},
      author = {Smith, Michael and Yau, Paul and Shively, Thomas and Kohn,  Robert},
      title = {Estimating Long-Term Trends in Tropospheric Ozone Levels},
      address = {1998-04-01},
      number = {2012-2018-685},
      series = {Working Paper 2/98},
      pages = {41},
      year = {1998},
      abstract = {This paper estimates the long-term trends in the daily  maxima of tropospheric ozone at six sites around the state  of Texas. The statistical methodology we use controls for  the effects of meteorological variables because it is known  that variables such as temperature, wind speed and humidity  substantially affect the formation of tropospheric ozone. A  nonparametric regression model is estimated in which a  general trivariate surface is used to model the  relationship between ozone and these meteorological  variables because there is little, or no, theory to specify  the functional dependence of ozone on these variables. The  model also allows for the effects of wind direction and  seasonality. Each function in the model is represented as a  linear combination of basis functions located at all of the  design points. A trivariate basis is used for the function  representing the combined effect of temperature, wind speed  and humidity, while univariate bases are used to represent  the other functions in the model. To estimate the functions  nonparametrically we use a Bayesian hierarchical framework  with a fractional prior. Due to the high dimensional  representation of the signal, a Markov chain Monte Carlo  sampling scheme employing Gibbs sub-chains that 'focus' on  the basis terms that are most likely to contribute to the  signal is used to carry out the computations. We also  estimate an appropriate data transformation simultaneously  with the function estimates. The empirical results indicate  that key meteorological variables explain most of the  variation in daily ozone maxima through a nonlinear  interaction and that their effects are consistent across  the six sites. However, the estimated trends vary  considerably from site to site, even within the same city.  A simulation based on the design of the data indicates that  the Bayesian approach is substantially more efficient than  MARS (Friedman, 1991).},
      url = {http://ageconsearch.umn.edu/record/267938},
      doi = {https://doi.org/10.22004/ag.econ.267938},
}