CHAPTER 12 Final Remarks There are a number of books available describing interesting applications of statistics in environmental science.. The book series Statistics in the Environment
Trang 1CHAPTER 12 Final Remarks
There are a number of books available describing interesting applications of statistics in environmental science The book series
Statistics in the Environment is a good starting point because it
contains papers arising from conferences with different themes covering environmental monitoring, pollution and contamination, climate change and meteorology, water resources, fisheries and forestry, radiation, and air quality (Barnett and Turkman, 1993, 1994, 1997) Further examples of applications are also provided by Fletcher
and Manly (1994), Fletcher et al (1998), and Nychka et al (1998).
For more details about statistical methods in general, the handbook
edited by Patil and Rao (1994) or the Encyclopedia of Environmetrics
(El-Shaarawi and Piegorsch, 2001) are good general references There are several journals that specialize in publishing papers on applications of statistics in environmental science, with the most
important being Environmetrics, Ecological and Environmental
Statistics, and The Journal of Agricultural, Biological and Environmental Statistics In addition, journals on environmental
management frequently contain papers on statistical methods
It is always risky to attempt to forecast the development of a subject area No doubt new statistical methods will continue to be proposed in all of the areas discussed in this book, but it does seem that the design and analysis of monitoring schemes, time series analysis, and spatial data analysis will receive particular attention as far as research is concerned In particular, approaches for handling temporal and spatial variation at the same time are still in the early stages of development
One important topic that has not been discussed in this book is the handling of the massive multivariate data sets that can be produced
by automated recording devices Often the question is how to reduce the data set to a smaller (but still very large) set that can be analysed
by standard statistical methods There are many future challenges for the statistics profession in learning how to handle the problems involved (Manly, 2000)
© 2001 by Chapman & Hall/CRC