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A Statistical Machine Learning Framework
Published in Richard M. Golden, Statistical Machine Learning, 2020
The assumption that a statistical environment is “stationary” means that environmental statistics cannot change as a function of time. An important type of stationary statistical environment can be defined by specifying a feature transformation that maps sequences of events in the real world into a sequence of feature vectors (see Chapter 9). In order for learning to be effective, some assumptions about the characteristics of the statistical learning environment are required. It is typically assumed that the statistical environment is stationary. Stationarity implies, for example, that if a learning machine is trained with a particular data set, then the particular time when this training occurs is irrelevant. Stationarity also implies that there exist statistical regularities in training data which will be useful for processing novel test data.
Environmental Statistics
Published in Sven Erik Jørgensen, A Systems Approach to the Environmental Analysis of Pollution Minimization, 2020
For persons with a background in natural sciences, environmental statistics will deal with the state of the environment by measuring concentrations. So, you just focus on a lake and measure the concentrations in the lake, e.g., phosphorus and nitrogen in the winter or the Secchi depth and phytoplankton in the summer. You should sample some polluted lakes and some clean lakes, and it would be preferable to have a geographical coverage, so a few lakes from each county would also be desirable. Some lakes have been measured for many years, so you could also include these in a nationwide monitoring program. A calculation of the mean should give the state of the environment in all lakes in the country.
Energy and Environmental Markets
Published in Anco S. Blazev, Power Generation and the Environment, 2021
The coal market is going through a major shift; in the U.S. the demand for coal is diminishing. To compensate for losses in the U.S. energy market, the coal companies are finding overseas markets for their coal and are exporting large quantities. That is a new phenomenon, which is quite controversial, because on one hand we boast reducing GHG emissions as a result of reducing the amount of coal burned, while on the other we are exporting massive amounts of to be burned somewhere else. The net effect to the GHG emissions is absolute zero, and we must account for that in the environmental statistics and formulas.
The health effects of Low-carbon Province Pilot Policy in China: an empirical evidence based on China Family Panel Studies (CFPS) from 2010 to 2016
Published in Journal of Experimental Nanoscience, 2023
Yongyou Nie, Zhiyi Wang, Fan Wu
For the annual emissions of , soot (dust) and , all provincial panel data are calculated based on the China Statistical Yearbook, China Environmental Statistics Yearbook, and China Environmental Statistics Annual Report. The effectiveness of health care has grown as a result of decentralised economic means. Important national choices on public financing, stakeholder involvement in decision-making, including decentralising administration of human resources are required. The Yearbook provides information on the advancement of China’s scientific or technological initiatives that includes statistical properties at the federal, province, municipal, or independent country sectors in addition to agencies immediately underneath the State Council.
Copula-based monitoring schemes for non-Gaussian multivariate processes
Published in Journal of Quality Technology, 2020
Pavel Krupskii, Fouzi Harrou, Amanda S. Hering, Ying Sun
Ying Sun received her Ph.D. in Statistics from Texas A&M in 2011 followed by a two-year postdoctoral research position at the Statistical and Applied Mathematical Sciences Institute and at the University of Chicago. She was an Assistant Professor at the Ohio State University for a year before joining KAUST in 2014. At KAUST, Professor Sun established and leads the Environmental Statistics research group which works on developing statistical models and methods for complex data to address important environmental problems. She has made original contributions to environmental statistics, in particular in the areas of spatio-temporal statistics, functional data analysis, visualization, computational statistics, with an exceptionally broad array of applications. Professor Sun won two prestigious awards: the Early Investigator Award in Environmental Statistics presented by the American Statistical Association, and the Abdel El-Shaarawi Young Research Award from the International Environmetrics Society.
The autonomous robotic environmental sensor (ARES)
Published in Science and Technology for the Built Environment, 2021
Benjamin Dyer, Mohammad Biglarbegian, Amir A. Aliabadi
This section describes the methods used for controlling the Autonomous Robotic Environmental Sensor (ARES) and analyzing the collected data (i.e. environmental variables such as wind velocity, temperature, and relative humidity). The platform is described, and a control scheme based on a kinematic model is designed. Standard methods for obtaining variances and covariances of environmental variables, and their systematic and random errors are formulated. The Predicted Mean Vote (PMV)-Predicted Percent Dissatisfied (PPD) model is briefly described, and the error due to simplifications of the model is quantified. Finally, an environmental sensing experiment is formulated to determine environmental statistics and predicted thermal comfort in an indoor office environment.