Parsimonious Var Models For Air Pollution Dynamic Analysis
نویسندگان
چکیده
منابع مشابه
Implications of Dynamic Factor Models for Var Analysis
This paper considers VAR models incorporating many time series that interact through a few dynamic factors. Several econometric issues are addressed including estimation of the number of dynamic factors and tests for the factor restrictions imposed on the VAR. Structural VAR identification based on timing restrictions, long run restrictions, and restrictions on factor loadings are discussed and...
متن کاملNumerical Time Integration for Air Pollution Models
Due to the large number of chemical species and the three space dimensions, o -the-shelf sti ODE integrators are not feasible for the numerical time integration of sti systems of advection-diffusion-reaction equations @c @t +r (uc) = r (Kr c) +R (c) ; c = c(x; t); c 2 IRm; x 2 IR3 from the eld of air pollution modelling. This has led to the use of special time integration techniques. This paper...
متن کاملAnalysis of Air Pollution
This research paper is an attempt towards analyzing real time air pollution data collected by PAQS sensor devices from some key locations in Bangalore. Air pollution in most of the metropolitan cities in India is turning out to be a major threat to our environment and hazardous to our health. Many infections and diseases related to lungs and throat are caused by the polluted air we breathe. The...
متن کاملAir Pollution Analysis using Ontologies and Regression Models
Rapidly throughout the world economy, "the expansive Web" in the "world" explosive growth, rapidly growing market characterized by short product cycles exists and the demand for increased flexibility as well as the extensive use of a new data vision managed data society. A new socio-economic system that relies more and more on movement and allocation results in data whose daily existence, refin...
متن کاملAnalysis of Dynamic Brain Networks Using VAR Models
In neuroscience it became popular to represent neuroimaging data from the human brain as networks. The edges of these (weighted) graphs represent a spatio-temporal similarity between paired data channels. The temporal series of graphs is commonly averaged to a weighted graph of which edge weights are eventually thresholded. Graph measures are then applied to this network to correlate them, e.g....
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Mathematics and Statistics
سال: 2005
ISSN: 1549-3644
DOI: 10.3844/jmssp.2005.258.267