On Efficient Confidence Intervals via Meta-Analysis of Geo-Spatial Data for Global Warming Control in Decision-Makers? Setup for Project-Managers
by Ashok Sahai and
With the advent of Geographical Information System (GIS) & Global Positioning System (GPS), it is quite common to have access to geospatial temporal/spatial panel data for analyses in a meta-data setup. In this context, researchers often employ pooling methods to evaluate the efficacy of meta-data analysis such as used to combine individual study results is the fixed-effects model, which assumes that a true-effect is equal for all studies. An alternative, and intuitively-more-appealing method, is the random-effects model. This paper addresses the efficient confidence-interval (in decision makers? set-up for the „project-managers?) estimation problem, using this method in the aforesaid meta-data setup of the „Geospatial Data? at hand. As compared to the latest state-of-art of such estimation-strategies in the literature up-to-date, this paper aims at a much more efficient estimation strategy furthering the gainful use of the „Geospatial Panel-Data? in the Global/Continental/Regional/National contexts. This „Statistical Theme? is, as such, equally gainfully applicable to any area of application in the present world-order at large inasmuch as the “Data-Mapping” in any context, e.g. the topically significant one of “Global Environmental Pollution-Mitigation for Arresting the Critical phenomenon of Global Warming”, etc; are tackle-able more readily, as the impactful advances in the “GIS & GPS” technologies have led to the concept of “Managing Global Village” in terms of „Geospatial Meta-Data?. This last fact has been seminal to special zeal-n-motivation to the authors to have worked for this improved paper containing rather a much more efficient strategy of confidence-interval estimation for decision-making team of managers for any impugned area of application.
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Abdallah Abdelfattah, firstname.lastname@example.org
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