Titlebook: Statistical Methods and Applications in Forestry and Environmental Sciences; Girish Chandra,Raman Nautiyal,Hukum Chandra Book 2020 The Edi

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Development in Copula Applications in Forestry and Environmental Sciences,ing power. They are the powerful instruments to model-dependent structures between various complex correlated variables in environmental sciences. This chapter studies the development of copula models and its applications in the area of environmental sciences. It reviews the literature on the types

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Small Area Estimation for Skewed Semicontinuous Spatially Structured Responses,ple. In such cases, model-based small area estimators can be used to improve the accuracy of the estimates by borrowing information from other sub-populations. Frequently, in surveys related to agriculture, forestry or the environment, we are interested in analyzing continuous variables which are ch

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Small Area Estimation for Total Basal Cover in the State of Maharashtra in India, herbs for the state of Maharashtra in India. All seven forest types are defined as small areas. The analysis uses the data of survey conducted by Tropical Forest Research Institute, Jabalpur, India during the Indian Council of Forestry Research and Education’s revisiting of forestry types of India

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Estimation of Abundance of Asiatic Elephants in Elephant Reserves of Kerala State, India,dia. Three different sampling methods, sampling block count and direct sighting and dung survey using line transect sampling were used. Dung survey appeared to be better method than the others. The total estimated elephant population using dung survey for all the reserves was 7490 with 95% confidenc

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2364-6748 studies related to forestry and environmental sciences.Inclu.This book presents recent developments in statistical methodologies with particular relevance to applications in forestry and environmental sciences. It discusses important methodologies like ranked set sampling, adaptive cluster sampling,

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Improved Nonparametric Estimation Using Partially Ordered Sets,od performance of the new CDF estimators, we further derive two mean estimators for partially ordered sets. Our numerical results from both simulation and real data show that the proposed estimators outperform their competitors provided that the quality of ranking is not low.