Statistical Methods and Applications in Forestry and Environmental Sciences / Forum for Interdisciplinary Mathematics (PDF)

GIRISH CHANDRA, PhD, is a scientist at the Division of Forestry Statistics, Indian Council of Forestry Research and Education (ICFRE), Dehradun, India. He previously worked at the Tropical Forest Research Institute, Jabalpur, and Central Agricultural University, Sikkim. He has published over 25 research papers in various respected journals and one book. He was awarded the Cochran-Hansen Prize 2017 by the International Association of Survey Statisticians, the Netherlands. He is also a recipient of the ICFRE Outstanding Research Award 2018 and Young Scientist Award in Mathematical Sciences from the Government of Uttarakhand. He has presented his research papers at various international conferences and workshops in India and abroad. He has organised two national conferences and is a member of various scientific institutions, including the International Statistical Institute, International Indian Statistical Association, Computational and Methodological Statistics, and the Indian Society for Probability and Statistics. His research interests include sample surveys, probability theory and numerical methods.
RAMAN NAUTIYAL is head of the Division of Forestry Statistics, ICFRE. He previously worked as a scientist at the Institute of Forest Genetic and Tree Breeding, Coimbatore. He has handled projects on forestry statistics funded by International Tropical Timber Organization, Japan, Central Statistical Office, NOVOD Board, and the Ministry of Environment, Forest and Climate Change, Government of India.
HUKUM CHANDRA is an ICAR National Fellow at the ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India. He holds a PhD from the University of Southampton, United Kingdom, and completed his postdoctoral research at the University of Wollongong, Australia. His main research areas include sample survey design and analysis, small area estimation, bootstrap methods, statistical modelling and data analysis, and statistical methodology for the improvement of