000			01697nam a22002417a 4500
005			20241125151643.0
008			241124b |||||||| |||| 00| 0 eng d
020			_a9783030637590
082			_a519.243 _bMOR
100			_aMorales, Domingo _918179
245			_aA course on small area estimation and mixed models: _bmethods, theory and applications in R
260			_bSpringer _aSwitzerland _c2021
300			_axx, 599 p.
365			_aEUR _b79.99
490			_aStatistics for Social and Behavioral Sciences
520			_aThis advanced textbook explores small area estimation techniques, covers the underlying mathematical and statistical theory and offers hands-on support with their implementation. It presents the theory in a rigorous way and compares and contrasts various statistical methodologies, helping readers understand how to develop new methodologies for small area estimation. It also includes numerous sample applications of small area estimation techniques. The underlying R code is provided in the text and applied to four datasets that mimic data from labor markets and living conditions surveys, where the socioeconomic indicators include the small area estimation of total unemployment, unemployment rates, average annual household incomes and poverty indicators. Given its scope, the book will be useful for master and PhD students, and for official and other applied statisticians. (https://link.springer.com/book/10.1007/978-3-030-63757-6)
650			_aEstimation techniques _918880
650			_aStatistical methodology _918881
700			_aEsteban, Maria Dolores _918890
700			_aPerez, Agustin _918891
700			_aHobza, Tomas _918892
942			_cBK _2ddc
999			_c7638 _d7638