00976nam0 2200289 450 00001935420081117100658.0047026695320081117d1979----km-y0itay50------baengUSy-------001yyQuasiconformal mappings and Riemann surfacesSamuil L. Krushkal'edited by Irvin KraWashingtonWiley and Sons1979XII, 319 p.23 cmScripta series in mathematics2001Scripta series in mathematicsQuasiconformal mappings and Riemann surfaces34020Superfici di Riemann515.920Funzioni di variabile complessaKrushkal',Samuil L.632367Kra,IrwinITUNIPARTHENOPE20081030RICAUNIMARC000019354M 515.9/1M 522DSA2008Quasiconformal mappings and Riemann surfaces34020UNIPARTHENOPE03490nam 22005535 450 991035024180332120250408081402.0981-13-9314-110.1007/978-981-13-9314-3(CKB)5340000000061457(MiAaPQ)EBC5925036(DE-He213)978-981-13-9314-3(PPN)238487016(EXLCZ)99534000000006145720190718d2019 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierApplications of Regression Techniques /by Manoranjan Pal, Premananda Bharati1st ed. 2019.Singapore :Springer Nature Singapore :Imprint: Springer,2019.1 online resource (181 pages)981-13-9313-3 Chapter 1: Introduction to Regression Analysis and an overview of the techniques used in the book -- Chapter 2: Regression Decomposition Technique towards Finding Intra-Household Gender Bias of Calorie Consumption -- Chapter 3: Estimation of Poverty Rates by Calorie Decomposition Method -- Chapter 4: Estimating Calorie-Poverty Rates through Regression -- Chapter 5: Contribution of Regressors: A Set Theoretic Approach -- Chapter 6: Estimation of Hidden Markov Chain through Regression -- Chapter 7: Finding Geometric Mean and Aggregate Growth Rate through regression -- Chapter 8: Summary and Discussions.This book discusses the need to carefully and prudently apply various regression techniques in order to obtain the full benefits. It also describes some of the techniques developed and used by the authors, presenting their innovative ideas regarding the formulation and estimation of regression decomposition models, hidden Markov chain, and the contribution of regressors in the set-theoretic approach, calorie poverty rate, and aggregate growth rate. Each of these techniques has applications that address a number of unanswered questions; for example, regression decomposition techniques reveal intra-household gender inequalities of consumption, intra-household allocation of resources and adult equivalent scales, while Hidden Markov chain models can forecast the results of future elections. Most of these procedures are presented using real-world data, and the techniques can be applied in other similar situations. Showing how difficult questions can be answered by developing simple models with simple interpretation of parameters, the book is a valuable resource for students and researchers in the field of model building.StatisticsEconometricsStatisticsStatistics in Business, Management, Economics, Finance, InsuranceEconometricsStatistical Theory and MethodsStatistics.Econometrics.Statistics.Statistics in Business, Management, Economics, Finance, Insurance.Econometrics.Statistical Theory and Methods.519.536Pal Manoranjanauthttp://id.loc.gov/vocabulary/relators/aut781840Bharati Premanandaauthttp://id.loc.gov/vocabulary/relators/autMiAaPQMiAaPQMiAaPQBOOK9910350241803321Applications of Regression Techniques2508223UNINA