LEADER 03662nam 22005295 450 001 9910350241803321 005 20200703102606.0 010 $a981-13-9314-1 024 7 $a10.1007/978-981-13-9314-3 035 $a(CKB)5340000000061457 035 $a(MiAaPQ)EBC5925036 035 $a(DE-He213)978-981-13-9314-3 035 $a(PPN)238487016 035 $a(EXLCZ)995340000000061457 100 $a20190718d2019 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aApplications of Regression Techniques /$fby Manoranjan Pal, Premananda Bharati 205 $a1st ed. 2019. 210 1$aSingapore :$cSpringer Singapore :$cImprint: Springer,$d2019. 215 $a1 online resource (181 pages) 311 $a981-13-9313-3 327 $aChapter 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. 330 $aThis 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. 606 $aStatistics  606 $aEconometrics 606 $aStatistics for Business, Management, Economics, Finance, Insurance$3https://scigraph.springernature.com/ontologies/product-market-codes/S17010 606 $aEconometrics$3https://scigraph.springernature.com/ontologies/product-market-codes/W29010 606 $aStatistical Theory and Methods$3https://scigraph.springernature.com/ontologies/product-market-codes/S11001 615 0$aStatistics . 615 0$aEconometrics. 615 14$aStatistics for Business, Management, Economics, Finance, Insurance. 615 24$aEconometrics. 615 24$aStatistical Theory and Methods. 676 $a519.536 700 $aPal$b Manoranjan$4aut$4http://id.loc.gov/vocabulary/relators/aut$0781840 702 $aBharati$b Premananda$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910350241803321 996 $aApplications of Regression Techniques$92508223 997 $aUNINA