LEADER 03785nam 22010213a 450 001 9910346844503321 005 20250203235432.0 010 $a9783038979654 010 $a3038979651 024 8 $a10.3390/books978-3-03897-965-4 035 $a(CKB)4920000000095195 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/54765 035 $a(ScCtBLL)68f369a3-04a0-4263-b60a-972b52e39bc1 035 $a(OCoLC)1118522558 035 $a(oapen)doab54765 035 $a(EXLCZ)994920000000095195 100 $a20250203i20192019 uu 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aNonparametric Econometric Methods and Application$fThanasis Stengos 210 $cMDPI - Multidisciplinary Digital Publishing Institute$d2019 210 1$aBasel, Switzerland :$cMDPI,$d2019. 215 $a1 electronic resource (224 p.) 311 08$a9783038979647 311 08$a3038979643 330 $aThe present Special Issue collects a number of new contributions both at the theoretical level and in terms of applications in the areas of nonparametric and semiparametric econometric methods. In particular, this collection of papers that cover areas such as developments in local smoothing techniques, splines, series estimators, and wavelets will add to the existing rich literature on these subjects and enhance our ability to use data to test economic hypotheses in a variety of fields, such as financial economics, microeconomics, macroeconomics, labor economics, and economic growth, to name a few. 610 $adiscrete duration models 610 $avolatility feedback effect 610 $asemiparametric estimation 610 $anonparametric method 610 $aGLS detrending 610 $afunctional coefficients 610 $apurified implied volatility 610 $acountry competitiveness index 610 $anonparametric frontiers 610 $aefficiency 610 $amaterials balance condition 610 $apanel data 610 $aDirichlet process prior 610 $aclassification 610 $aindicators 610 $aKendall?s tau 610 $arealised volatility 610 $aMalmquist productivity index 610 $aconditional dependence index 610 $awavelet 610 $adependent Bayesian nonparametrics 610 $aTFP growth 610 $aSolow economic growth convergence model 610 $aunit root testing 610 $anonparametric 2SLS estimator 610 $arandom forests 610 $acompetitiveness 610 $aslice sampling 610 $aintegrated difference kernel estimator 610 $amaximum score estimator 610 $aheterogeneous autoregressive model 610 $ageneralized additive models 610 $aMonte Carlo 610 $atensor products 610 $acubic spline penalty 610 $aM-estimation 610 $anonparametric copula 610 $aleverage effect 610 $aconditional quantile function 610 $aemissions 610 $aefficient semiparamteric estimation 610 $aDEA 610 $atail dependence index 610 $adifference kernel estimator 610 $anonparametric threshold regression 610 $amachine learning 610 $afactors 610 $alocal linear regression 610 $aEuropean Union 610 $afinancial development 610 $aseries estimator 610 $aproduction efficiency 700 $aStengos$b Thanasis$0732518 801 0$bScCtBLL 801 1$bScCtBLL 906 $aBOOK 912 $a9910346844503321 996 $aNonparametric Econometric Methods and Application$93028068 997 $aUNINA LEADER 03033nam 22006015 450 001 9910917190203321 005 20251022152020.0 010 $a981-9784-60-3 024 7 $a10.1007/978-981-97-8460-8 035 $a(CKB)36959124200041 035 $a(MiAaPQ)EBC31824043 035 $a(Au-PeEL)EBL31824043 035 $a(OCoLC)1478695807 035 $a(DE-He213)978-981-97-8460-8 035 $a(EXLCZ)9936959124200041 100 $a20241210d2024 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aGenerative AI: Current Trends and Applications /$fedited by Khalid Raza, Naeem Ahmad, Deepak Singh 205 $a1st ed. 2024. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2024. 215 $a1 online resource (0 pages) 225 1 $aStudies in Computational Intelligence,$x1860-9503 ;$v1177 311 08$a981-9784-59-X 327 $aGenerative AI Fundamentals and Evolution -- Fundamentals of Encoders and Decoders in Generative AI -- Generative AI Applications Development using OpenAI -- Generative AI for Smart Data Analytics -- Detection of Real vs Fake Images on Social Media through Generative Adversarial Networks. 330 $aThis book focuses on the latest advancements in generative AI, including state-of-the-art techniques and models that are pushing the boundaries of what is possible. It covers recent developments in areas such as generative AI models, transfer learning, and natural language processing (NLP) highlighting their potential to revolutionize content generation and creative applications including OpenAI, LangChain, NLTK, and their practical implementations across diverse domains. The volume provides insights into emerging research areas, novel architectures, and innovative approaches in generative AI, giving searchers a glimpse into the exciting future of the field. 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