000			03301nam a22005295i 4500
001			978-3-642-35443-4
003			DE-He213
005			20200421112542.0
007			cr nn 008mamaa
008			121214s2013 gw | s |||| 0|eng d
020			_a9783642354434 _9978-3-642-35443-4
024	7		_a10.1007/978-3-642-35443-4 _2doi
050		4	_aQ342
072		7	_aUYQ _2bicssc
072		7	_aCOM004000 _2bisacsh
082	0	4	_a006.3 _223
245	1	0	_aUncertainty Analysis in Econometrics with Applications _h[electronic resource] : _bProceedings of the Sixth International Conference of the Thailand Econometric Society TES'2013 / _cedited by Van-Nam Huynh, Vladik Kreinovich, Songsak Sriboonchitta, Komsan Suriya.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg : _bImprint: Springer, _c2013.
300			_aXVI, 319 p. 34 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aAdvances in Intelligent Systems and Computing, _x2194-5357 ; _v200
505	0		_aPart I Keynote Addresses -- Part II Fundamental Theory -- Part III Applications.
520			_aUnlike uncertain dynamical systems in physical sciences where models for prediction are somewhat given to us by physical laws, uncertain dynamical systems in economics need statistical models. In this context, modeling and optimization surface as basic ingredients for fruitful applications. This volume concentrates on the current methodology of copulas and maximum entropy optimization. This volume contains main research presentations at the Sixth International Conference of the Thailand Econometrics Society held at the Faculty of Economics, Chiang Mai University, Thailand, during January 10-11, 2013. It consists of keynote addresses, theoretical and applied contributions. These contributions to Econometrics are somewhat centered around the theme of Copulas and Maximum Entropy Econometrics. The method of copulas is applied to a variety of economic problems where multivariate model building and correlation analysis are needed. As for the art of choosing copulas in practical problems, the principle of maximum entropy surfaces as a potential way to do so. The state-of-the-art of Maximum Entropy Econometrics is presented in the first keynote address, while the second keynote address focusses on testing stationarity in economic time series data.
650		0	_aEngineering.
650		0	_aArtificial intelligence.
650		0	_aComputational intelligence.
650		0	_aEconometrics.
650	1	4	_aEngineering.
650	2	4	_aComputational Intelligence.
650	2	4	_aArtificial Intelligence (incl. Robotics).
650	2	4	_aEconometrics.
700	1		_aHuynh, Van-Nam. _eeditor.
700	1		_aKreinovich, Vladik. _eeditor.
700	1		_aSriboonchitta, Songsak. _eeditor.
700	1		_aSuriya, Komsan. _eeditor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783642354427
830		0	_aAdvances in Intelligent Systems and Computing, _x2194-5357 ; _v200
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-642-35443-4
912			_aZDB-2-ENG
942			_cEBK
999			_c58348 _d58348