01582oam 2200349 a 450 991070322300332120120509125757.0(CKB)4950000000096563(OCoLC)766008552(EXLCZ)99495000000009656320111202d2011 ua 0engurbn|||||||||txtrdacontentcrdamediacrrdacarrierProviding for consideration of the bill (H.R. 10) to amend chapter 8 of title 5, United States Code, to provide that major rules of the executive branch shall have no force or effect unless a joint resolution of approval is enacted into law, and for other purposes[electronic resource] (to accompany H. Res. 479)[Washington, D.C.] :[U.S. G.P.O.],[2011]1 online resource (6 pages)Report / 112th Congress, 1st session, House of Representatives ;112-311Title from title screen (viewed on Dec. 2, 2011)."December 1, 2011."PROVIDING FOR CONSIDERATION OF THE BILL Executive powerUnited StatesExecutive powerGPOGPOGPOBOOK9910703223003321Providing for consideration of the bill (H.R. 10) to amend chapter 8 of title 5, United States Code, to provide that major rules of the executive branch shall have no force or effect unless a joint resolution of approval is enacted into law, and for other purposes3442654UNINA04914nam 22006854a 450 991101950850332120200520144314.0978661027206897812802720661280272066978047029876304702987669780470866986047086698597804708669930470866993(CKB)1000000000018899(EBL)210562(OCoLC)475919098(SSID)ssj0000161433(PQKBManifestationID)11151953(PQKBTitleCode)TC0000161433(PQKBWorkID)10198872(PQKB)11543221(MiAaPQ)EBC210562(Perlego)2760791(EXLCZ)99100000000001889920040402d2004 uy 0engur|n|---|||||txtccrGeneralized least squares /Takeaki Kariya, Hiroshi KurataChichester, West Sussex, England ;Hoboken, NJ Wileyc20041 online resource (313 p.)Wiley series in probability and statisticsDescription based upon print version of record.9780470866979 0470866977 Includes bibliographical references (p. 281-286) and index.Contents; Preface; 1 Preliminaries; 1.1 Overview; 1.2 Multivariate Normal and Wishart Distributions; 1.3 Elliptically Symmetric Distributions; 1.4 Group Invariance; 1.5 Problems; 2 Generalized Least Squares Estimators; 2.1 Overview; 2.2 General Linear Regression Model; 2.3 Generalized Least Squares Estimators; 2.4 Finiteness of Moments and Typical GLSEs; 2.5 Empirical Example: CO[sub(2)] Emission Data; 2.6 Empirical Example: Bond Price Data; 2.7 Problems; 3 Nonlinear Versions of the Gauss-Markov Theorem; 3.1 Overview; 3.2 Generalized Least Squares Predictors3.3 A Nonlinear Version of the Gauss-Markov Theorem in Prediction3.4 A Nonlinear Version of the Gauss-Markov Theorem in Estimation; 3.5 An Application to GLSEs with Iterated Residuals; 3.6 Problems; 4 SUR and Heteroscedastic Models; 4.1 Overview; 4.2 GLSEs with a Simple Covariance Structure; 4.3 Upper Bound for the Covariance Matrix of a GLSE; 4.4 Upper Bound Problem for the UZE in an SUR Model; 4.5 Upper Bound Problems for a GLSE in a Heteroscedastic Model; 4.6 Empirical Example: CO[sub(2)] Emission Data; 4.7 Problems; 5 Serial Correlation Model; 5.1 Overview5.2 Upper Bound for the Risk Matrix of a GLSE5.3 Upper Bound Problem for a GLSE in the Anderson Model; 5.4 Upper Bound Problem for a GLSE in a Two-equation Heteroscedastic Model; 5.5 Empirical Example: Automobile Data; 5.6 Problems; 6 Normal Approximation; 6.1 Overview; 6.2 Uniform Bounds for Normal Approximations to the Probability Density Functions; 6.3 Uniform Bounds for Normal Approximations to the Cumulative Distribution Functions; 6.4 Problems; 7 Extension of Gauss-Markov Theorem; 7.1 Overview; 7.2 An Equivalence Relation on S(n); 7.3 A Maximal Extension of the Gauss-Markov Theorem7.4 Nonlinear Versions of the Gauss-Markov Theorem7.5 Problems; 8 Some Further Extensions; 8.1 Overview; 8.2 Concentration Inequalities for the Gauss-Markov Estimator; 8.3 Efficiency of GLSEs under Elliptical Symmetry; 8.4 Degeneracy of the Distributions of GLSEs; 8.5 Problems; 9 Growth Curve Model and GLSEs; 9.1 Overview; 9.2 Condition for the Identical Equality between the GME and the OLSE; 9.3 GLSEs and Nonlinear Version of the Gauss-Markov Theorem; 9.4 Analysis Based on a Canonical Form; 9.5 Efficiency of GLSEs; 9.6 Problems; A: AppendixA.1 Asymptotic Equivalence of the Estimators of θ in the AR(1) Error Model and Anderson ModelBibliography; Index; A; B; C; D; E; G; H; I; K; L; M; N; O; R; S; U; WGeneralised Least Squares adopts a concise and mathematically rigorous approach. It will provide an up-to-date self-contained introduction to the unified theory of generalized least squares estimations, adopting a concise and mathematically rigorous approach. The book covers in depth the 'lower and upper bounds approach', pioneered by the first author, which is widely regarded as a very powerful and useful tool for generalized least squares estimation, helping the reader develop their understanding of the theory. The book also contains exercises at the end of each chapter and applicatiWiley series in probability and statistics.Least squaresLeast squares.511/.42Kariya Takeaki102096Kurata Hiroshi1967-525079MiAaPQMiAaPQMiAaPQBOOK9911019508503321Generalized least squares822770UNINA00900nam 2200277 450 99669707660331620260108164222.020260108d1968----km y0itay5003 baitaITy 00 y<<L'>>evoluzione dell'istituto regionale nell'esperienza delle Regioni a statuto specialeUmberto Allegretti[S.l.s.n.1968]23 p.25 cmTitolo della copertinaEstratto da: Esperienze amministrative, n. 13-14 (lug. 1968)SBNCF3ALLEGRETTI,Umberto<1934- >140411ITcbaREICAT996697076603316XVI.7.Misc. 13221340 FBUOXVI.7.Misc.BKFBUOEvoluzione dell'istituto regionale nell'esperienza delle Regioni a statuto speciale4519841UNISA