LEADER 07363nam 22016815 450 001 9910154750703321 005 20190708092533.0 010 $a1-4008-8201-X 024 7 $a10.1515/9781400882014 035 $a(CKB)3710000000622805 035 $a(MiAaPQ)EBC4738645 035 $a(DE-B1597)468025 035 $a(OCoLC)979911332 035 $a(DE-B1597)9781400882014 035 $a(EXLCZ)993710000000622805 100 $a20190708d2016 fg 101 0 $aeng 135 $aurcnu|||||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 00$aAdvances in Game Theory. (AM-52), Volume 52 /$fMelvin Dresher, Albert William Tucker, Lloyd S. Shapley 210 1$aPrinceton, NJ : $cPrinceton University Press, $d[2016] 210 4$d©1964 215 $a1 online resource (693 pages) $cillustrations 225 0 $aAnnals of Mathematics Studies ;$v302 311 $a0-691-07902-1 320 $aIncludes bibliographical references. 327 $tFrontmatter -- $tPreface / $rDresher, M. / Shapley, L. S. / Tucker, A. W. -- $tContents -- $t1. Some Topics in Two-Person Games / $rShapley, L. S. -- $t2. Games With a Random Move / $rRestrepo, Rodrigo A. -- $t3. A Search Game / $rJohnson, Selmer M. -- $t4. The Rendezvous Value of a Metric Space / $rGross, O. -- $t5. Generalized Gross Substitutability and Extremization / $rNikaidò, Hukukane -- $t6. Adaptive Competitive Decision / $rRosenfeld, Jack L. -- $t7. Infinite Games of Perfect Information / $rDavis, Morton -- $t8. Continuous Games of Perfect Information / $rMycielski, Jan -- $t9. A Theory of Pursuit and Evasion / $rRyll-Nardzewski, C. -- $t10. A Variational Approach to Differential Games / $rBerkovitz, Leonard D. -- $t11. A Differential Game Without Pure Strategy Solutions on an Open Set / $rBerkovitz, Leonard D. -- $t12. The Convergence Problem for Differential Games, II / $rFleming, Wendell H. -- $t13. Markov Games / $rZachrisson, Lars Erik -- $t14. Homogeneous Games, III / $rIsbell, J. R. -- $t15. Solutions of Compound Simple Games / $rShapley, L. S. -- $t16. The Tensor Composition of Nonnegative Games / $rOwen, Guillermo -- $t17. On the Cardinality of Solutions of Four-Person Constant- Sum Games / $rGalmarino, Alberto Raul -- $t18. The Doubly Discriminatory Solutions of the Four-Person Constant-Sum Game / $rHebert, Michael H. -- $t19. Three-Person Cooperative Games Without Side Payments / $rStearns, R. E. -- $t20. Some Thoughts on the Theory of Cooperative Games / $rJentzsch, Gerd -- $t21. The Bargaining Set for Cooperative Games / $rAumann, Robert J. / Maschler, Michael -- $t22. Stable Payoff Configurations for Quota Games / $rMaschler, Michael -- $t23. On the Bargaining Set M0 of m-Quota Games / $rPeleg, Bezalel -- $t24. A Property of Stability Possessed by Certain Imputations / $rRadstrom, Hans -- $t25. Coalition Bargaining in n-Person Games / $rNering, Evar D. -- $t26. The n-Person Bargaining Game / $rMiyasawa, Koichi -- $t27. Valuation of n-Person Games / $rSelten, Reinhard -- $t28. Mixed and Behavior Strategies in Infinite Extensive Games / $rAumann, Robert J . -- $t29. A General Solution for Finite Noncooperative Games Based on Risk-Dominance / $rHarsanyi, John C. 330 $aThe description for this book, Advances in Game Theory. (AM-52), Volume 52, will be forthcoming. 410 0$aAnnals of mathematics studies ;$vNumber 52. 606 $aGame theory 610 $aAlmost surely. 610 $aAutomorphism. 610 $aAxiom. 610 $aBasis (linear algebra). 610 $aBayesian probability. 610 $aBig O notation. 610 $aBounded set (topological vector space). 610 $aCalculation. 610 $aCartesian product. 610 $aCharacteristic function (probability theory). 610 $aComplete theory. 610 $aConditional probability distribution. 610 $aContinuous function (set theory). 610 $aContinuum hypothesis. 610 $aCooperative game. 610 $aCoset. 610 $aCounterexample. 610 $aCumulative distribution function. 610 $aDecision rule. 610 $aDecision-making. 610 $aDeterminacy. 610 $aDiagram (category theory). 610 $aDifferential game. 610 $aDistribution function. 610 $aDyadic rational. 610 $aEquation solving. 610 $aEquation. 610 $aEquilibrium point. 610 $aEstimation. 610 $aExistence theorem. 610 $aFamily of sets. 610 $aFoundations of mathematics. 610 $aFunction (mathematics). 610 $aFundamental theorem. 610 $aGame show. 610 $aHamilton?Jacobi equation. 610 $aHarmonic function. 610 $aIndependence (probability theory). 610 $aInequality (mathematics). 610 $aInfimum and supremum. 610 $aInitial value problem. 610 $aInvertible matrix. 610 $aJacobian matrix and determinant. 610 $aJoint probability distribution. 610 $aLinear inequality. 610 $aLinear map. 610 $aLinear programming. 610 $aLipschitz continuity. 610 $aMarkov chain. 610 $aMarkov process. 610 $aMarkov property. 610 $aMathematical analysis. 610 $aMathematical economics. 610 $aMathematical induction. 610 $aMathematical optimization. 610 $aMatrix (mathematics). 610 $aMinimax theorem. 610 $aMinor (linear algebra). 610 $aMutual exclusivity. 610 $aN-vector. 610 $aOpen set. 610 $aOutcome (probability). 610 $aParity (mathematics). 610 $aPartially ordered set. 610 $aPayment. 610 $aPermutation. 610 $aPreference (economics). 610 $aPrime number. 610 $aPrimitive root modulo n. 610 $aProbability distribution function. 610 $aProbability distribution. 610 $aProbability measure. 610 $aProbability. 610 $aQuantifier (logic). 610 $aQuantity. 610 $aRandom variable. 610 $aRandomization. 610 $aRanking (information retrieval). 610 $aRepresentation theory. 610 $aSample space. 610 $aScientific notation. 610 $aSearch game. 610 $aSet (mathematics). 610 $aShapley value. 610 $aSimultaneous equations. 610 $aSkew-symmetric matrix. 610 $aSolution concept. 610 $aSpecial case. 610 $aStrategy (game theory). 610 $aSubset. 610 $aSummation. 610 $aSuperadditivity. 610 $aSylow theorems. 610 $aTheorem. 610 $aTheory of Games and Economic Behavior. 610 $aTheory. 610 $aTopology. 610 $aUtility. 610 $aVariable (mathematics). 610 $aWeighted arithmetic mean. 615 0$aGame theory. 676 $a512.8 702 $aDresher$b Melvin, 702 $aShapley$b Lloyd S., 702 $aTucker$b Albert William, 801 0$bDE-B1597 801 1$bDE-B1597 906 $aBOOK 912 $a9910154750703321 996 $aAdvances in Game Theory. (AM-52), Volume 52$92788317 997 $aUNINA LEADER 08201nam 22006975 450 001 9910957358803321 005 20250813214906.0 010 $a1-4612-0921-8 024 7 $a10.1007/978-1-4612-0921-8 035 $a(CKB)3400000000089322 035 $a(SSID)ssj0001295892 035 $a(PQKBManifestationID)11777922 035 $a(PQKBTitleCode)TC0001295892 035 $a(PQKBWorkID)11348362 035 $a(PQKB)11630470 035 $a(DE-He213)978-1-4612-0921-8 035 $a(MiAaPQ)EBC3074088 035 $a(PPN)237993236 035 $a(EXLCZ)993400000000089322 100 $a20121227d1992 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aApplied Multivariate Data Analysis $eVolume II: Categorical and Multivariate Methods /$fby J.D. Jobson 205 $a1st ed. 1992. 210 1$aNew York, NY :$cSpringer New York :$cImprint: Springer,$d1992. 215 $a1 online resource (XXIX, 732 p.) 225 1 $aSpringer Texts in Statistics,$x2197-4136 300 $aIncludes index. 311 08$a0-387-97804-6 311 08$a1-4612-6947-4 327 $a6 Contingency Tables -- 6.1 Multivariate Data Analysis Data Matrices and Measurement Scales -- 6.2 Two-Dimensional Contingency Tables -- 6.3 Multidimensional Contingency Tables -- 6.4 The Weighted Least Squares Approach -- Cited Literature and References -- Exercises for Chapter 6 -- Questions for Chapter 6 -- 7 Multivariate Distributions Inference Regression and Canonical Correlation -- 7.1 Multivariate Random Variables and Samples -- 7.2 The Multivariate Normal Distribution -- 7.3 Testing for Normality Outliers and Robust Estimation -- 7.4 Inference for the Multivariate Normal -- 7.5 Multivariate Regression and Canonical Correlation -- Cited Literature and References -- Exercises for Chapter 7 -- Questions for Chapter 7 -- 8 Manova Discriminant Analysis and Qualitative Response Models -- 8.1 Multivariate Analysis of Variance -- 8.2 Discriminant Analysis -- 8.3 Qualitative Response Regression Models and Logistic Regression -- 9 Principal Components Factors and Correspondence Analysis -- 9.1 Principal Components -- 9.2 The Exploratory Factor Analysis Model -- 9.3 Singular Value Decomposition and Matrix Approximation -- 9.4 Correspondence Analysis -- Cited Literature and References -- Exercises for Chapter 9 -- Questions for Chapter 9 -- 10 Cluster Analysis and Multidimensional Scaling -- 10.1 Proximity Matrices Derived from Data Matrices -- 10.2 Cluster Analysis -- 10.3 Multidimensional Scaling -- Cited Literature and References -- Exercises for Chapter 10 -- Questions for Chapter 10 -- 1. Matrix Algebra -- 1.1 Matrices -- Matrix -- Transpose of a Matrix -- Row Vector and Column Vector -- Square Matrix -- Symmetric Matrix -- Diagonal Elements -- Trace of a Matrix -- Null or Zero Matrix -- Identity Matrix -- Diagonal Matrix -- Submatrix -- 1.2 Matrix Operations -- Equality of Matrices -- Addition of Matrices -- Additive Inverse -- Scalar Multiplication of a Matrix -- Product of Two Matrices -- Multiplicative Inverse -- Idempotent Matrix -- Kronecker Product -- 1.3 Determinants and Rank -- Determinant -- Nonsingular -- Relation Between Inverse -- and Determinant -- Rank of a Matrix -- 1.4 Quadratic Forms and Positive Definite Matrices -- Quadratic Form -- Congruent Matrix -- Positive Definite -- Positive Semidefinite -- Negative Definite -- Non-negative Definite -- 1.5 Partitioned Matrices -- Product of Partitioned Matrices -- Inverse of a Parti-tioned Matrix -- Determinant of a Partitioned Matrix -- 1.6 Expectations of Random Matrices -- 1.7 Derivatives of Matrix Expressions -- 2. Linear Algebra -- 2.1 Geometric Representation for Vectors -- n Dimensional Space -- Directed Line Segment -- Coordinates -- Addition of Vectors -- Scalar Multiplication -- Length of a Vector -- Angle Between Vectors -- Orthogonal Vectors -- Projection -- 2.2 Linear Dependence And Linear Transformations -- Linearly Dependent Vectors -- Linearly Independent Vectors -- Basis for an n-Dimensional Space -- Generation of a Vector Space and Rank of a Matrix -- Linear Transformation -- Orthogonal Transformation -- Rotation -- Orthogonal Matri -- 2.3 Systems of Equations -- Solution Vector for a System of Equations -- Homoge-neous Equations ? Trivial and Nontrivial Solutions -- 2.4 Column Spaces -- Projection Operators and Least -- Squares -- Column Space -- Orthogonal Complement -- Projection -- Ordinary Least Squares Solution Vector -- Idempotent Matrix ? Projection Operator -- 3. Eigenvalue Structure and Singular Value Decomposition -- 3.1 Eigenvalue Structure for Square Matrices -- Eigenvalues and Eigenvectors -- Characteristic Polynomial -- Characteristic Roots -- Latent Roots -- Eigen-values -- Eigenvalues and Eignevectors for Real Symmetric Matrices and SomeProperties -- Spectral Decomposition -- Matrix Approximation -- Eigenvalues for Nonnegative Definite Matrices -- 3.2 Singular Value Decomposition -- Left and Right Singular Vectors -- Complete Singular Value Decomposition -- Generalized Singular Value Decomposition -- Relationship to Spectral Decomposition and Eigenvalues -- Data Appendix For Volume II -- Data Set V1 -- Data Set V2 -- Data Set V3 -- Data Set V4 -- Data Set V5 -- Data Set V6 -- Data Set V7 -- Data Set V8 -- Data Set V9 -- Data Set V10 -- Data Set Vll -- Data Set V12 -- Data Set V13 -- Data Set V14 -- Data Set V15 -- Data Set V16 -- Data Set V17 -- Data Set V18 -- Data Set V19 -- Data Set V20 -- Data Set V21 -- Data Set V22 -- Table V1 -- Table V2 -- Table V3 -- Table V4 -- Table V5 -- Table V6 -- Table V7 -- Table V8 -- Table V9 -- Table V10 -- Table V11 -- Table V12 -- Table V13 -- Table V14 -- Table V15 -- Table V16 -- Table V17 -- Table V18 -- Table V19 -- Table V20 -- Table V21 -- Table V22 -- Author Index. 330 $aA Second Course in Statistics The past decade has seen a tremendous increase in the use of statistical data analysis and in the availability of both computers and statistical software. Business and government professionals, as well as academic researchers, are now regularly employing techniques that go far beyond the standard two-semester, introductory course in statistics. Even though for this group of users shorl courses in various specialized topics are often available, there is a need to improve the statistics training of future users of statistics while they are still at colleges and universities. In addition, there is a need for a survey reference text for the many practitioners who cannot obtain specialized courses. With the exception of the statistics major, most university students do not have sufficient time in their programs to enroll in a variety of specialized one-semester courses, such as data analysis, linear models, experimental de­ sign, multivariate methods, contingency tables, logistic regression, and so on. There is a need for a second survey course that covers a wide variety of these techniques in an integrated fashion. It is also important that this sec­ ond course combine an overview of theory with an opportunity to practice, including the use of statistical software and the interpretation of results obtained from real däta. 410 0$aSpringer Texts in Statistics,$x2197-4136 606 $aMathematics 606 $aStatistics 606 $aBiometry 606 $aMedical sciences 606 $aApplications of Mathematics 606 $aStatistics in Business, Management, Economics, Finance, Insurance 606 $aBiostatistics 606 $aHealth Sciences 615 0$aMathematics. 615 0$aStatistics. 615 0$aBiometry. 615 0$aMedical sciences. 615 14$aApplications of Mathematics. 615 24$aStatistics in Business, Management, Economics, Finance, Insurance. 615 24$aBiostatistics. 615 24$aHealth Sciences. 676 $a519 700 $aJobson$b J.D$4aut$4http://id.loc.gov/vocabulary/relators/aut$0103510 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910957358803321 996 $aApplied multivariate data analysis$9438320 997 $aUNINA