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Strong rigidity of locally symmetric spaces
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ISBN: 0691081360 1400881838 9780691081366 Year: 1973 Volume: 78 Publisher: Princeton : Princeton University Press,

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Abstract

Locally symmetric spaces are generalizations of spaces of constant curvature. In this book the author presents the proof of a remarkable phenomenon, which he calls "strong rigidity": this is a stronger form of the deformation rigidity that has been investigated by Selberg, Calabi-Vesentini, Weil, Borel, and Raghunathan.The proof combines the theory of semi-simple Lie groups, discrete subgroups, the geometry of E. Cartan's symmetric Riemannian spaces, elements of ergodic theory, and the fundamental theorem of projective geometry as applied to Tit's geometries. In his proof the author introduces two new notions having independent interest: one is "pseudo-isometries"; the other is a notion of a quasi-conformal mapping over the division algebra K (K equals real, complex, quaternion, or Cayley numbers). The author attempts to make the account accessible to readers with diverse backgrounds, and the book contains capsule descriptions of the various theories that enter the proof.

Keywords

Differential geometry. Global analysis --- Riemannian manifolds --- Symmetric spaces --- Rigidity (Geometry) --- 512 --- Lie groups --- Geometric rigidity --- Rigidity theorem --- Discrete geometry --- Spaces, Symmetric --- Geometry, Differential --- Manifolds, Riemannian --- Riemannian space --- Space, Riemannian --- Manifolds (Mathematics) --- Groups, Lie --- Lie algebras --- Topological groups --- Algebra --- Lie groups. --- Riemannian manifolds. --- Symmetric spaces. --- Rigidity (Geometry). --- 512 Algebra --- Addition. --- Adjoint representation. --- Affine space. --- Approximation. --- Automorphism. --- Axiom. --- Big O notation. --- Boundary value problem. --- Cohomology. --- Compact Riemann surface. --- Compact space. --- Conjecture. --- Constant curvature. --- Corollary. --- Counterexample. --- Covering group. --- Covering space. --- Curvature. --- Diameter. --- Diffeomorphism. --- Differentiable function. --- Dimension. --- Direct product. --- Division algebra. --- Ergodicity. --- Erlangen program. --- Existence theorem. --- Exponential function. --- Finitely generated group. --- Fundamental domain. --- Fundamental group. --- Geometry. --- Half-space (geometry). --- Hausdorff distance. --- Hermitian matrix. --- Homeomorphism. --- Homomorphism. --- Hyperplane. --- Identity matrix. --- Inner automorphism. --- Isometry group. --- Jordan algebra. --- Matrix multiplication. --- Metric space. --- Morphism. --- Möbius transformation. --- Normal subgroup. --- Normalizing constant. --- Partially ordered set. --- Permutation. --- Projective space. --- Riemann surface. --- Riemannian geometry. --- Sectional curvature. --- Self-adjoint. --- Set function. --- Smoothness. --- Stereographic projection. --- Subgroup. --- Subset. --- Summation. --- Symmetric space. --- Tangent space. --- Tangent vector. --- Theorem. --- Topology. --- Tubular neighborhood. --- Two-dimensional space. --- Unit sphere. --- Vector group. --- Weyl group. --- Riemann, Variétés de --- Lie, Groupes de --- Geometrie differentielle globale --- Varietes riemanniennes

Mathematical methods of statistics
Author:
ISBN: 0691005478 0691080046 1400883865 9780691080048 9780691005478 Year: 1946 Volume: 9 Publisher: Princeton : Princeton University Press,

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Abstract

In this classic of statistical mathematical theory, Harald Cramér joins the two major lines of development in the field: while British and American statisticians were developing the science of statistical inference, French and Russian probabilitists transformed the classical calculus of probability into a rigorous and pure mathematical theory. The result of Cramér's work is a masterly exposition of the mathematical methods of modern statistics that set the standard that others have since sought to follow. For anyone with a working knowledge of undergraduate mathematics the book is self contained. The first part is an introduction to the fundamental concept of a distribution and of integration with respect to a distribution. The second part contains the general theory of random variables and probability distributions while the third is devoted to the theory of sampling, statistical estimation, and tests of significance.

Keywords

Mathematical statistics --- 519.2 --- 519.2 Probability. Mathematical statistics --- Probability. Mathematical statistics --- Mathematics --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Statistical methods --- Statistique mathématique --- Mathematical statistics. --- Statistique mathématique --- Statistique mathématique. --- Distribution (théorie des probabilités) --- Distribution (Probability theory) --- A priori probability. --- Addition theorem. --- Additive function. --- Analysis of covariance. --- Arithmetic mean. --- Axiom. --- Bayes' theorem. --- Bias of an estimator. --- Binomial distribution. --- Binomial theorem. --- Bolzano–Weierstrass theorem. --- Borel set. --- Bounded set (topological vector space). --- Calculation. --- Cartesian product. --- Central moment. --- Characteristic function (probability theory). --- Characteristic polynomial. --- Coefficient. --- Commutative property. --- Confidence interval. --- Convergence of random variables. --- Correlation coefficient. --- Degeneracy (mathematics). --- Degrees of freedom (statistics). --- Diagram (category theory). --- Dimension. --- Distribution (mathematics). --- Distribution function. --- Empirical distribution function. --- Equation. --- Estimation theory. --- Estimation. --- Identity matrix. --- Independence (probability theory). --- Interval (mathematics). --- Inverse probability. --- Invertible matrix. --- Joint probability distribution. --- Laplace distribution. --- Lebesgue integration. --- Lebesgue measure. --- Lebesgue–Stieltjes integration. --- Likelihood function. --- Limit (mathematics). --- Linear regression. --- Logarithm. --- Logarithmic derivative. --- Logarithmic scale. --- Marginal distribution. --- Mathematical analysis. --- Mathematical induction. --- Mathematical theory. --- Mathematics. --- Matrix (mathematics). --- Maxima and minima. --- Measure (mathematics). --- Method of moments (statistics). --- Metric space. --- Minor (linear algebra). --- Moment (mathematics). --- Moment matrix. --- Normal distribution. --- Numerical analysis. --- Parameter. --- Parity (mathematics). --- Poisson distribution. --- Probability distribution. --- Probability theory. --- Probability. --- Proportionality (mathematics). --- Quantity. --- Random variable. --- Realization (probability). --- Riemann integral. --- Sample space. --- Sampling (statistics). --- Scientific notation. --- Series (mathematics). --- Set (mathematics). --- Set function. --- Sign (mathematics). --- Standard deviation. --- Statistic. --- Statistical Science. --- Statistical hypothesis testing. --- Statistical inference. --- Statistical regularity. --- Statistical theory. --- Subset. --- Summation. --- Theorem. --- Theory. --- Transfinite number. --- Uniform distribution (discrete). --- Variable (mathematics). --- Variance. --- Weighted arithmetic mean. --- Z-test. --- Distribution (théorie des probabilités)

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