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Approximation of integrals over asymptotic sets with applications to statistics and probability PDF

325 Pages·2003·2.163 MB·English
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Preview Approximation of integrals over asymptotic sets with applications to statistics and probability

Approximation 3 of 0 0 IInntteeggrraallss 2 c e D oovveerr 5 aassyymmppttoottiicc sseettss 1 v 2 3 wwiitthh aapppplliiccaattiioonnss ttoo 1 2 1 SSttaattiissttiiccss aanndd PPrroobbaabbiilliittyy 3 0 / R P . h t a m : v i X r a Philippe Barbe Philippe Barbe APPROXIMATION of INTEGRALS over ASYMPTOTIC SETS with applications to Statistics and Probability Philippe Barbe CNRS 90 Rue de Vaugirard 75006 PARIS FRANCE c Ph. Barbe (cid:13) Contents 1. Introduction 1 Notes 16 2. The logarithmic estimate 19 Notes 23 3. The basic bounds 25 1. The normal flow and the normal foliation 25 2. Base manifolds and their orthogonal leaves 36 Notes 40 4. Analyzing the leading term for some smooth sets 43 1. Quadratic approximation of τ near a dominating manifold 44 A 2. Approximation for det (t,v) 46 A 3. What should the result be? 50 5. The asymptotic formula 53 Notes 65 6. Asymptotic for sets translated towards infinity 69 Notes 77 7. Homothetic sets, homogeneous I and Laplace’s method 79 Notes 88 8. Quadratic forms of random vectors 89 1. An example with light tail distribution 89 2. An example with heavy tail distribution 93 3. Heavy tail and degeneracy 142 Notes 162 9. Random linear forms 165 1. Some results on convex sets 166 2. Example with light tails 177 3. Example with heavy tails 185 Notes 193 10. Random matrices 197 1. Random determinants, light tails 200 2. Random determinants, heavy tails 210 3. Geometry of the unit ball of M(n,R) 218 4. Norms of random matrices 229 Notes 236 11. Finite sample results for autoregressive processes 239 1. Background on autoregressive processes 239 2. Autoregressive process of order 1 243 3. Autoregressive process of arbitrary order 255 Notes 271 12. Suprema of some stochastic processes 273 1. Maxima of processes and maxima of their variances 273 2. Asymptotic expansions for the tail of the supremum of Gaussian processes can be arbitrary bad 276 3. Maximum of nonindependent Gaussian random variables 280 4. The truncated Brownian bridge 282 5. Polar processes on boundary of convex sets 293 Notes 296 Appendix 1. Gaussian and Student tails 297 Appendix 2. Exponential map 303 References 305 Notation 311 Postface 315

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