LEADER 05618nam 2200685Ia 450 001 9910782120603321 005 20230617010806.0 010 $a1-281-89874-0 010 $a9786611898748 010 $a981-270-271-7 035 $a(CKB)1000000000537784 035 $a(EBL)1679794 035 $a(SSID)ssj0000227882 035 $a(PQKBManifestationID)11176530 035 $a(PQKBTitleCode)TC0000227882 035 $a(PQKBWorkID)10269755 035 $a(PQKB)10743826 035 $a(MiAaPQ)EBC1679794 035 $a(WSP)00005548 035 $a(Au-PeEL)EBL1679794 035 $a(CaPaEBR)ebr10255571 035 $a(CaONFJC)MIL189874 035 $a(OCoLC)879024118 035 $a(EXLCZ)991000000000537784 100 $a20041012d2004 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aProbability, finance and insurance$b[electronic resource] $eproceedings of a workshop at the University of Hong Kong, Hong Kong, 15-17 July 2002 /$feditors, Tze Leung Lai, Hailiang Yang, Siu Pang Yung 210 $aSingapore ;$aRiver Edge $cWorld Scientific$dc2004 215 $a1 online resource (252 p.) 300 $aDescription based upon print version of record. 311 $a981-238-853-2 320 $aIncludes bibliographical references. 327 $aPreface; List of Participants; CONTENTS; Limit theorems for moving averages; 1. Introduction; 2. Strong limit theorems for moving averages; 3. Large deviation approximations for logarithmic window sizes; 4. Window sizes associated with moderate deviation approximations; 5. Maxima and boundary crossing probabilities of asymptotically Gaussian random fields; References; On large deviations for moving average processes; 1. Introduction; 2. Main results; 3. A priori estimation; 4. Proofs of Theorem 2.1 and Theorem 2.2; 5. Proofs of Theorem 2.3 Corollary 2.1 327 $a6. Proofs of Propositions 2.1 2.2 and Theorem 2.47. Appendix: proof of Lemma 3.3; References; Recent progress on self-normalized limit theorems; 1. Introduction; 2. Self-normalized saddlepoint approximations; 3. Limit distributions of self-normalized sums; 4. Weak invariance principle for self-normalized partial sum processes; 5. Darling-Erdos theorems for self-normalized sums; 6. Large and moderate deviations for self-normalized empirical processes; 7. Cramer type large deviations for independent random variables; 8. Exponential inequalities for self-normalized processes; References 327 $aLimit theorems for independent self-normalized sums1. Introduction; 2. Asymptotic Normality; 3. Uniform Berry-Esseen Bounds; 4. Non-Uniform Berry-Esseen Bounds; 5. Exponential Non-Uniform Berry-Esseen Bounds; 6. Edgeworth Expansions; 7. Moderate Deviations; 8. Large Deviations; 9. Saddlepoint Approximations; 10. LIL for Partial Sums; 11. LIL for Increments of Partial Sums; 12. Summary; References; Phase changes in random recursive structures and algorithms; 1. Phase changes related to the Poisson distribution; 2. Phase changes related to Quicksort; 3. Conclusions; References 327 $aIterated random function system: convergence theorems1. Introduction; 2. Stochastic stability and ergodic theorem; 3. Central limit theorem and quick convergence: Poisson equation approach; References; Asymptotic properties of adaptive designs via strong approximations; 1. Introduction; 2. Play-the-Winner rule and Markov chain adaptive designs; 3. Randomized play-the-Winner rule and generalized Polya urn; 4. Doubly adaptive biased coin designs; 5. The drop-the-loss rule; 6. The minimum asymptotic variance; References; Johnson-Mehl tessellations: asymptotics and inferences; 1. Introduction 327 $a2. Asymptotics3. Statistics; References; Rapid simulation of correlated defaults and the valuation of basket default swaps; 1. Introduction; 2. Hazard rate model and calibration; 3. Pricing basket default swaps; 4. Conclusion; Appendix A. Explicit solution of the jump CIR generating function; Appendix B. Copula Functions; References; Optimal consumption and portfolio in a market where the volatility is driven by fractional Brownian motion; 1. Introduction; 2. General Results; 3. Some Particular Utility Functions; 4. Conclusion; References 327 $aMLE for change-point in ARMA-GARCH models with a changing drift 330 $aThis workshop was the first of its kind in bringing together researchers in probability theory, stochastic processes, insurance and finance from mainland China, Taiwan, Hong Kong, Singapore, Australia and the United States. In particular, as China has joined the WTO, there is a growing demand for expertise in actuarial sciences and quantitative finance. The strong probability research and graduate education programs in many of China's universities can be enriched by their outreach in fields that are of growing importance to the country's expanding economy, and the workshop and its proceedings 606 $aInvestments$xMathematics$vCongresses 606 $aFinance$xMathematical models$vCongresses 606 $aInsurance$xStatistical methods$vCongresses 615 0$aInvestments$xMathematics 615 0$aFinance$xMathematical models 615 0$aInsurance$xStatistical methods 676 $a332.015192 701 $aLai$b T. L$0614538 701 $aYang$b Hailiang$01531944 701 $aYung$b Siu Pang$01531945 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910782120603321 996 $aProbability, finance and insurance$93777924 997 $aUNINA