REFERENCES
- Battiti , R. and Masulli , F. 1990 . BFGS Optimization for Faster and Automated Supervised Learning . Proceedings of the International Neural Network Conference (INNC 90)-Paris-France , : 757 – 760 .
- Bauwens , L. and Lubrano , M. 2008 . Bayesian Inference on GARCH Models Using the Gibbs Sampler . The Econometrics Journal , 1 : 23 – 46 .
- Bollerslev , T. 1986 . Generalized Autoregressive Conditional Heteroskedasticity . Journal of Econometrics , 31 : 307 – 327 .
- Chow , Y. and Teicher , H. 2003 . Probability Theory: Independence, Interchangeability, Martingales , New York : Springer Verlag .
- Cochran , W. 1977 . Sampling Techniques , New York : Wiley .
- Denny , M. 2001 . Introduction to Importance Sampling in Rare-Event Simulations . European Journal of Physics , 22 : 403
- Duffie , D. and Pan , J. 1997 . An Overview of Value at Risk . The Journal of Derivatives , 4 : 7 – 49 .
- Dunkel , J. and Weber , S. 2007 . Efficient Monte Carlo Methods for Convex Risk Measures in Portfolio Credit Risk Models . Proceedings of the 2007 Winter Simulation Conference , : 958 – 966 .
- Durrett , R. 1996 . Probability: Theory and Examples , Pacific Grove , CA : Duxbury Press .
- Emond , M. , Raftery , A. and Steele , R. 2001 . Easy Computation of Bayes Factors and Normalizing Constants for Mixture Models via Mixture Importance Sampling . Technical Report No. 398, Department of Statistics, Washington University Seattle
- Engle , R. 1982 . Autoregressive Conditional Heteroscedasticity With Estimates of the Variance of United Kingdom Inflation . Econometrica: Journal of the Econometric Society , 50 : 987 – 1007 .
- Fan , S. , Chenney , S. , Hu , B. , Tsui , K. and Lai , Y. 2006 . “ Optimizing Control Variate Estimators for Rendering ” . In Computer Graphics Forum (Vol. 25) , Edited by: Gröller , E. and Szirmay-Kalos , L. 351 – 357 . Goslar , Germany : Eurographics Association .
- Ford , E. and Gregory , P. 2007 . “ Bayesian Model Selection and Extrasolar Planet Detection ” . In Statistical Challenges in Modern Astronomy IV (Vol. 371) , Edited by: Babu , G. J. and Feigelson , E. D. 189 San Francisco , CA : Astron. Soc. Pacific .
- Gelman , A. and Meng , X. 1998 . Simulating Normalizing Constants: From Importance Sampling to Bridge Sampling to Path Sampling . Statistical Science , 13 : 163 – 185 .
- Geweke , J. 1989 . Bayesian Inference in Econometric Models Using Monte Carlo Integration . Econometrica: Journal of the Econometric Society , 57 : 1317 – 1339 .
- Geweke , J. 1994 . Bayesian Comparison of Econometric Models . Working Paper No. 532, Federal Reserve Bank of Minneapolis
- Geyer , C. 1994 . On the Asymptotics of Constrained M-Estimation . The Annals of Statistics , 22 : 1993 – 2010 .
- Giordani , P. and Kohn , R. 2010 . Adaptive Independent Metropolis-Hastings by Fast Estimation of Mixtures of Normals . Journal of Computational and Graphical Statistics , 19 : 243 – 259 .
- Givens , G. and Raftery , A. 1996 . Local Adaptive Importance Sampling for Multivariate Densities With Strong Nonlinear Relationships . Journal of the American Statistical Association , 91 : 132 – 141 .
- Glasserman , P. , Heidelberger , P. and Shahabuddin , P. 2000 . Variance Reduction Techniques for Estimating Value-at-Risk . Management Science , 46 : 1349 – 1364 .
- Haberman , S. 1989 . Concavity and Estimation . The Annals of Statistics , 17 : 1631 – 1661 .
- Hesterberg , T. 1988 . Advances in Importance Sampling . Ph.D. dissertation, Department of Statistics, Stanford University
- Hesterberg , T. 1995 . Weighted Average Importance Sampling and Defensive Mixture Distributions . Technometrics , 37 : 185 – 194 .
- Hjort , N. and Pollard , D. 1994 . Asymptotics for Minimisers of Convex Processes . Statistical Research Report, Department of Mathematics, University of Oslo
- Hoogerheide , L. and Van Dijk , H. 2010 . Bayesian Forecasting of Value at Risk and Expected Shortfall Using Adaptive Importance Sampling . International Journal of Forecasting , 26 : 231 – 247 .
- Jorion , P. 1997 . Value At Risk: The New Benchmark for Controlling Market Risk , Chicago : McGraw-Hill .
- Kong , A. , McCullagh , P. , Meng , X. , Nicolae , D. and Tan , Z. 2003 . A Theory of Statistical Models for Monte Carlo Integration . Journal of the Royal Statistical Society, Series B , 65 : 585 – 604 .
- Liang , F. , Liu , C. and Carroll , R. J. 2007 . Stochastic Approximation in Monte Carlo Computation . Journal of the American Statistical Association , 102 : 305 – 320 .
- Liu , J. 2008 . Monte Carlo Strategies in Scientific Computing , New York : Springer Verlag .
- Oh , M. and Berger , J. 1993 . Integration of Multimodal Functions by Monte Carlo Importance Sampling . Journal of the American Statistical Association , 88 : 450 – 456 .
- Owen , A. and Zhou , Y. 2000 . Safe and Effective Importance Sampling . Journal of the American Statistical Association , 95 : 135 – 143 .
- Owen , A. and Zhou , Y. 1999 . Adaptive Importance Sampling by Mixtures of Products of Beta Distributions . Technical Report No. 1999-1, Department of Statistics, Stanford University
- Raghavan , N. and Cox , D. 1998 . Adaptive Mixture Importance Sampling . Journal of Statistical Computation and Simulation , 60 : 237 – 260 .
- Robert , C. and Casella , G. 2004 . Monte Carlo Statistical Methods , New York : Springer Verlag .
- Rothenberg , T. 1984 . “ Approximating the Distributions of Econometric Estimators and Test Statistics ” . In Handbook of Econometrics (Vol. 2) , Edited by: Griliches , Z. and Intriligator , M. D. 881 – 935 . Amsterdam : North Holland .
- Rubinstein , R. and Kroese , D. 2008 . Simulation and the Monte Carlo Method , Hoboken , NJ : Wiley .
- Smith , P. , Shafi , M. and Gao , H. 1997 . Quick Simulation: A Review of Importance Sampling Techniques in Communications Systems . IEEE Journal on Selected Areas in Communications , 15 : 597 – 613 .
- Tan , Z. 2004 . On a Likelihood Approach for Monte Carlo Integration . Journal of the American Statistical Association , 99 : 1027 – 1036 .
- van der Vaart , A. 2000 . Asymptotic Statistics , Cambridge : Cambridge University Press .
- van der Vaart , A. and Wellner , J. 1996 . Weak Convergence and Empirical Processes , New York : Springer Verlag .
- Veach , E. and Guibas , L. 1995 . Optimally Combining Sampling Techniques for Monte Carlo Rendering . Proceedings of the 22nd Annual Conference on Computer Graphics and Interactive Techniques , : 419 – 428 .
- West , M. 1993 . Approximating Posterior Distributions by Mixture . Journal of the Royal Statistical Society, Series B , 55 : 409 – 422 .