Yung Wun Na. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 60-62). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Statistical Modeling --- p.4 / Chapter 2.1 --- Capture Recapture Model --- p.4 / Chapter 2.1.1 --- Petersen Estimate --- p.5 / Chapter 2.1.2 --- Chapman Estimate --- p.8 / Chapter 2.2 --- The Bootstrap Method --- p.9 / Chapter 2.2.1 --- The Bootstrap Percentile Method --- p.10 / Chapter 2.3 --- The Double Bootstrap Method --- p.12 / Chapter 2.3.1 --- The Robbins-Monro Method --- p.12 / Chapter 2.3.2 --- Confidence Interval generated by the Robbins-Monro Method --- p.13 / Chapter 2.3.3 --- Three Different Approaches --- p.16 / Chapter 3 --- Empirical Study --- p.19 / Chapter 3.1 --- Introduction --- p.19 / Chapter 3.2 --- Double Bootstrap Method --- p.20 / Chapter 3.2.1 --- Petersen Estimate --- p.20 / Chapter 3.2.2 --- Chapman Estimate --- p.27 / Chapter 3.2.3 --- Comparison of Petersen and Chapman Estimates --- p.31 / Chapter 3.3 --- Conclusion --- p.33 / Chapter 4 --- Simulation Study --- p.35 / Chapter 4.1 --- Introduction --- p.35 / Chapter 4.2 --- Simulation Results of Double Bootstrap Method --- p.36 / Chapter 5 --- Conclusion and Discussion --- p.52 / References --- p.60

Au Tsz-yin. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (leaves 76-79). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Forecasting Time Series --- p.1 / Chapter 1.2 --- Importance of Multiple Forecasts --- p.2 / Chapter 1.3 --- Methodology of Forecasting for Autoregressive Models --- p.3 / Chapter 1.4 --- Bootstrap Approach --- p.9 / Chapter 1.5 --- Objectives --- p.12 / Chapter 2 --- "Bootstrapping Simultaneous Prediction Intervals, Case A: p known" --- p.15 / Chapter 2.1 --- TS Procedure --- p.16 / Chapter 2.2 --- CAO Procedure --- p.18 / Chapter 2.3 --- MAS Procedure --- p.20 / Chapter 3 --- "Bootstrapping Simultaneous Prediction Intervals, Case B: p unknown" --- p.24 / Chapter 3.1 --- TS Procedure --- p.25 / Chapter 3.2 --- CAO Procedure --- p.27 / Chapter 3.3 --- MAS Procedure --- p.28 / Chapter 4 --- Simulation Study --- p.29 / Chapter 4.1 --- Design of The Experiment --- p.29 / Chapter 4.2 --- Simulation Results --- p.33 / Chapter 5 --- A Real-Data Case --- p.36 / Chapter 5.1 --- Case A --- p.37 / Chapter 5.2 --- Case B --- p.42 / Chapter 6 --- Conclusion --- p.46 / Chapter A --- Tables of Simulation Results for Case A --- p.49 / Chapter B --- Tables of Simulation Results for Case B --- p.62 / Chapter C --- References --- p.76

Lui Ying Kin. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 63-65). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Introduction to bootstrap and efficiency bootstrap simulation --- p.5 / Chapter 2.1 --- Background of bootstrap --- p.5 / Chapter 2.2 --- Basic idea of bootstrap --- p.7 / Chapter 2.3 --- Variance reduction methods --- p.10 / Chapter 2.3.1 --- Control variates --- p.10 / Chapter 2.3.2 --- Common random numbers --- p.12 / Chapter 2.3.3 --- Antithetic variates --- p.14 / Chapter 2.3.4 --- Importance Sampling --- p.15 / Chapter 2.4 --- Efficient bootstrap simulation --- p.17 / Chapter 2.4.1 --- Linear approximation --- p.18 / Chapter 2.4.2 --- Centring method --- p.19 / Chapter 2.4.3 --- Balanced resampling --- p.20 / Chapter 2.4.4 --- Antithetic resampling --- p.21 / Chapter 3 --- Methodology --- p.22 / Chapter 3.1 --- Introduction --- p.22 / Chapter 3.2 --- Cluster analysis --- p.24 / Chapter 3.3 --- Regression estimator and mixture experiment --- p.25 / Chapter 3.4 --- Estimate of standard error and bias --- p.30 / Chapter 4 --- Simulation study --- p.45 / Chapter 4.1 --- Introduction --- p.45 / Chapter 4.2 --- Ratio estimation --- p.46 / Chapter 4.3 --- Time series problem --- p.50 / Chapter 4.4 --- Regression problem --- p.54 / Chapter 5 --- Conclusion and discussion --- p.60 / Reference --- p.63

Thesis (Ph. D.)--Ohio State University, 2006. / Title from first page of PDF file. Includes bibliographical references (p. 110-114).

Thesis (Ph. D.)--University of Chicago, Department of Statistics, August 2001. / Includes bibliographical references. Also available on the Internet.

Thesis (Ph. D.)--University of California, San Diego, 2003. / Vita. Includes bibliographical references (leaves 106-109).

published_or_final_version / Statistics and Actuarial Science / Doctoral / Doctor of Philosophy

published_or_final_version / Statistics and Actuarial Science / Master / Master of Philosophy

We consider the use of a sieve bootstrap based on moving average (MA) and autoregressive moving average (ARMA) approximations to test the unit root hypothesis when the true Data Generating Process (DGP) is a general linear process. We provide invariance principles for these bootstrap DGPs and we prove that the resulting ADF tests are asymptotically valid. Our simulations indicate that these tests sometimes outperform those based on the usual autoregressive (AR) sieve bootstrap. We study the reasons for the failure of the AR sieve bootstrap tests and propose some solutions, including a modified version of the fast double bootstrap. / We also argue that using biased estimators to build bootstrap DGPs may result in less accurate inference. Some simulations confirm this in the case of ADF tests. We show that one can use the GLS transformation matrix to obtain equations that can be used to estimate bias in general ARMA(p,q) models. We compare the resulting bias reduced estimator to a widely used bootstrap based bias corrected estimator. Our simulations indicate that the former has better finite sample properties then the latter in the case of MA models. Finally, our simulations show that using bias corrected or bias reduced estimators to build bootstrap DGP sometimes provides accuracy gains.

Thesis (M.Eng.) - Carleton University, 2002. / Includes bibliographical references (p. 130-135). Also available in electronic format on the Internet.