scholarly journals Block Bootstrap Theory for Multivariate Integrated and Cointegrated Processes

2014 ◽  
Vol 36 (3) ◽  
pp. 416-441 ◽  
Author(s):  
Carsten Jentsch ◽  
Dimitris N. Politis ◽  
Efstathios Paparoditis
Keyword(s):  
Block Bootstrap ◽  
Bootstrap Theory

scholarly journals Do Different Models Induce Changes in Mortality Indicators? That Is a Key Question for Extending the Lee-Carter Model

2021 ◽  
Vol 18 (4) ◽  
pp. 2204
Author(s):  
Ana Debón ◽  
Steven Haberman ◽  
Francisco Montes ◽  
Edoardo Otranto
Keyword(s):  
Historical Data ◽  
Mortality Rates ◽  
Parametric Model ◽  
Mortality Data ◽  
Block Bootstrap ◽  
Functional Anova ◽  
Model Block ◽  
The Usa ◽  
Data Constraints ◽  
Carter Model

The parametric model introduced by Lee and Carter in 1992 for modeling mortality rates in the USA was a seminal development in forecasting life expectancies and has been widely used since then. Different extensions of this model, using different hypotheses about the data, constraints on the parameters, and appropriate methods have led to improvements in the model’s fit to historical data and the model’s forecasting of the future. This paper’s main objective is to evaluate if differences between models are reflected in different mortality indicators’ forecasts. To this end, nine sets of indicator predictions were generated by crossing three models and three block-bootstrap samples with each of size fifty. Later the predicted mortality indicators were compared using functional ANOVA. Models and block bootstrap procedures are applied to Spanish mortality data. Results show model, block-bootstrap, and interaction effects for all mortality indicators. Although it was not our main objective, it is essential to point out that the sample effect should not be present since they must be realizations of the same population, and therefore the procedure should lead to samples that do not influence the results. Regarding significant model effect, it follows that, although the addition of terms improves the adjustment of probabilities and translates into an effect on mortality indicators, the model’s predictions must be checked in terms of their probabilities and the mortality indicators of interest.

scholarly journals Bayesian Spatial Autoregressive for Reducing Blurring Effect in Image

2007 ◽  
Vol 11 (3) ◽  
pp. 308-311
Author(s):  
Siana Halim ◽  
Keyword(s):  
Error Estimate ◽  
Weight Matrix ◽  
Block Bootstrap ◽  
Two Dimensional ◽  
Large Matrix ◽  
Specific Manner ◽  
Spatial Autoregressive ◽  
The Difference ◽  
Blurring Effect ◽  
Regular Texture

We apply the Bayesian Spatial Autoregressive, which is developed by Geweke and LeSage, for reducing the blurring effect in the image. This blurring effect, particularly comes from the synthesizing semi regular texture via, e.g., two dimensional block bootstrap. We model the error, i.e., the difference between the true image and the synthesis one, as the Bayesian Spatial Autoregressive (SAR). Moreover, the weight matrix is defined in a specific manner, such that the problem in the computational for a very large matrix can be avoided. Finally, we use the error estimate, as the result of Bayesian SAR modelling, for reducing the blurring effect in the synthesis image.

scholarly journals Topological bootstrap theory of hadrons

1981 ◽  
Vol 11 (1) ◽  
pp. 59-93 ◽  
Author(s):  
G. F. Chew ◽  
V. Poénaru
Keyword(s):  
Bootstrap Theory

scholarly journals RESIDUAL-BASED GARCH BOOTSTRAP AND SECOND ORDER ASYMPTOTIC REFINEMENT

2016 ◽  
Vol 33 (3) ◽  
pp. 779-790 ◽  
Author(s):  
Minsoo Jeong
Keyword(s):  
Second Order ◽  
Moment Condition ◽  
Garch Models ◽  
Exact Order ◽  
Block Bootstrap ◽  
Edgeworth Expansions ◽  
Coverage Probabilities ◽  
The Moment ◽  
Autoregressive Conditional Heteroscedasticity ◽  
Reliable Methods

The residual-based bootstrap is considered one of the most reliable methods for bootstrapping generalized autoregressive conditional heteroscedasticity (GARCH) models. However, in terms of theoretical aspects, only the consistency of the bootstrap has been established, while the higher order asymptotic refinement remains unproven. For example, Corradi and Iglesias (2008) demonstrate the asymptotic refinement of the block bootstrap for GARCH models but leave the results of the residual-based bootstrap as a conjecture. To derive the second order asymptotic refinement of the residual-based GARCH bootstrap, we utilize the analysis in Andrews (2001, 2002) and establish the Edgeworth expansions of the t-statistics, as well as the convergence of their moments. As expected, we show that the bootstrap error in the coverage probabilities of the equal-tailed t-statistic and the corresponding test-inversion confidence intervals are at most of the order of O(n−1), where the exact order depends on the moment condition of the process. This convergence rate is faster than that of the block bootstrap, as well as that of the first order asymptotic test.

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