The results indicate that incorporating DPQBootstrap can improve point forecast accuracy and the quality of predictive intervals, particularly when combined with appropriately specified ARMA models.
Abstract
This work presents DPQBootstrap, a probabilistic resampling method for time series based on a Dirichlet quantile bootstrap and rank-based temporal reconstruction. The method aims to generate pseudo-series that preserve the main characteristics of the original series, including variability, quantiles, extreme values, and part of its temporal dependence. A simulation study compares DPQBootstrap with the Maximum Entropy Bootstrap implemented in the meboot package across eight simulation scenarios. The results show that DPQBootstrap achieves 106 wins over 86 for meboot according to the Interval Score, indicating a strong ability to preserve the statistical properties of the original time series. In addition, an empirical application was conducted within a Bayesian ARMA forecasting framework, with and without DPQBootstrap integration. The results indicate that incorporating DPQBootstrap can improve point forecast accuracy and the quality of predictive intervals, particularly when combined with appropriately specified ARMA models.
This paper introduces a quantile-based Kumaraswamy-Teissier autoregressive moving average (KTARMA) model for positive-valued time series. Leveraging the flexibility of the extended Kumaraswamy-Teissier distribution within an observation-driven framework, the random component of the distribution is conditioned on the hi...
Kamana Mishra, Tanmay Kayal, Sarita Azad School Of Mathematical et al.· 0 citations
Martingale posteriors and related predictive resampling methods replace the likelihood--prior pair used within Bayesian inference with a predictive model for future observations. These methods are simple to implement and increasingly popular due to their computational efficiency, but little is known about their ability...
Hui Wang, Edwin Fong, David T. Frazier· 0 citations
Estimating extreme conditional quantiles faces two major challenges: understanding complex nonlinear relationships between variables and accurately extrapolating into the tails of the distribution, where data are sparse. To address both issues, we introduce a novel approach that combines the theoretical framework of bl...
Lucien M. Vidagbandji, Alexandre Berred, Cyrille Bertelle et al.· 0 citations
We consider the estimation of an extreme conditional quantile for a heavy‐tailed distribution in the case of a strictly stationary time series . Here, denotes the conditional quantile function of given and is small, i.e., smaller than , where is the size of the sample on which the estimation is based. In a first step...
Y. Goegebeur, A. Guillou, Jing Qin· Canadian journal of statisti...· 0 citations
The beta autoregressive moving average (βARMA) models are a dynamic model based on beta regression, used to model time series that take values in the interval (0, 1) and exhibit serial dependence. However, the presence of outliers can affect parameter estimation and the quality of predictions. Effective detection of th...
We examine four two-sided confidence intervals for parameter θ of the Double XShanker distribution: the likelihood-based, Wald-type, bootstrap-t, and the bias-corrected and accelerated (BCa) bootstrap intervals. An explicit expression for the observed Fisher information is derived, allowing theWald interval to be compu...
W. Panichkitkosolkul, Patchanok Srisuradetchai· Statistics in Transition New...· 0 citations
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