Results 21 to 30 of about 222 (103)
Asymptotic normality of Huber-Dutter estimators in a linear EV model with AR(1) processes
Journal of Inequalities and Applications, 2013 The paper studies a linear errors-in-variables model with first order autoregressive processes. The Huber-Dutter (HD) estimators of unknown parameters are given, and the asymptotic normality of the HD estimators is investigated. Finally, a simple example
Hongchang Hu, Xiong Pan
semanticscholar +2 more sources
Prediction of time series by statistical learning: general losses and fast rates
Dependence Modeling, 2013 We establish rates of convergences in statistical learning for time series forecasting. Using the PAC-Bayesian approach, slow rates of convergence √ d/n for the Gibbs estimator under the absolute loss were given in a previous work [7], where n is the ...
Alquier Pierre +2 more
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Time series modelling of the Kobe‐Osaka earthquake recordings
International Journal of Mathematics and Mathematical Sciences, Volume 29, Issue 8, Page 467-479, 2002., 2002 A problem of great interest in monitoring a nuclear test ban treaty (NTBT) is related to interpreting properly the differences between a waveform generated by a nuclear explosion and that generated by an earthquake. With a view of comparing these two types of waveforms, Singh (1992) developed a technique for identifying a model in time domain ...
N. Singh +2 more
wiley +1 more source
Majalah Ilmiah Matematika dan Statistika, 2022 The Infant Mortality Rate (IMR) is fundamental indicator that reflects the health status in the surrounding community. The Infant Mortality Rate is still categorized as high in Indonesia.
Muhammad Marizal +1 more
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Parallelization algorithms for modeling ARM processes
International Journal of Stochastic Analysis, Volume 13, Issue 4, Page 393-410, 2000., 2000 AutoRegressive Modular (ARM) processes are a new class of nonlinear stochastic processes, which can accurately model a large class of stochastic processes, by capturing the empirical distribution and autocorrelation function simultaneously. Given an empirical sample path, the ARM modeling procedure consists of two steps: a global search for locating ...
Benjamin Melamed, Santokh Singh
wiley +1 more source
Sample correlations of infinite variance time series models: an empirical and theoretical study
International Journal of Stochastic Analysis, Volume 11, Issue 3, Page 255-282, 1998., 1998 When the elements of a stationary ergodic time series have finite variance the sample correlation function converges (with probability 1) to the theoretical correlation function. What happens in the case where the variance is infinite? In certain cases, the sample correlation function converges in probability to a constant, but not always.
Jason Cohen +2 more
wiley +1 more source
Peramalan curah hujan di Provinsi Aceh menggunakan metode Box-Jenkins
Majalah Ilmiah Matematika dan Statistika, 2023 Floods are one of the natural disasters that frequently occur in Indonesia, including in Aceh Province. Floods primarily occur when rainfall is intense, mainly in the rainy season.
Nurhafifah Nurhafifah +5 more
doaj +1 more source
The empirical TES methodology: modeling empirical time series
International Journal of Stochastic Analysis, Volume 10, Issue 4, Page 333-353, 1997., 1997 TES (Transform‐Expand‐Sample) is a versatile class of stochastic sequences defined via an autoregressive scheme with modulo‐1 reduction and additional transformations. The scope of TES encompasses a wide variety of sample path behaviors, which in turn give rise to autocorrelation functions with diverse functional forms ‐ monotone, oscillatory ...
Benjamin Melamed
wiley +1 more source
Quasi-maximum likelihood estimator of Laplace (1, 1) for GARCH models
Open Mathematics, 2017 This paper studies the quasi-maximum likelihood estimator (QMLE) for the generalized autoregressive conditional heteroscedastic (GARCH) model based on the Laplace (1,1) residuals.
Xuan Haiyan +3 more
doaj +1 more source
, 2019 In this study, the forecasting accuracy of a new forecasting method that is alternative to two major forecasting approaches: exponential smoothing (ES) and ARIMA, will be evaluated.
G. Yapar +3 more
semanticscholar +1 more source