Results 11 to 20 of about 7,202 (166)
CoRR, 2022 Autoregressive processes naturally arise in a large variety of real-world scenarios, including stock markets, sales forecasting, weather prediction, advertising, and pricing. When facing a sequential decision-making problem in such a context, the temporal dependence between consecutive observations should be properly accounted for guaranteeing ...
Francesco Bacchiocchi +6 more
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SUBGEOMETRICALLY ERGODIC AUTOREGRESSIONS WITH AUTOREGRESSIVE CONDITIONAL HETEROSKEDASTICITY
Econometric Theory, 2023 In this paper, we consider subgeometric (specifically, polynomial) ergodicity of univariate nonlinear autoregressions with autoregressive conditional heteroskedasticity (ARCH). The notion of subgeometric ergodicity was introduced in the Markov chain literature in the 1980s, and it means that the transition probability measures converge to the ...
Mika Meitz, Pentti Saikkonen
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Journal of the American Statistical Association, 2008 This paper considers geometric ergodicity and likelihood based inference for linear and nonlinear Poisson autoregressions.In the linear case the conditional mean is linked linearly to its past values as well as the observedvalues of the Poisson process.
Fokianos, Konstantinos +5 more
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J. Mach. Learn. Res., 2020 We propose a first-order autoregressive (i.e. AR(1)) model for dynamic network processes in which edges change over time while nodes remain unchanged. The model depicts the dynamic changes explicitly. It also facilitates simple and efficient statistical inference methods including a permutation test for diagnostic checking for the fitted network models.
Binyan Jiang +2 more
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Quantile Double Autoregression [PDF]
SSRN Electronic Journal, 2019 Many financial time series have varying structures at different quantile levels, and also exhibit the phenomenon of conditional heteroskedasticity at the same time. However, there is presently no time series model that accommodates both of these features.
Zhu, Qianqian, Li, Guodong
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Noncausal Vector Autoregression [PDF]
SSRN Electronic Journal, 2009 In this paper, we propose a new noncausal vector autoregressive (VAR) model for non-Gaussian time series. The assumption of non-Gaussianity is needed for reasons of identifiability. Assuming that the error distribution belongs to a fairly general class of elliptical distributions, we develop an asymptotic theory of maximum likelihood estimation and ...
Lanne, Markku, Saikkonen, Pentti
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Energies, 2019 The recent developments in combining point forecasts of day-ahead electricity prices across calibration windows have provided an extremely simple, yet a very efficient tool for improving predictive accuracy. Here, we consider two novel extensions of this
Tomasz Serafin +2 more
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Maximum-Likelihood Estimation in a Special Integer Autoregressive Model
Econometrics, 2020 The paper is concerned with estimation and application of a special stationary integer autoregressive model where multiple binomial thinnings are not independent of one another.
Robert C. Jung, Andrew R. Tremayne
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Modeling Best Practice Life Expectancy Using Gumbel Autoregressive Models
Risks, 2021 Best practice life expectancy has recently been modeled using extreme value theory. In this paper we present the Gumbel autoregressive model of order one—Gumbel AR(1)—as an option for modeling best practice life expectancy. This class of model represents
Anthony Medford
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Combined Method for Informative Feature Selection for Speech Pathology Detection
Доклады Белорусского государственного университета информатики и радиоэлектроники, 2023 The task of detecting vocal abnormalities is characterized by a small amount of available data for training, as a consequence of which classification systems that use low-dimensional data are the most relevant.
D. S. Likhachov +3 more
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