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REGRESSION, AUTOREGRESSION MODELS
Journal of Time Series Analysis, 1986Abstract.The accuracy of least squares fitted regression autoregression models as approximations to more general stochastic structures is considered, attention being paid to the accuracy of the estimates of coefficients, of the innovations sequence and to the behaviour of the order (i.e., maximum lag) as determined by methods such as IAC, BIC.
Hannan, E. J., Kavalieris, L.
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Inverse Gaussian Autoregressive Models
Journal of Time Series Analysis, 1999A first‐order autoregressive process with one‐dimensional inverse Gaussian marginals is introduced. The innovation distributions are obtained in certain special cases. The unknown parameters are estimated using different methods and these estimators are shown to be consistent and asymptotically normal.
Abraham, B., Balakrishna, N.
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Multivariate Hysteretic Autoregressive Models
Statistica Sinica, 2020Summary: This paper proposes a multivariate hysteretic autoregressive model with multiple threshold variables for modeling nonlinear time series. The proposed model encompasses the two-regime multivariate threshold autoregressive model and the hysteretic autoregressive model as special cases.
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Functional Threshold Autoregressive Model
Statistica SinicaSummary: We propose a functional threshold autoregressive model for flexible functional time series modeling. In particular, the behavior of a function at a given time point can be described by different autoregressive mechanisms, depending on the values of a threshold variable at a past time point. Sufficient conditions for the strict stationarity and
Li, Yuanbo +3 more
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Averaged Autoregression Quantiles in Autoregressive Model
2020This paper considers the averaged autoregression quantile in autoregressive models. Our primary interest is its structure, qualities, and its applications. Moreover, under the local heteroscedasticity we investigate the properties of averaged autoregression quantile. For an illustration, a simulation study is provided.
Yeşim Güney +2 more
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NON‐NEGATIVE AUTOREGRESSIVE MODELS
Journal of Time Series Analysis, 1992Abstract. Consider a stationary non‐negative autoregressive (AR) model given xt=b1xt‐1, +…+bpxt‐p+et, where the et are independent identically distributed non‐negative variables and b1, …, bp are non‐negative parameters, and all the roots of the equation 1 –b1u–…–bpup= 0 are outside the unit circle.
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2002 International Zurich Seminar on Broadband Communications Access - Transmission - Networking (Cat. No.02TH8599), 2003
The autoregressive process has been used by several authors to model MPEG video traffic and attempts to capture the frame correlation as well as the Gaussian shape of the bit rate variation. However, the autoregressive process alone does not capture scene changes.
C. Amo-Quarm, M. Mezhoudi, K. Ravindran
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The autoregressive process has been used by several authors to model MPEG video traffic and attempts to capture the frame correlation as well as the Gaussian shape of the bit rate variation. However, the autoregressive process alone does not capture scene changes.
C. Amo-Quarm, M. Mezhoudi, K. Ravindran
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Functional autoregressive model
2012This chapter studies the functional autoregressive (FAR) process which has found many applications. The theory of autoregressive and more general linear processes in Hilbert and Banach spaces is developed in the monograph of Bosq (2000), on which Sections 13.1 and 13.2 are based.
Lajos Horváth, Piotr Kokoszka
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Antibody–drug conjugates: Smart chemotherapy delivery across tumor histologies
Ca-A Cancer Journal for Clinicians, 2022Paolo Tarantino +2 more
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