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Toplam kayıt 4, listelenen: 1-4

A New Estimation Technique for AR(1) Model with Long-Tailed Symmetric Innovations

Dener Akkaya, Ayşen; Türker Bayrak, Özlem (Springer, 2018)

In recent years, it is seen in many time series applications that innovations are non-normal. In this situation, it is known that the least squares (LS) estimators are neither efficient nor robust and maximum likelihood ...
A New Estimation Technique for AR(1) Model with Long-Tailed Symmetric Innovations

Dener Akkaya, Ayşen; Türker Bayrak, Özlem (2017)

In recent years, it is seen in many time series applications that innovations are non-normal. In this situation, it is known that the least squares (LS) estimators are neither efficient nor robust and maximum likelihood ...
Adaptive Estimation of Autoregressive Models Under Long-Tailed Symmetric Distribution

Yengür, Begüm; Bayrak, Özlem Türker; Dener Akkaya, Ayşen (Association for Computing Machinery, 2019-07-08)

In this paper, we consider the autoregressive models where the error term is non-normal; specifically belongs to a long-tailed symmetric distribution family since it is more relevant in practice than the normal distribution. ...
Inference of Autoregressive Model with Stochastic Exogenous Variable Under Short-Tailed Symmetric Distributions

Bayrak, Özlem Türker; Dener Akkaya, Ayşen (Springer International Publishing AG, 2018-12)

In classical autoregressive models, it is assumed that the disturbances are normally distributed and the exogenous variable is non-stochastic. However, in practice, short-tailed symmetric disturbances occur frequently and ...

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