The autoregressive (AR) model is a widely used model to understand time series data.
Traditionally, the innovation noise of the AR is modeled as Gaussian. However, many time …

In this article, normal inverse Gaussian (NIG) autoregressive model is introduced. The
parameters of the model are estimated using expectation maximization (EM) algorithm. The …

In this paper, we study the autoregressive (AR) model with normal inverse Gaussian (NIG)
innovations. The NIG distribution is semi heavy-tailed and is helpful in capturing the extreme …

Data collected over time are common in applications and may contain censored or missing
observations, making it difficult to use standard statistical procedures. This article proposes …

This paper considers the vector autoregressive model of order p, VAR (p), with multivariate t
error distributions, the latter being more prevalent in real life than the usual multivariate …

Os modelos de regressão com erros autorregressivos considerando dados incompletos, isto
é, quando a variável de interesse não está completamente disponível, seja pelo fato de ser …

The time series models with normally distributed innovations generate stationary normal
sequences. However, if the innovations are not normal then the stationary marginal …

ﻢﺘﻳﺭﻮﮕﻟﺍ ﺯﺍ ﻩﺩﺎﻔﺘﺳﺍ ﺎﺑ ﻝﺪﻣ ﻱﺎﻫﺮﺘﻣﺍﺭﺎﭘ ﺲﭙﺳ ﻭ ﺖﺳﺍ ﻩﺪﺷ ﻲﻓﺮﻌﻣ ﻲﮐﺪﻨﭼ ﻮﻴﺳﺮﮔﺭﻮﺗﺍ ﻲﻧﺎﻣﺯ ﻱﺮﺳ ﻝﺪﻣ، ﻪﻟﺎﻘﻣ ﻦﻳﺍ ﺭﺩ: ﻩﺪﻴﮑﭼ ﺭﺩ
ﻲﻳﺎﻤﻨﺘﺳﺭﺩ ﻊﺑﺎﺗ. ﺪﻧﻮﺷ ﻲﻣ ﺩﺭﻭﺁﺮﺑ، ﺖﺳﺍ ﻲﻳﺎﻤﻨﺘﺳﺭﺩ ﻢﻤﻴﺴﮐﺎﻣ ﻱﺎﻫﺩﺭﻭﺁﺮﺑ ﻪﺒﺳﺎﺤﻣ ﻱﺍﺮﺑ ﻱﺭﺍﺮﮑﺗ ﺵﻭﺭ ﮏﻳ ﻪﮐ SEM ﻩﺩﺎﻔﺘﺳﺍ ﺎﺑ ﻭ …‎

In this paper, the quantile autoregressive time series model is introduce and then the model
parameters are estimated using the Stochastic EM algorithm, which is an iterative method to …