Estimation of AR and ARMA models by stochastic complexity

Giurcăneanu, Ciprian Doru; Rissanen, Jorma

doi:10.1214/074921706000000941

Cited by 10 publications

(5 citation statements)

References 21 publications

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“…However, the calculation of the 6 complexity of the model class involves Monte-Carlo integration, which is not a good candidate for scalability. Moreover, we found it difficult to estimate the values above AR order 6, an observation already presented in [13]. There is an alternative encoding,whic histobebasedontheC onditional Normalised Maximum-Likelihood code of [19] for generalisedG a u s s i a nr e g r e s s i o nf a m i l i e sf o rA R models.…”

Section: Discussionmentioning

confidence: 70%

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Scalable structural break detection

íLtetö¹,

Hansen²,

Germain-Renaud³

et al. 2012

Applied Soft Computing

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This paper deals with a statistical model fitting procedure for non-stationary time series. This procedure selects the parameters of a piecewise autoregressive model using the Minimum Description Length principle. The existing chromosome representation of the piecewise autoregressive model and its corresponding optimisation algorithm are improved.F i r s t , w e s h o w t h a t o u r p r o p o s e d chromosome representation better captures the intrinsic properties of the piecewise autoregressive model. Second, we apply an optimisation algorithm, the Covariance Matrix Adaptation -Evolution Strategy, with which our setup converges faster to the optimal fit. Our proposed method achieves at least one order of magnitude performance improvement compared to the existing solution.

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Section: Discussionmentioning

confidence: 70%

“…The dimensionality of the NI algorithm with bilevel optimisation is the same, however with effectively only two different values. The dimensionality of the problem for CMA-ES with bilevel optimisation depends on the number of break points explored on the optimisation path (it is at most [10][11][12][13][14][15][16][17][18][19][20].…”

Section: Technical Detailsmentioning

confidence: 99%

Scalable structural break detection

íLtetö¹,

Hansen²,

Germain-Renaud³

et al. 2012

Applied Soft Computing

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“…The time series y ( t ) can be regressed, in a similar manner as described in Ref. 12, using the sinusoidal regression model with Gaussian uncertainty; that is,…”

Section: Methodsmentioning

confidence: 99%

Robust motion correction and outlier rejection of in vivo functional MR images of the fetal brain and placenta during maternal hyperoxia

et al. 2015

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Subject motion is a major challenge in functional magnetic resonance imaging studies (fMRI) of the fetal brain and placenta during maternal hyperoxia. We propose a motion correction and volume outlier rejection method for the correction of severe motion artifacts in both fetal brain and placenta. The method is optimized to the experimental design by processing different phases of acquisition separately. It also automatically excludes high-motion volumes and all the missing data are regressed from ROI-averaged signals. The results demonstrate that the proposed method is effective in enhancing motion correction in fetal fMRI without large data loss, compared to traditional motion correction methods.

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“…Among the few forms are ridge regression, seasonality regression analysis, Fourier regression, trigonometric series regression analysis, and smoothing splines regression [11][12][13][14]. All these mentioned forms are for demonstrating the dummy variables for estimation of seasonal effects in a time series, to penalize estimators in situation where the number of parameters estimated is strictly greater than the sample size, and to free the distributional property of the observations in non-parametric settings [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

On Two Covariates Cosine and Sine Noisy-Wave Trigonometry Regression of Heartbeats

Olawale¹

2019

AJAMS

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This paper proposes and describes the acumen on alternate two covariates linear Cosine and Sine regression functions that possessed a noisy-wave or tone frequencies via wave-trend of actualized observations of regressors and responsive variable needed in fitting a wavy equation of trigonometry regression. The method of maximum likelihood was used in estimating parameters associated to the Cosine and Sine alternate functions via vector coefficients as well as their distributional and residual properties. The estimations obtained via the method were enthralled to the noisy-wave mesokurtic observations of babies’ rate of heartbeats exactly an hour after birth (HR1), two hours after birth (HR2) and three hours after birth (HR3). The implementation and illustrative application was via R using the heartbeat dataset. It was gleaned that the trigonometry equation line .......

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Estimation of AR and ARMA models by stochastic complexity

Cited by 10 publications

References 21 publications

Scalable structural break detection

Scalable structural break detection

Robust motion correction and outlier rejection of in vivo functional MR images of the fetal brain and placenta during maternal hyperoxia

On Two Covariates Cosine and Sine Noisy-Wave Trigonometry Regression of Heartbeats

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