• Communications for Statistical Applications and Methods
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• v.17 no.3
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• pp.441-450
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• 2010

In this paper we consider the problem of forecasting binomial time series, modelled by the binomial autoregressive model. This paper considers proposed by McKenzie (1985) and is extended to a higher order by $Wei{\ss}$(2009). Since the binomial autoregressive model is a Markov chain, we can apply the earlier work of Bu and McCabe (2008) for integer valued autoregressive(INAR) model to the binomial autoregressive model. We will discuss how to compute the h-step-ahead forecast of the conditional probabilities of $X_{T+h}$ when T periods are used in fitting. Then we obtain the maximum likelihood estimator of binomial autoregressive model and use it to derive the maximum likelihood estimator of the h-step-ahead forecast of the conditional probabilities of $X_{T+h}$. The methodology is illustrated by applying it to a data set previously analyzed by $Wei{\ss}$(2009).

• The Journal of the Acoustical Society of Korea
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• v.15 no.2E
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• pp.4-7
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• 1996

In this paper, we propose a formula to compute the exact autocorrelations of the autoregressive (AR) process. For an arbitrary value of N, we first review the Yule-Walker equation and some basic properties of the AR model. We then modify the Yule-Walker equation to construct a new system of N+1 linear equations that can be used to solve for the N+1 autocorrelation coefficients for lags 0, 1, …, N, provided that the AR parameters of order N and the power of the white noise of the AR process are given.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.2
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• pp.103-110
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• 2004

This paper proposes a new method on the EMG AR(autoregressive) modeling in pattern recognition for various head motions. The proper electrode placement in applying AR or cepstral coefficients for EMG signature discrimination is investigated. EMG signals are measured for different 10 motions with two electrode arrangements simultaneously. Electrode pairs are located separately on dominant muscles(S-type arrangement), because the bandwidth of signals obtained from S-type placement is wider than that from C-type(closely in the region between muscles). From the result of EMG pattern recognition test, the proposed mIAR(modified integrated mean autoregressive model) technique improves the recognitions rate around 17-21％ compared with other the AR and cepstral methods.

• Journal of Korean Society for Quality Management
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• v.31 no.3
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• pp.126-135
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• 2003

A basic assumption in standard applications of control charts is that the observations are statistically independent. However, this assumption is often violated from processes in many industries. The presence of autocorrelation has a serious impact on the performance of control charts, causing a dramatic increase in the frequency of false alarms. This paper considers a process in which the observations can be modeled as a first order autoregressive(AR(1)) process, and develops (equation omitted) charts with the variable sample size(VSS) scheme for monitoring the mean of this process.

• International Journal of Safety
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• v.3 no.1
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• pp.1-5
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• 2004

This study discusses frequency analysis based on autoregressive (AR) time series model, and the characterization of surface quality in orthogonal cutting of a fiber-matrix composite materials. A sparsely distributed idealized composite material, namely a glass reinforced polyester (GFRP) was used as workpiece. Analysis method employs a force sensor and the signals from the sensor are processed using AR time series model. The experimental correlations between the fiber pull-out and AR model coefficients are then established.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.29-32
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• 1999

In this study, the time delay model were simulated using the well-known AR model. Frequency response of the time delay model can be obtained by mapping AR model to JTC model in the time domain. That is, from the few measurement data in JTC model, the channel frequency response can be obtained by the estimation of AR model parameters. From this channel frequency response, the time delay model can be obtained using Fourier transformation. To prove the validity of the suggested method, three models of JTC were shown and analyzed.

• Communications of the Korean Mathematical Society
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• v.17 no.2
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• pp.309-319
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• 2002

We consider the nonlinear autoregressive model with nonlinear ARCH errors, and find sufficient conditions for the existence of a strictly stationary process. New results are obtained, and known results are shown to emerge as special oases.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.6
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• pp.163-174
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• 2009

Applying the maximally polyphase decomposition and noble identity to the adaptive filter in subband structure, the conventional fullband affine projection algorithm is translated to the subband affine projection (SAP) algorithm. The Maximally polyphase decomposed SAP (MPDSAP) algorithm is a special version of the SAP algorithm, and its adaptive sub-filters have unity projection dimension. The weight updating formular of the MPDSAP is similar to that of the NLMS algorithm, so it may be more proper algorithm than other AP-type algorithms for many practical applications. This paper presents a new statistical analysis of the MPDSAP algorithm. The analytical model is derived for autoregressive (AR) inputs and the nonunity adaptive gain in the subband structure with the orthonormal analysis filters (OAF), The pre-whitening by the OAF allows the derivation of a simple-analytical model for the MPDSAP with the AR inputs and the nonunity adaptive gain.

• Journal of Biomedical Engineering Research
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• v.21 no.4
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• pp.339-344
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• 2000

The mucociliary system is one of the most important airway defense mechanisms in human body and impairment of ciliary movement results in various diseases in respiratory tract. In this study, we have developed a system that can measure ciliary movement in vivo and quantified ciliary beat frequency (CBF) through autoregressive (AR) power spectrum. To measure the frequency in vivo, we applied a photoelectric method that was composed of a laser light and a fiber optic probe. Scattered signals are transferred to a PC in which they are displayed on the monitor and its CBF is determined by the AR method in were acquired. For 8 normal subjects, the analyzed CBFs ranged from 5 to 10Hz and its mean was 7.3${\pm}$1.1Hz. This result showed similar aspects to the reported results of CBFs to data. We expect that this result will be applied in various clinical studies such as analysis of CBF changes by drugs or by diseaes.

• The Korean Journal of Applied Statistics
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• v.32 no.6
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• pp.909-922
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• 2019

In this paper, we study the adaptive least absolute shrinkage and selection operator (LASSO) for the unstable autoregressive (AR) model. To identify the existence of the unit root, we apply the adaptive LASSO to the augmented Dickey-Fuller regression model, not the original AR model. We illustrate our method with simulations and a real data analysis. Simulation results show that the adaptive LASSO obtained by minimizing the Bayesian information criterion selects the order of the autoregressive model as well as the degree of differencing with high accuracy.