Estimation of the outlier-free and outlier-contaminated transfer function models

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Contributions of influence function using the inverse autocorrelation function in the detection of outliers

Outliers in time series, depending on their nature may have a moderate to significant impact on the effectiveness of the standard methodology for time series analysis with respect to model identification, estimation and forecasting. The suggested procedure used for identifying the outliers graphically in time series data was investigated by considering the influence function for the inverse autocorrelation function (IACF). Form the findings, it was noticed that for large series the influence was almost positive in values while for relatively short series the large negative influence are noticeable. The model order determination technique was also proposed. JONAMP Vol. 11 2007: pp. 627-63

Identification of the time series interrelationships with reference to dynamic regression models

In this study, the model of interest is that of a rational distributed lag function Y on X plus an independent Autoregressive Moving Average (ARMA) model. To investigate the model structure relating X and Y we considered the inverse cross correlation function for the observed and residual series in the presence of outliers. A two stage identification procedure is presented which involves fitting univariate time series model to each series and identifying a dynamic shock model relating the two univariate model series. The models so far obtained were combined to identify a dynamic regression model, which were fitted in the usual ways. From our findings, there was a reduction in the error variance of the final model with the outlier free stationary series which is an indication that the two-stage procedure is reliable and efficient. JONAMP Vol. 11 2007: pp. 621-62

Some basic tests on time series outliers

Outliers are common place in applied time series analysis and various types of structural changes occur frequently and raises the question of efficiency and adequacy in fitting models. The methods under consideration for the tests of time series outliers are the Peirce’s criterion, Chauvenet’s criterion and Grubbs’ test. A set of data was considered and later on tested for outliers. From the findings, the Peirce’s criterion identified two outliers in the data set while the Chauvenet’s and Grubbs’ tests both identified only one outlier. In the Peirce’s criterion, the result of two outliers were opposed by the Chauvenet’s criterion and Grubb’s Test because Peirce’s criterion accounts for the case where there is more than one suspect data point at once.Journal of the Nigerian Association of Mathematical Physics, Volume 15 (November, 2009), pp 101 - 10

A new spectral density Kalman filter function

Spectral density function with respect to Kalman filter process is defined and investigated. Using the basic ideas of Kalman filter mean, filter variance  and filter covariance the model gives a computational efficiency of getting a solution. Keywords: Spectral density, Kalman filter, Filter mean, Filter variance, Filter covariance

Ratio type estimators with Kalman filter output

Kalman filter have been very successful as an estimator in various forms. In this paper, we introduce the use of the Kalman filter version for two phase sampling with an auxiliary variance. The computational instance gave the same precise result as that of the conventional process. Keywords: Kalman filter, Phase sampling, Auxiliary variable