Effect Of Error Correlation Research Articles

Simulation Study to Evaluate Full Information Maximum Likelihood as Parameter Estimation Methods for Spatial Vector Autoregressive Model with Calendar Variation

Spatial Vector Autoregressive models with calendar variation can be used to analyze the interrelationships between variables, the relationship of variables with their past and the relationship of variables in a location with those variables in other locations. It also can accommodate the effects of calendar variations. The parameter estimation of this model can be done using Full Information Maximum Likelihood (FIML). The purpose of this study is to evaluate the performance of FIML and it is compared to Ordinary Least Square (OLS) through simulation. The simulation is done using generated data which is designed following Spatial Vector Autoregressive model with calendar variation. There are three aspects studied in this simulation, namely how the effect of error correlation between equations, the variance of error and length of period on the performance of FIML Method and OLS Method. The result of the simulation is the variance of FIML parameter estimator is smaller than OLS, especially when the error correlation between equation are high. While the variance of errors and length of periods have no effect on performance of the estimator. The simulations also show that the mean of parameter estimators both FIML and OLS are very close to the parameters specified.

Models should account for correlated observation errors

Scientists need to account for observation errors in models. In some cases, such as when multiple observations are derived from a single direct measurement, the errors in different observations are correlated. Noting that few groundwater studies have taken into account correlated observational errors, Tiedeman and Green investigated the effect of error correlation on parameter estimates, predictions, and measures of uncertainty in a simple generic inverse model and in a more complex groundwater transport model of denitrification. They compared model results obtained with and without error correlations and found that not accounting for error correlations can significantly affect the results.

The Effect of Error Correlation on Interfactor Correlation in Psychometric Measurement

This article shows how interfactor correlation is affected by error correlations. Theoretical and practical justifications for error correlations are given, and a new equivalence class of models is presented to explain the relationship between interfactor correlation and error correlations. The class allows simple, parsimonious modeling of error correlations via prespecifying reliabilities. Within the class, the correlation between latent factors can be as high as 1.0, and as low as the correlation between certain component scores. The models are indistinguishable in terms of parameter parsimony, identifiability, and fit statistics, implying that interfactor correlation is not identifiable within the class. The existence of the class is problematic for psychometric measurement, because estimates of interfactor correlation form the foundation of much of the literature.

Analysis of Stop-and-Wait ARQ for a wireless channel

In this paper, we study the behavior of the transmitter buffer of a system working under a Stop-and-Wait retransmission protocol. The buffer at the transmitter side is modeled as a discrete-time infinite-capacity queue. The numbers of information packets entering the buffer during consecutive slots are assumed to be independent and identically distributed random variables. The packets are sent over an unreliable channel and transmission errors occur in a correlated manner. Specifically, the probability of an erroneous transmission is modulated by a two-state Markov chain. An expression is derived for the probability generating function of the buffer content. This expression is then used to derive several queue-length characteristics and the mean packet delay. Numerical examples illustrate the strong effect of error correlation on the system performance. The obtained analytical results are also compared with appropriate simulations.

On the effect of error correlation on matched-field geoacoustic inversion

The effect of correlated data errors on matched-field geoacoustic inversion for vertical array data is examined. The correlated errors stem from the inability to model the inhomogeneities in the environment resulting in an additional error term beyond ambient noise. Simulated data with these correlated errors are generated and then inverted with or without using the proper covariance matrix. Results show that the correlated error has a negative impact on geoacoustic parameter estimation if not accounted for properly.

On the effect of error correlation on linear inversions

We have examined the effect on linear helioseismic inversions of correlations in data errors, taking an example from one-dimensional rotational splitting inversion. Artificial data with correlated errors were generated and then inverted with or without using the proper covariance matrix. The effects of using incorrect covariance matrices, on solutions as well as on trade-offs, are discussed. It is found that improper account of the correlations can be deleterious to the faithfulness of the inversions, and yields incorrect error estimates, which under some circumstances can lead to misleading inferences.

The effect of correlated errors on the performance of TCP

Wireless channels are known to introduce correlated bursts of errors at the physical layer. Although these errors affect the performance at all layers of a protocol stack, their precise impact, especially at the higher layers, is not completely understood. We study the effect of error correlations on the throughput of a single transport control protocol (TCP) Tahoe connection. We find that TCP Tahoe performs better in the presence of clustered errors. This suggests that techniques that reduce channel memory may be undesirable.

Effect of error correlations and systematic errors on average array sidelobes

The effect of error correlations on the average error-induced sidelobe level of a planar array is studied. It is found that correlations among the random errors rapidly decrease the average error-sidelobe level (relative to the level when all errors are uncorrelated) as the correlation radius is increased. However, even though the average error-sidelobe level decreases, there are certain types of correlation that produce large local sidelobe increases. In particular, it is found that a one-dimensional correlation produces a large ridge of sidelobes in the plane orthogonal to the direction of the correlation. Simple results are derived from both the amplitude and the angular width of this ridge.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>