This paper proposes a new GMM estimator for spatial regression models with moving average errors. Monte Carlo results are given indicating that the GMM estimates are close to expectation and robust to non-normality, and the Bootstrap method is suggested as a way of testing the significance of the moving average parameter. The estimator is applied in a model of English real estate prices, in whi...

This dissertation studies the least squares estimator of a trend parameter in a simple linear regression model with multiple changepoints when the changepoint times are known. The error component in the model is allowed to be autocorrelated. The least squares estimator of the trend and the variance of the trend estimator are derived. Consistency and asymptotic normality of the trend estimator a...

SUMMARY In this paper we consiaer the problem wnere repeated measurements are taKen on eacn experimental unit of a randomized experimental design. If the design is a randomized complete block then traditionally a split-bloCK analysis is used. Here we consider an extension of tne traaitional sol it-bloCK analysis where \~e allow for several orthogonal autocorrelated error components deoending on...

Given the potential for rapid and microgeographical adaptation, ecologists increasingly are exploring evolutionary explanations for community patterns. Biotic selection can generate local adaptations that alter species interactions. Although some gene flow might be necessary to fuel local adaptation, higher gene flow can homogenise traits across regions and generate local maladaptation. Herein,...

• In this paper it is proposed a spatio-temporal area level linear mixed model involving spatially correlated and temporally autocorrelated random effects. An empirical best linear unbiased predictor (EBLUP) for small area parameters has been obtained under the proposed model. Using previous research in this area, analytical and bootstrap estimators of the mean squared prediction error (MSPE) o...

Multiscale monitoring of autocorrelated processes using wavelets analysis Huairui Guo , Kamran Paynabar & Jionghua Jin To cite this article: Huairui Guo , Kamran Paynabar & Jionghua Jin (2012) Multiscale monitoring of autocorrelated processes using wavelets analysis, IIE Transactions, 44:4, 312-326, DOI: 10.1080/0740817X.2011.609872 To link to this article: http://dx.doi.org/10.1080/0740817X.20...

We used previously accumulated skin conductance (SC) and EEG data to examine the effects of their respective autocorrelations upon hypothesis testing. We found that SC data remain autocorrelated for many seconds, and that EEG data remain autocorrelated for many fractions of a second depending upon filtering parameters. We show that the effect of these non-zero autocorrelations upon the interpre...

Motivated by steady-state simulation experiments, we consider the problem of estimating the marginal variance of a stationary time series. The usual estimator, the sample variance is biased for autocorrelated data. To reduce bias, other authors have suggested interlaced estimators. These estimators which like the sample variance are sums of squares are a generalization of the sample variance an...