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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...

We propose a multiplicative speci cation of a discrete choice model that renders choice probabilities independent of the scale of the utility. The scale can thus be random with unspeci ed distribution. The model mostly outperforms the classical additive formulation over a range of stated choice data sets. In some cases, the improvement in likelihood is greater than that obtained from adding obs...

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...