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Autocorrelation Coefficient (autocorrelation + coefficient)
Selected AbstractsTowards an integrated computational tool for spatial analysis in macroecology and biogeographyGLOBAL ECOLOGY, Issue 4 2006Thiago Fernando L. V. B. Rangel ABSTRACT Because most macroecological and biodiversity data are spatially autocorrelated, special tools for describing spatial structures and dealing with hypothesis testing are usually required. Unfortunately, most of these methods have not been available in a single statistical package. Consequently, using these tools is still a challenge for most ecologists and biogeographers. In this paper, we present sam (Spatial Analysis in Macroecology), a new, easy-to-use, freeware package for spatial analysis in macroecology and biogeography. Through an intuitive, fully graphical interface, this package allows the user to describe spatial patterns in variables and provides an explicit spatial framework for standard techniques of regression and correlation. Moran's I autocorrelation coefficient can be calculated based on a range of matrices describing spatial relationships, for original variables as well as for residuals of regression models, which can also include filtering components (obtained by standard trend surface analysis or by principal coordinates of neighbour matrices). sam also offers tools for correcting the number of degrees of freedom when calculating the significance of correlation coefficients. Explicit spatial modelling using several forms of autoregression and generalized least-squares models are also available. We believe this new tool will provide researchers with the basic statistical tools to resolve autocorrelation problems and, simultaneously, to explore spatial components in macroecological and biogeographical data. Although the program was designed primarily for the applications in macroecology and biogeography, most of sam's statistical tools will be useful for all kinds of surface pattern spatial analysis. The program is freely available at http://www.ecoevol.ufg.br/sam (permanent URL at http://purl.oclc.org/sam/). [source] Mean Reversion in the Short Horizon Returns of UK PortfoliosJOURNAL OF BUSINESS FINANCE & ACCOUNTING, Issue 1-2 2001Patricia Chelley-Steeley This paper will show that short horizon stock returns for UK portfolios are more predictable than suggested by sample autocorrelation co-efficients. Four capitalisation based portfolios are constructed for the period 1976,1991. It is shown that the first order autocorrelation coefficient of monthly returns can explain no more than 10% of the variation in monthly portfolio returns. Monthly autocorrelation coefficients assume that each weekly return of the previous month contains the same amount of information. However, this will not be the case if short horizon returns contain predictable components which dissipate rapidly. In this case, the return of the most recent week would say a lot more about the future monthly portfolio return than other weeks. This suggests that when predicting future monthly portfolio returns more weight should be given to the most recent weeks of the previous month, because, the most recent weekly returns provide the most information about the subsequent months' performance. We construct a model which exploits the mean reverting characteristics of monthly portfolio returns. Using this model we forecast future monthly portfolio returns. When compared to forecasts that utilise the autocorrelation statistic the model which exploits the mean reverting characteristics of monthlyportfolio returns can forecast future returns better than the autocorrelation statistic, both in and out of sample. [source] Default Bayesian Priors for Regression Models with First-Order Autoregressive ResidualsJOURNAL OF TIME SERIES ANALYSIS, Issue 3 2003Malay Ghosh Abstract. The objective of this paper is to develop default priors when the parameter of interest is the autocorrelation coefficient in normal regression models with first-order autoregressive residuals. Jeffreys' prior as well as reference priors are found. These priors are compared in the light of how accurately the coverage probabilities of Bayesian credible intervals match the corresponding frequentist coverage probabilities. It is found that the reference priors have a definite edge over Jeffreys' prior in this respect. Also, the credible intervals based on these reference priors seem superior to similar intervals based on certain divergence measures. [source] Heterogeneous genetic structure in a Fagus crenata population in an old-growth beech forest revealed by microsatellite markersMOLECULAR ECOLOGY, Issue 5 2004Y. Asuka Abstract The within-population genetic structure of Fagus crenata in a 4-ha plot (200 × 200 m) of an old-growth beech forest was analysed using microsatellite markers. To assess the genetic structure, Moran's I spatial autocorrelation coefficient was calculated. Correlograms of Moran's I showed significant positive values less than 0.100 for short-distance classes, indicating weak genetic structure. The genetic structure within the population is created by limited seed dispersal, and is probably weakened by overlapping seed shadow, secondary seed dispersal, extensive pollen flow and the thinning process. Genetic structure was detected in a western subplot of 50 × 200 m with immature soils and almost no dwarf bamboos (Sasa spp.), where small and intermediate-sized individuals were distributed in aggregations with high density because of successful regeneration. By contrast, genetic structure was not found in an eastern subplot of the same size with mature soils and Sasa cover, where successful regeneration was prevented, and the density of the small and intermediate-sized individuals was low. Moreover, genetic structure of individuals in a small-size class (diameter at breast height < 12 cm) was more obvious than in a large-size class (diameter at breast height , 12 cm). The apparent genetic structure detected in the 4-ha plot was therefore probably the result of the structure in the western portion of the plot and in small and intermediate-sized individuals that successfully regenerated under the favourable environment. The heterogeneity in genetic structure presumably reflects variation in the density that should be affected by differences in regeneration dynamics associated with heterogeneity in environmental conditions. [source] Mean Reversion in the Short Horizon Returns of UK PortfoliosJOURNAL OF BUSINESS FINANCE & ACCOUNTING, Issue 1-2 2001Patricia Chelley-Steeley This paper will show that short horizon stock returns for UK portfolios are more predictable than suggested by sample autocorrelation co-efficients. Four capitalisation based portfolios are constructed for the period 1976,1991. It is shown that the first order autocorrelation coefficient of monthly returns can explain no more than 10% of the variation in monthly portfolio returns. Monthly autocorrelation coefficients assume that each weekly return of the previous month contains the same amount of information. However, this will not be the case if short horizon returns contain predictable components which dissipate rapidly. In this case, the return of the most recent week would say a lot more about the future monthly portfolio return than other weeks. This suggests that when predicting future monthly portfolio returns more weight should be given to the most recent weeks of the previous month, because, the most recent weekly returns provide the most information about the subsequent months' performance. We construct a model which exploits the mean reverting characteristics of monthly portfolio returns. Using this model we forecast future monthly portfolio returns. When compared to forecasts that utilise the autocorrelation statistic the model which exploits the mean reverting characteristics of monthlyportfolio returns can forecast future returns better than the autocorrelation statistic, both in and out of sample. [source] The Effect of the Estimation on Goodness-of-Fit Tests in Time Series ModelsJOURNAL OF TIME SERIES ANALYSIS, Issue 4 2005Yue Fang Abstract., We analyze, by simulation, the finite-sample properties of goodness-of-fit tests based on residual autocorrelation coefficients (simple and partial) obtained using different estimators frequently used in the analysis of autoregressive moving-average time-series models. The estimators considered are unconditional least squares, maximum likelihood and conditional least squares. The results suggest that although the tests based on these estimators are asymptotically equivalent for particular models and parameter values, their sampling properties for samples of the size commonly found in economic applications can differ substantially, because of differences in both finite-sample estimation efficiencies and residual regeneration methods. [source] Phylogenetic autocorrelation and heritability of geographic range size, shape and position of fiddler crabs, genus Uca (Crustacea, Decapoda)JOURNAL OF ZOOLOGICAL SYSTEMATICS AND EVOLUTIONARY RESEARCH, Issue 2 2010J. C. Nabout Abstract The aim of this study was to evaluate the levels of phylogenetic heritability of the geographical range size, shape and position for 88 species of fiddler crabs of the world, using phylogenetic comparative methods and simulation procedures to evaluate their fit to the neutral model of Brownian motion. The geographical range maps were compiled from literature, and range size was based on the entire length of coastline occupied by each species, and the position of each range was calculated as its latitudinal and longitudinal midpoint. The range shape of each species was based in fractal dimension (box-counting technique). The evolutionary patterns in the geographical range metrics were explored by phylogenetic correlograms using Moran's I autocorrelation coefficients, autoregressive method (ARM) and phylogenetic eigenvector regression (PVR). The correlograms were compared with those obtained by simulations of Brownian motion processes across phylogenies. The distribution of geographical range size of fiddler crabs is right-skewed and weak phylogenetic autocorrelation was observed. On the other hand, there was a strong phylogenetic pattern in the position of the range (mainly along longitudinal axis). Indeed, the ARM and PVR evidenced, respectively, that ca. 86% and 91% of the longitudinal midpoint could be explained by phylogenetic relationships among the species. The strong longitudinal phylogenetic pattern may be due to vicariant allopatric speciation and geographically structured cladogenesis in the group. The traits analysed (geographical range size and position) did not follow a Brownian motion process, thus suggesting that both adaptive ecological and evolutionary processes must be invoked to explain their dynamics, not following a simple neutral inheritance in the fiddler-crab evolution. Resumen El objetivo de este trabajo fue estimar los niveles de herencia filogenética existentes en la posición geográfica, forma y el tamaño de rango geográfico en 88 especies de cangrejo violinista del mundo, mediante simulaciones y métodos comparativos filogenéticos para así evaluar su ajuste al modelo neutro de evolución browniana. Los mapas de rango geográfico se obtuvieron de la literatura. La forma de rango geográfico fue estimada en la dimensión fractal. Los patrones evolutivos en el tamaño y forma del rango geográfico y la posición geográfica fueron explorados mediante correlogramas filogenéticos utilizando el índice I de Moran, coeficientes autorregresivos (ARM) y regressión por autovetores filogenéticos (PVR). Estos correlogramas fueron comparados con aquellos obtenidos mediante la simulación de procesos de evolución browniana en las filogenias. El tamaño y forma de rango geográfico del cangrejo violinista mostró una distribución apuntada hacia la derecha aunque no se encontró autocorrelación filogenética. Por otra parte, se observó un marcado patrón filogenético para la posición geográfica del rango (principalmente a lo largo del eje longitudinal). De hecho, el ARM y PVR evidenció respectivamente que cerca del 86% y 91% de la localización del punto medio longitudinal del rango se puede explicar mediante las relaciones filogenéticas existentes entre las especies. El fuerte patrón filogenético en la longitud podría ser debido a especiación alopátrica y a una cladogénesis estructurada geográficamente para el grupo, tal y como se propuso en las hipótesis. Los rasgos analizados (rango geográfico y posición geográfica) no siguieron un proceso de evolución browniana, sugiriendo pues que tanto los procesos evolutivos como la adaptación ecológica deberían ser tenidos en cuenta para explicar sus dinámicas, ya que el transcurso de la evolución del cangrejo violinista no se explica mediante un simple modelo de herencia neutra. [source] | ||||||||||