In this essay the author compares Virginia Woolf’s A Room of One’s Own (1928) to Caitlin Moran’s How To Be a Woman (2012). The two texts have both been described as feminist manifests of their time. The essay focuses on differences and similarities between the two texts, mainly focusing on the authors’ reasons for writing their texts and on the rhetoric they use to reach the audience. The comparison shows that there are many similarities between the texts, given the historical context they were written in. For instance, both Woolf and Moran use humor as rhetorical means and they both see cooperation between women and men as the solution for a better future.

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In order to account for spatial correlation in residuals in regression models for areal and lattice data, different disciplines have developed distinct approaches. Bayesian spatial statistics typically has used a Gaussian conditional autoregressive (CAR) prior on random effects, while geographers utilize Moran's I statistic as a measure of spatial autocorrelation and the basis for creating spatial models. Recent work in both fields has recognized and built on a common feature of the two approaches, specifically the implicit or explicit incorporation into the linear predictor of eigenvectors of a matrix representing the spatial neighborhood structure. The inclusion of appropriate choices of these vectors effectively reduces the spatial autocorrelation found in the residuals. We begin with extensive simulation studies to compare Bayesian CAR models, Restricted Spatial Regression (RSR), Bayesian Spatial Filtering (BSF), and Eigenvector Spatial Filtering (ESF) with respect to estimation of fixed-effect coefficients, prediction, and reduction of residual spatial autocorrelation. The latter three models incorporate the neighborhood structure of the data through the eigenvectors of a Moran operator. We propose an alternative selection algorithm for all candidate predictors that avoids the ad hoc approach of RSR and selects on both model fit and reduction of autocorrelation in the residuals. The algorithm depends on the marginal posterior density a quantity that measures what proportion of the total variance can be explained by the measurement error. The algorithm selects candidate predictors that lead to a high probability that this quantity is large in addition to having large marginal posterior inclusion probabilities (PIP) according to model fit. Two methods were constructed. The first is based on orthogonalizing all of the candidate predictors while the second can be applied to the design matrix of candidate predictors without orthogonalization. Our algorithm was applied to the same simulated data that compared the RSR, BSF and ESF models. Although our algorithm performs similarly to the established methods, the first of our selection methods shows an improvement in execution time. In addition, our approach is a statistically sound, fully Bayesian method.

This article summarizes the ideas behind a few programs we developed for spatial data analysis in EViews and MATLAB. They allow the user to check for spatial autocorrelation using Moran's I and provide a spatial filtering procedure based on the Gi statistic by Getis and Ord (1992). We have also implemented graphical tools like Moran Scatterplots for the detection of outliers or local spatial clusters.

The popular image of the West in the nineteenth and early twentieth centuries incorporates radically opposing images: the West is viewed as a Garden of Eden at times, but it is also frequently seen as violent, a land inimical to man. The region both attracted and repelled. Among those attracted were artists who carried back some of the first images of the land. Thomas Moran (1837-1926) became associated quite early with the West because a pair of his paintings of western canyons was purchased by the United States Government.

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Dissertação (mestrado)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Estatística, 2015. / Submitted by Fernanda Percia França (fernandafranca@bce.unb.br) on 2016-03-18T19:01:43Z No. of bitstreams: 1 2015_TomazBackCarrijo.pdf: 715083 bytes, checksum: ead16a8f8890378a60bd9261544e56da (MD5) / Approved for entry into archive by Raquel Viana(raquelviana@bce.unb.br) on 2016-03-21T22:09:17Z (GMT) No. of bitstreams: 1 2015_TomazBackCarrijo.pdf: 715083 bytes, checksum: ead16a8f8890378a60bd9261544e56da (MD5) / Made available in DSpace on 2016-03-21T22:09:17Z (GMT). No. of bitstreams: 1 2015_TomazBackCarrijo.pdf: 715083 bytes, checksum: ead16a8f8890378a60bd9261544e56da (MD5) / Devido a sua simplicidade, o índice I de Moran é a estatística mais famosa e largamente utilizada quando se deseja mensurar a autocorrelação em dados georreferenciados. A primeira parte do trabalho revisou o estado da arte desse índice, objetivando entender sua evolução e características intrínsecas. Logo em seguida, demonstrou-se que essa estatística apresenta limitações quando a quantidade de dados no sistema é pequena. Além disso, propôs uma modificação do índice I de Moran, introduzindo no coeficiente de correlação de Pearson o conceito de modelagem espacial autorregressiva de primeira ordem. Os resultados dessa proposta se mostraram bastante coerentes, principalmente quando o sistema possui poucos dados. / The Moran's I is the most famous and widely used spatial statistic when we want to measure spatial autocorrelation in geo-referenced data. The first part of this work reviewed the state of the art of Moran's I, in order to understand its evolution and intrinsic characteristics. Next, we showed that this statistic has limitations when the sample size of the system is small. In addition, we present a proposal of modification on Moran's I, introducing on the Pearson correlation coefficient the concept of the first order autoregressive spatial modelling. The results of this proposal were very consistent, especially when the system has few data.