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Simon D M White - One of the best experts on this subject based on the ideXlab platform.
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Autocorrelations of stellar light and mass at z 0 and 1 from sdss to deep2
arXiv: Cosmology and Nongalactic Astrophysics, 2011Co-Authors: Simon D M White, Yanmei Chen, Alison L Coil, Marc Davis, Gabriella De Lucia, Qi Guo, Yipeng Jing, Guinevere Kauffmann, Christopher N A Willmer, Wei ZhangAbstract:We present measurements of projected autocorrelation functions w_p(r_p) for the stellar mass of galaxies and for their light in the U, B and V bands, using data from the third data release of the DEEP2 Galaxy Redshift Survey and the final data release of the Sloan Digital Sky Survey (SDSS). We investigate the clustering bias of stellar mass and light by comparing these to projected Autocorrelations of dark matter estimated from the Millennium Simulations (MS) at z=1 and 0.07, the median redshifts of our galaxy samples. All of the autocorrelation and bias functions show systematic trends with spatial scale and waveband which are impressively similar at the two redshifts. This shows that the well-established environmental dependence of stellar populations in the local Universe is already in place at z=1. The recent MS-based galaxy formation simulation of Guo et al. (2011) reproduces the scale-dependent clustering of luminosity to an accuracy better than 30% in all bands and at both redshifts, but substantially overpredicts mass Autocorrelations at separations below about 2 Mpc. Further comparison of the shapes of our stellar mass bias functions with those predicted by the model suggests that both the SDSS and DEEP2 data prefer a fluctuation amplitude of sigma_8 0.8 rather than the sigma_8=0.9 assumed by the MS.
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Autocorrelations of stellar light and mass in the low redshift universe
Monthly Notices of the Royal Astronomical Society, 2010Co-Authors: Simon D M WhiteAbstract:The final data release of the Sloan Digital Sky Survey (SDSS) provides reliable photometry and spectroscopy for about half a million galaxies with median redshift 0.09. Here, we use these data to estimate projected autocorrelation functions w(p)(r(p)) for the light of galaxies in the five SDSS photometric bands. Comparison with the analogous stellar mass autocorrelation, estimated in a previous paper, shows that stellar luminosity is less strongly clustered than stellar mass in all bands and on all scales. Over the full non-linear range 10 h-1 kpc < r(p) < 10 h-1 Mpc our autocorrelation estimates are extremely well represented by power laws. The parameters of the corresponding spatial functions xi(r) = (r/r(0))gamma vary systematically from r(0) = 4.5 h-1 Mpc and gamma = -1.74 for the bluest band (the u band) to r(0) = 5.8 h-1 Mpc and gamma = -1.83 for the reddest one (the z band). These may be compared with r(0) = 6.1 h-1 Mpc and gamma = -1.84 for the stellar mass. Ratios of w(p)(r(p)) between two given wavebands are proportional to the mean colour of correlated stars at projected distance r(p) from a randomly chosen star. The ratio of the stellar mass to luminosity Autocorrelations measures an analogous mean stellar mass-to-light ratio (M(*)/L). All colours get redder and all mass-to-light ratios get larger with decreasing r(p), with the amplitude of the effects decreasing strongly to redder passbands. Even for the u band the effects are quite modest, with maximum shifts of about 0.1 in u - g and about 25 per cent in M(*)/L(u). These trends provide a precise characterization of the well-known dependence of stellar populations on environment.
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Autocorrelations of stellar light and mass in the low redshift universe
arXiv: Cosmology and Nongalactic Astrophysics, 2009Co-Authors: Simon D M WhiteAbstract:The final data release of the Sloan Digital Sky Survey (SDSS) provides reliable photometry and spectroscopy for about half a million galaxies with median redshift 0.09. Here we use these data to estimate projected autocorrelation functions w_p(r_p) for the light of galaxies in the five SDSS photometric bands. Comparison with the analogous stellar mass autocorrelation, estimated in a previous paper, shows that stellar luminosity is less strongly clustered than stellar mass in all bands and on all scales. Over the full nonlinear range 10 kpc/h < r_p < 10 Mpc/h our autocorrelation estimates are extremely well represented by power laws. The parameters of the corresponding spatial functions \xi(r) = (r/r_0)^\gamma vary systematically from r_0=4.5 Mpc/h and \gamma=-1.74 for the bluest band (the u band) to r_0=5.8 Mpc/h and \gamma=-1.83 for the reddest one (the z band). These may be compared with r_0=6.1 Mpc/h and \gamma=-1.84 for the stellar mass. Ratios of w_p(r_p) between two given wavebands are proportional to the mean colour of correlated stars at projected distance r_p from a randomly chosen star. The ratio of the stellar mass and luminosity Autocorrelations measures an analogous mean stellar mass-to-light ratio (M*/L). All colours get redder and all mass-to-light ratios get larger with decreasing r_p, with the amplitude of the effects decreasing strongly to redder passbands. Even for the u-band the effects are quite modest, with maximum shifts of about 0.1 in u-g and about 25% in M*/L_u. These trends provide a precise characterisation of the well-known dependence of stellar populations on environment.
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the distribution of stellar mass in the low redshift universe
Monthly Notices of the Royal Astronomical Society, 2009Co-Authors: Simon D M WhiteAbstract:We use a complete and uniform sample of almost half a million galaxies from the Sloan Digital Sky Survey to characterize the distribution of stellar mass in the low-redshift Universe. Galaxy abundances are well determined over almost four orders of magnitude in stellar mass and are reasonably but not perfectly fit by a Schechter function with characteristic stellar mass m(*) = 6.7 x 1010 M(circle dot) and with faint-end slope alpha = -1.155. For a standard cosmology and a standard stellar initial mass function, only 3.5 per cent of the baryons in the low-redshift Universe are locked up in stars. The projected autocorrelation function of stellar mass is robustly and precisely determined for r(p) < 30 h-1 Mpc. Over the range 10 h-1 kpc < r(p) < 10 h-1 Mpc, it is extremely well represented by a power law. The corresponding three-dimensional autocorrelation function is xi*(r) = (r/6.1 h-1 Mpc)-1.84. Relative to the dark matter, the bias of the stellar mass distribution is approximately constant on large scales, but varies by a factor of 5 for r(p) < 1 h-1 Mpc. This behaviour is approximately but not perfectly reproduced by current models for galaxy formation in the concordance Lambda cold dark matter cosmology. Detailed comparison suggests that a fluctuation amplitude Sigma(8) similar to 0.8 is preferred to the somewhat larger value adopted in the Millennium Simulation models with which we compare our data. This comparison also suggests that observations of stellar mass Autocorrelations as a function of redshift might provide a powerful test for the nature of Dark Energy.
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the distribution of stellar mass in the low redshift universe
arXiv: Cosmology and Nongalactic Astrophysics, 2009Co-Authors: Simon D M WhiteAbstract:We use a complete and uniform sample of almost half a million galaxies from the Sloan Digital Sky Survey to characterise the distribution of stellar mass in the low-redshift Universe. Galaxy abundances are well determined over almost four orders of magnitude in stellar mass, and are reasonably but not perfectly fit by a Schechter function with characteristic stellar mass m* = 6.7 x 10^10 M_sun and with faint-end slope \alpha = -1.155. For a standard cosmology and a standard stellar Initial Mass Function, only 3.5% of the baryons in the low-redshift Universe are locked up in stars. The projected autocorrelation function of stellar mass is robustly and precisely determined for r_p < 30 Mpc/h. Over the range 10 kpc/kpc < r_p < 10 Mpc/h it is extremely well represented by a power law. The corresponding three-dimensional autocorrelation function is \xi*(r) = (r/6.1 Mpc/h)^{-1.84}. Relative to the dark matter, the bias of the stellar mass distribution is approximately constant on large scales, but varies by a factor of five for r_p < 1 Mpc/h. This behaviour is approximately but not perfectly reproduced by current models for galaxy formation in the concordance LCDM cosmology. Detailed comparison suggests that a fluctuation amplitude \sigma_8 ~ 0.8 is preferred to the somewhat larger value adopted in the Millennium Simulation models with which we compare our data. This comparison also suggests that observations of stellar mass Autocorrelations as a function of redshift might provide a powerful test for the nature of Dark Energy.
Robert F Whitelaw - One of the best experts on this subject based on the ideXlab platform.
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partial adjustment or stale prices implications from stock index and futures return Autocorrelations
Review of Financial Studies, 2002Co-Authors: Donghyun Ahn, Jacob Boudoukh, Matthew Richardson, Robert F WhitelawAbstract:We investigate the relation between returns on stock indices and their corresponding futures contracts to evaluate potential explanations for the pervasive yet anomalous evidence of positive, short-horizon portfolio Autocorrelations. Using a simple theoretical framework, we generate empirical implications for both microstructure and partial adjustment models. The major findings are (i) return Autocorrelations of indices are generally positive even though futures contracts have Autocorrelations close to zero, and (ii) these autocorrelation differences are maintained under conditions favorable for spot-futures arbitrage and are most prevalent during low-volume periods. These results point toward microstructure-based explanations and away from explanations based on behavioral models.
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partial adjustment or stale prices implications from stock index and futures return Autocorrelations
2000Co-Authors: Donghyun Ahn, Jacob Boudoukh, Matthew Richardson, Robert F WhitelawAbstract:This paper investigates the relation between returns on stock indices and their corresponding futures contracts in order to evaluate potential explanations for the pervasive yet anomalous evidence of positive, short-horizon portfolio Autocorrelations. Using a simple theoretical framework, we generate empirical implications for both microstructure and partial adjustment models. These implications are then tested using futures data on 24 contracts across 15 countries. The major findings are (i) return Autocorrelations of indices tend to be positive even though their corresponding futures contracts have Autocorrelations close to zero, (ii) these autocorrelation differences between spot and futures markets are maintained even under conditions favorable for spot-futures arbitrage, and (iii) these autocorrelation differences are most prevalent during low volume periods. These results point us towards a market microstructure-based explanation for short-horizon Autocorrelations and away from explanations based on current popular behavioral models.
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a tale of three schools insights on Autocorrelations of short horizon stock returns
Social Science Research Network, 1998Co-Authors: Jacob Boudoukh, Matthew Richardson, Robert F WhitelawAbstract:This paper reexamines the autocorrelation patterns of short- horizon stock returns. We document empirical results which imply that these Autocorrelations have been overstated in the existing literature. Based on several new insights, we provide support for a market efficiency-based explanation of the evidence. Our analysis suggests institutional factors are the most likely source of the autocorrelation patterns.
Matthew Richardson - One of the best experts on this subject based on the ideXlab platform.
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partial adjustment or stale prices implications from stock index and futures return Autocorrelations
Review of Financial Studies, 2002Co-Authors: Donghyun Ahn, Jacob Boudoukh, Matthew Richardson, Robert F WhitelawAbstract:We investigate the relation between returns on stock indices and their corresponding futures contracts to evaluate potential explanations for the pervasive yet anomalous evidence of positive, short-horizon portfolio Autocorrelations. Using a simple theoretical framework, we generate empirical implications for both microstructure and partial adjustment models. The major findings are (i) return Autocorrelations of indices are generally positive even though futures contracts have Autocorrelations close to zero, and (ii) these autocorrelation differences are maintained under conditions favorable for spot-futures arbitrage and are most prevalent during low-volume periods. These results point toward microstructure-based explanations and away from explanations based on behavioral models.
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partial adjustment or stale prices implications from stock index and futures return Autocorrelations
2000Co-Authors: Donghyun Ahn, Jacob Boudoukh, Matthew Richardson, Robert F WhitelawAbstract:This paper investigates the relation between returns on stock indices and their corresponding futures contracts in order to evaluate potential explanations for the pervasive yet anomalous evidence of positive, short-horizon portfolio Autocorrelations. Using a simple theoretical framework, we generate empirical implications for both microstructure and partial adjustment models. These implications are then tested using futures data on 24 contracts across 15 countries. The major findings are (i) return Autocorrelations of indices tend to be positive even though their corresponding futures contracts have Autocorrelations close to zero, (ii) these autocorrelation differences between spot and futures markets are maintained even under conditions favorable for spot-futures arbitrage, and (iii) these autocorrelation differences are most prevalent during low volume periods. These results point us towards a market microstructure-based explanation for short-horizon Autocorrelations and away from explanations based on current popular behavioral models.
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a tale of three schools insights on Autocorrelations of short horizon stock returns
Social Science Research Network, 1998Co-Authors: Jacob Boudoukh, Matthew Richardson, Robert F WhitelawAbstract:This paper reexamines the autocorrelation patterns of short- horizon stock returns. We document empirical results which imply that these Autocorrelations have been overstated in the existing literature. Based on several new insights, we provide support for a market efficiency-based explanation of the evidence. Our analysis suggests institutional factors are the most likely source of the autocorrelation patterns.
Allaudeen Hameed - One of the best experts on this subject based on the ideXlab platform.
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time varying factors and cross Autocorrelations in short horizon stock returns
Social Science Research Network, 1997Co-Authors: Allaudeen HameedAbstract:In this paper I show that the lead-lag pattern between large and small market value portfolio returns is consistent with differential variations in their expected return components. I find that the larger predictability of returns on the portfolio of small stocks may be due to a higher exposure of these firms to persistent (time-varying) latent factors. Additional evidence suggest that the asymmetric predictability cannot be fully explained by lagged price adjustments to common factor shocks: (i) lagged returns on large stocks do not have strong causal effect on returns on small stocks; (ii) trading volume is positively related to own and cross-Autocorrelations in weekly portfolio returns; and (iii) significant cross- autocorrelation exists between current returns on large stocks and lagged returns on small stocks when trading volume is high.
Michael Fernandez - One of the best experts on this subject based on the ideXlab platform.
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proteometric modelling of protein conformational stability using amino acid sequence autocorrelation vectors and genetic algorithm optimised support vector machines
Molecular Simulation, 2008Co-Authors: Julio Caballero, Leyden Fernandez, Michael Fernandez, Pedro Sanchez, Jose Ignacio AbreuAbstract:The conformational stability of more than 1500 protein mutants was modelled by a proteometric approach using amino acid sequence autocorrelation vector (AASA) formalism. 48 amino acid/residue properties selected from the AAindex database weighted the AASA vectors. Genetic algorithm-optimised support vector machine (GA-SVM), trained with subset of AASA descriptors, yielded predictive classification and regression models of unfolding Gibbs free energy change (ΔΔG). Function mapping and binary SVM models correctly predicted about 50 and 80% of ΔΔG variances and signs in crossvalidation experiments, respectively. Test set prediction showed adequate accuracies about 70% for stable single and double point mutants. Conformational stability depended on Autocorrelations at medium and long ranges in the mutant sequences of general structural, physico-chemical and thermodynamical properties relative to protein hydration process. A preliminary version of the predictor is available online at http://gibk21.bse.kyutech....
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proteometric study of ghrelin receptor function variations upon mutations using amino acid sequence autocorrelation vectors and genetic algorithm based least square support vector machines
Journal of Molecular Graphics & Modelling, 2007Co-Authors: Julio Caballero, Leyden Fernandez, Jose Ignacio Abreu, Miguel Garriga, Simona Collina, Michael FernandezAbstract:Abstract Functional variations on the human ghrelin receptor upon mutations have been associated with a syndrome of short stature and obesity, of which the obesity appears to develop around puberty. In this work, we reported a proteometrics analysis of the constitutive and ghrelin-induced activities of wild-type and mutant ghrelin receptors using amino acid sequence autocorrelation (AASA) approach for protein structural information encoding. AASA vectors were calculated by measuring the Autocorrelations at sequence lags ranging from 1 to 15 on the protein primary structure of 48 amino acid/residue properties selected from the AAindex database. Genetic algorithm-based multilinear regression analysis (GA-MRA) and genetic algorithm-based least square support vector machines (GA-LSSVM) were used for building linear and non-linear models of the receptor activity. A genetic optimized radial basis function (RBF) kernel yielded the optimum GA-LSSVM models describing 88% and 95% of the cross-validation variance for the constitutive and ghrelin-induced activities, respectively. AASA vectors in the optimum models mainly appeared weighted by hydrophobicity-related properties. However, differently to the constitutive activity, the ghrelin-induced activity was also highly dependent of the steric features of the receptor.
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amino acid sequence autocorrelation vectors and bayesian regularized genetic neural networks for modeling protein conformational stability gene v protein mutants
Proteins, 2007Co-Authors: Leyden Fernandez, Julio Caballero, Jose Ignacio Abreu, Michael FernandezAbstract:Development of novel computational approaches for modeling protein properties from their primary structure is the main goal in applied proteomics. In this work, we reported the extension of the autocorrelation vector formalism to amino acid sequences for encoding protein structural information with modeling purposes. Amino acid sequence autocorrelation (AASA) vectors were calculated by measuring the Autocorrelations at sequence lags ranging from 1 to 15 on the protein primary structure of 48 amino acid/residue properties selected from the AAindex data base. A total of 720 AASA descriptors were tested for building predictive models of the change of thermal unfolding Gibbs free energy change (ΔΔG) of gene V protein upon mutation. In this sense, ensembles of Bayesian-regularized genetic neural networks (BRGNNs) were used for obtaining an optimum nonlinear model for the conformational stability. The ensemble predictor described about 88% and 66% variance of the data in training and test sets respectively. Furthermore, the optimum AASA vector subset not only helped to successfully model unfolding stability but also well distributed wild-type and gene V protein mutants on a stability self-organized map (SOM), when used for unsupervised training of competitive neurons. Proteins 2007. © 2007 Wiley-Liss, Inc.
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amino acid sequence autocorrelation vectors and ensembles of bayesian regularized genetic neural networks for prediction of conformational stability of human lysozyme mutants
Journal of Chemical Information and Modeling, 2006Co-Authors: Julio Caballero, Leyden Fernandez, Jose Ignacio Abreu, Michael FernandezAbstract:Development of novel computational approaches for modeling protein properties from their primary structure is a main goal in applied proteomics. In this work, we reported the extension of the autocorrelation vector formalism to amino acid sequences for encoding protein structural information with modeling purposes. Amino Acid Sequence Autocorrelation (AASA) vectors were calculated by measuring the Autocorrelations at sequence lags ranging from 1 to 15 on the protein primary structure of 48 amino acid/residue properties selected from the AAindex database. A total of 720 AASA descriptors were tested for building predictive models of the thermal unfolding Gibbs free energy change of human lysozyme mutants. In this sense, ensembles of Bayesian-Regularized Genetic Neural Networks (BRGNNs) were used for obtaining an optimum nonlinear model for the conformational stability. The ensemble predictor described about 88% and 68% variance of the data in training and test sets, respectively. Furthermore, the optimum AA...
