Institution
SAS Institute
Company•Cary, North Carolina, United States•
About: SAS Institute is a company organization based out in Cary, North Carolina, United States. It is known for research contribution in the topics: Population & Set (abstract data type). The organization has 1174 authors who have published 1513 publications receiving 43448 citations. The organization is also known as: SAS Institute, Inc..
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Food and Drug Administration1, GE Healthcare2, Thermo Fisher Scientific3, Illumina4, Agilent Technologies5, National Institutes of Health6, Applied Biosystems7, University of Toledo8, Stratagene9, United States Environmental Protection Agency10, University of Massachusetts Boston11, Clinical Data, Inc12, University of California, Los Angeles13, SAS Institute14, Biogen Idec15, Yale University16, Cold Spring Harbor Laboratory17, Discovery Institute18, Stanford University19, Harvard University20, Vanderbilt University21, University of Texas at Dallas22, University of Oslo23, Novartis24, University of Texas MD Anderson Cancer Center25, Luminex Corporation26, Wake Forest University27, University of Illinois at Urbana–Champaign28
TL;DR: This study describes the experimental design and probe mapping efforts behind the MicroArray Quality Control project and shows intraplatform consistency across test sites as well as a high level of interplatform concordance in terms of genes identified as differentially expressed.
Abstract: Over the last decade, the introduction of microarray technology has had a profound impact on gene expression research. The publication of studies with dissimilar or altogether contradictory results, obtained using different microarray platforms to analyze identical RNA samples, has raised concerns about the reliability of this technology. The MicroArray Quality Control (MAQC) project was initiated to address these concerns, as well as other performance and data analysis issues. Expression data on four titration pools from two distinct reference RNA samples were generated at multiple test sites using a variety of microarray-based and alternative technology platforms. Here we describe the experimental design and probe mapping efforts behind the MAQC project. We show intraplatform consistency across test sites as well as a high level of interplatform concordance in terms of genes identified as differentially expressed. This study provides a resource that represents an important first step toward establishing a framework for the use of microarrays in clinical and regulatory settings.
1,987 citations
University of California, Santa Barbara1, University of Texas at Austin2, University of Wrocław3, Dresden University of Technology4, University of Tartu5, Gulu University6, Middle East University7, Stockholm University8, University of the Punjab9, University of Nigeria, Nsukka10, Istanbul University11, Franklin & Marshall College12, Norwegian University of Science and Technology13, University of Algiers14, Australian National University15, Russian State University for the Humanities16, Russian Academy of Sciences17, İzmir University of Economics18, University of Social Sciences and Humanities19, Université catholique de Louvain20, Ankara University21, Pontifical Catholic University of Peru22, Cumhuriyet University23, University of the Republic24, ISCTE – University Institute of Lisbon25, The Chinese University of Hong Kong26, National Autonomous University of Mexico27, University of Pécs28, University of Constantine the Philosopher29, University of Maribor30, University of Zagreb31, University of Malaya32, Central University of Finance and Economics33, University of Crete34, University of Primorska35, Institute of Molecular and Cell Biology36, University of Amsterdam37, Catholic University of the Sacred Heart38, VU University Amsterdam39, University of Granada40, University of Delhi41, University of Havana42, Pontifical Catholic University of Rio de Janeiro43, University of Vienna44, Universiti Utara Malaysia45, Vilnius University46, University of British Columbia47, University of Sussex48, Romanian Academy49, Slovak Academy of Sciences50, Comenius University in Bratislava51, University of Monterrey52, SAS Institute53, DHA Suffa University54, Pontifical Catholic University of Chile55, South-West University "Neofit Rilski"56, University of São Paulo57, Kyung Hee University58, University of Ljubljana59
TL;DR: This work combines this large cross-cultural sample with agent-based models to compare eight hypothesized models of human mating markets and finds that this cross-culturally universal pattern of mate choice is most consistent with a Euclidean model of mate preference integration.
Abstract: Humans express a wide array of ideal mate preferences. Around the world, people desire romantic partners who are intelligent, healthy, kind, physically attractive, wealthy, and more. In order for these ideal preferences to guide the choice of actual romantic partners, human mating psychology must possess a means to integrate information across these many preference dimensions into summaries of the overall mate value of their potential mates. Here we explore the computational design of this mate preference integration process using a large sample of n = 14,487 people from 45 countries around the world. We combine this large cross-cultural sample with agent-based models to compare eight hypothesized models of human mating markets. Across cultures, people higher in mate value appear to experience greater power of choice on the mating market in that they set higher ideal standards, better fulfill their preferences in choice, and pair with higher mate value partners. Furthermore, we find that this cross-culturally universal pattern of mate choice is most consistent with a Euclidean model of mate preference integration.
1,827 citations
TL;DR: In this article, a pseudo-likelihood estimation procedure is developed to fit this class of mixed models based on an approximate marginal model for the mean response, implemented via iterated fitting of a weighted Gaussian linear mixed model to a modified dependent variable.
Abstract: A useful extension of the generalized linear model involves the addition of random effects andlor correlated errors. A pseudo-likelihood estimation procedure is developed to fit this class of mixed models based on an approximate marginal model for the mean response. The procedure is implemented via iterated fitting of a weighted Gaussian linear mixed model to a modified dependent variable. The approach allows for flexible specification of covariance structures for both the random effects and the correlated errors. An estimate of an additional dispersion parameter for underlying exponential family distributions is optionally automatic. The method allows for subject-specific and population-averaged inference, and the Salamander data example from McCullagh and Nelder (1989) is used to illustrate both.
1,256 citations
TL;DR: A statistical approach is presented that allows direct control over the percentage of false positives in such a list of differentially expressed genes and, under certain reasonable assumptions, improves on existing methods with respect to the percentages of false negatives.
Abstract: The determination of a list of differentially expressed genes is a basic objective in many cDNA microarray experiments. We present a statistical approach that allows direct control over the percentage of false positives in such a list and, under certain reasonable assumptions, improves on existing methods with respect to the percentage of false negatives. The method accommodates a wide variety of experimental designs and can simultaneously assess significant differences between multiple types of biological samples. Two interconnected mixed linear models are central to the method and provide a flexible means to properly account for variability both across and within genes. The mixed model also provides a convenient framework for evaluating the statistical power of any particular experimental design and thus enables a researcher to a priori select an appropriate number of replicates. We also suggest some basic graphics for visualizing lists of significant genes. Analyses of published experiments studying hu...
1,170 citations
27 Jan 2017
TL;DR: In this article, the effects of correlation on statistical inference have been investigated in the context of spatial analysis. But the authors focus on the use of non-Euclidean distances in Geostatistics.
Abstract: INTRODUCTION The Need for Spatial Analysis Types of Spatial Data Autocorrelation-Concept and Elementary Measures Autocorrelation Functions The Effects of Autocorrelation on Statistical Inference Chapter Problems SOME THEORY ON RANDOM FIELDS Stochastic Processes and Samples of Size One Stationarity, Isotropy, and Heterogeneity Spatial Continuity and Differentiability Random Fields in the Spatial Domain Random Fields in the Frequency Domain Chapter Problems MAPPED POINT PATTERNS Random, Aggregated, and Regular Patterns Binomial and Poisson Processes Testing for Complete Spatial Randomness Second-Order Properties of Point Patterns The Inhomogeneous Poisson Process Marked and Multivariate Point Patterns Point Process Models Chapter Problems SEMIVARIOGRAM AND COVARIANCE FUNCTION ANALYSIS AND ESTIMATION Introduction Semivariogram and Covariogram Covariance and Semivariogram Models Estimating the Semivariogram Parametric Modeling Nonparametric Estimation and Modeling Estimation and Inference in the Frequency Domain On the Use of Non-Euclidean Distances in Geostatistics Supplement: Bessel Functions Chapter Problems SPATIAL PREDICTION AND KRIGING Optimal Prediction in Random Fields Linear Prediction-Simple and Ordinary Kriging Linear Prediction with a Spatially Varying Mean Kriging in Practice Estimating Covariance Parameters Nonlinear Prediction Change of Support On the Popularity of the Multivariate Gaussian Distribution Chapter Problems SPATIAL REGRESSION MODELS Linear Models with Uncorrelated Errors Linear Models with Correlated Errors Generalized Linear Models Bayesian Hierarchical Models Chapter Problems SIMULATION OF RANDOM FIELDS Unconditional Simulation of Gaussian Random Fields Conditional Simulation of Gaussian Random Fields Simulated Annealing Simulating from Convolutions Simulating Point Processes Chapter Problems NON-STATIONARY COVARIANCE Types of Non-Stationarity Global Modeling Approaches Local Stationarity SPATIO-TEMPORAL PROCESSES A New Dimension Separable Covariance Functions Non-Separable Covariance Functions The Spatio-Temporal Semivariogram Spatio-Temporal Point Processes
1,022 citations
Authors
Showing all 1177 results
| Name | H-index | Papers | Citations |
|---|---|---|---|
| Russell D. Wolfinger | 57 | 154 | 19983 |
| Yonggang Yao | 53 | 132 | 10681 |
| Olga Pechanova | 38 | 144 | 3520 |
| Jun Liu | 37 | 90 | 5760 |
| Peter Goos | 35 | 251 | 4263 |
| Hee Sun Park | 34 | 177 | 3526 |
| Vidyadhar G. Kulkarni | 33 | 147 | 5073 |
| Tao Hong | 32 | 76 | 5048 |
| Ankur Gupta | 31 | 230 | 4000 |
| Bradley Jones | 31 | 123 | 3323 |
| Dahlia M. Nielsen | 29 | 49 | 7508 |
| Yan Xu | 28 | 153 | 2478 |
| Li Li | 25 | 49 | 5666 |
| Randy B Machemehl | 24 | 229 | 2666 |
| Michael C. Edwards | 24 | 61 | 2111 |