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  • Computational Methods, Genomics  (3)
  • Belief, L13 - Oligopoly and Other Imperfect Markets, L81 - Retail and Wholesale Trade  (2)
  • Oxford University Press  (5)
  • American Geophysical Union (AGU)
  • 1
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    Hysteresis, Price Acceptance, and Reference Prices (2016)
    Oxford University Press
    Details
    Publication Date: 2016-04-24
    Description: The existence of price thresholds in grocery retailing is well-documented. Most authors explain the existence of price thresholds using Assimilation-Contrast Theory, Adaptation Level Theory, or Prospect Theory. However, each of these theories is untenable if consumers are believed to behave rationally. We offer a theoretical explanation grounded in Real Options Theory (ROT) and economic hysteresis. We test the ROT hypothesis against three plausible alternatives using a maximum likelihood friction model that we augment for unobserved heterogeneity. Our findings support the ROT hypothesis, and suggest that the existence of price thresholds in aggregate data are driven by a common recognition of real option values, which do not disappear with the inclusion of consumer heterogeneity.
    Keywords: D12 - Consumer Economics: Empirical Analysis, D83 - Search ; Learning ; Information and Knowledge ; Communication ; Belief, L13 - Oligopoly and Other Imperfect Markets, L81 - Retail and Wholesale Trade ; e-Commerce
    Print ISSN: 0002-9092
    Electronic ISSN: 1467-8276
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition , Economics
    Published by Oxford University Press
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  • 2
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    SLiMPrints: conservation-based discovery of functional motif fingerprints in intrinsically disordered protein regions (2012)
    Oxford University Press
    Details
    Publication Date: 2012-11-25
    Description: Large portions of higher eukaryotic proteomes are intrinsically disordered, and abundant evidence suggests that these unstructured regions of proteins are rich in regulatory interaction interfaces. A major class of disordered interaction interfaces are the compact and degenerate modules known as short linear motifs (SLiMs). As a result of the difficulties associated with the experimental identification and validation of SLiMs, our understanding of these modules is limited, advocating the use of computational methods to focus experimental discovery. This article evaluates the use of evolutionary conservation as a discriminatory technique for motif discovery. A statistical framework is introduced to assess the significance of relatively conserved residues, quantifying the likelihood a residue will have a particular level of conservation given the conservation of the surrounding residues. The framework is expanded to assess the significance of groupings of conserved residues, a metric that forms the basis of SLiMPrints (short linear motif fingerprints), a de novo motif discovery tool. SLiMPrints identifies relatively overconstrained proximal groupings of residues within intrinsically disordered regions, indicative of putatively functional motifs. Finally, the human proteome is analysed to create a set of highly conserved putative motif instances, including a novel site on translation initiation factor eIF2A that may regulate translation through binding of eIF4E.
    Keywords: Computational Methods, Genomics
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
    Published by Oxford University Press
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  • 3
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    Attribute Search in Online Retailing (2017)
    Oxford University Press
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    Publication Date: 2017-01-05
    Description: Online retailing has created an empirical opportunity to examine consumer search behavior using click stream data. In this article we examine the implications of greater variety online for consumer search intensity, and equilibrium prices. We test our hypothesis using consumer data on online search and purchase behavior from the comScore Web Behavior Panel. We find that search intensity systematically decreases in categories with broader product ranges, and equilibrium prices rise. Our findings suggests that broader product ranges in online retail markets can produce anti-competitive effects that are mediated through equilibrium responses in consumer search behavior.
    Keywords: D12 - Consumer Economics: Empirical Analysis, D83 - Search ; Learning ; Information and Knowledge ; Communication ; Belief, L13 - Oligopoly and Other Imperfect Markets, L81 - Retail and Wholesale Trade ; e-Commerce
    Print ISSN: 0002-9092
    Electronic ISSN: 1467-8276
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition , Economics
    Published by Oxford University Press
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  • 4
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    svaseq: removing batch effects and other unwanted noise from sequencing data (2014)
    Oxford University Press
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    Publication Date: 2014-11-28
    Description: It is now known that unwanted noise and unmodeled artifacts such as batch effects can dramatically reduce the accuracy of statistical inference in genomic experiments. These sources of noise must be modeled and removed to accurately measure biological variability and to obtain correct statistical inference when performing high-throughput genomic analysis. We introduced surrogate variable analysis (sva) for estimating these artifacts by (i) identifying the part of the genomic data only affected by artifacts and (ii) estimating the artifacts with principal components or singular vectors of the subset of the data matrix. The resulting estimates of artifacts can be used in subsequent analyses as adjustment factors to correct analyses. Here I describe a version of the sva approach specifically created for count data or FPKMs from sequencing experiments based on appropriate data transformation. I also describe the addition of supervised sva (ssva) for using control probes to identify the part of the genomic data only affected by artifacts. I present a comparison between these versions of sva and other methods for batch effect estimation on simulated data, real count-based data and FPKM-based data. These updates are available through the sva Bioconductor package and I have made fully reproducible analysis using these methods available from: https://github.com/jtleek/svaseq .
    Keywords: Computational Methods, Genomics
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
    Published by Oxford University Press
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  • 5
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    A scale-space method for detecting recurrent DNA copy number changes with analytical false discovery rate control (2013)
    Oxford University Press
    Details
    Publication Date: 2013-05-04
    Description: Tumor formation is partially driven by DNA copy number changes, which are typically measured using array comparative genomic hybridization, SNP arrays and DNA sequencing platforms. Many techniques are available for detecting recurring aberrations across multiple tumor samples, including CMAR, STAC, GISTIC and KC-SMART. GISTIC is widely used and detects both broad and focal (potentially overlapping) recurring events. However, GISTIC performs false discovery rate control on probes instead of events. Here we propose Analytical Multi-scale Identification of Recurrent Events, a multi-scale Gaussian smoothing approach, for the detection of both broad and focal (potentially overlapping) recurring copy number alterations. Importantly, false discovery rate control is performed analytically (no need for permutations) on events rather than probes. The method does not require segmentation or calling on the input dataset and therefore reduces the potential loss of information due to discretization. An important characteristic of the approach is that the error rate is controlled across all scales and that the algorithm outputs a single profile of significant events selected from the appropriate scales. We perform extensive simulations and showcase its utility on a glioblastoma SNP array dataset. Importantly, ADMIRE detects focal events that are missed by GISTIC, including two events involving known glioma tumor-suppressor genes: CDKN2C and NF1.
    Keywords: Computational Methods, Genomics
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
    Published by Oxford University Press
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