164 research outputs found

    DPRESS: Localizing estimates of predictive uncertainty

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    <p>Abstract</p> <p>Background</p> <p>The need to have a quantitative estimate of the uncertainty of prediction for QSAR models is steadily increasing, in part because such predictions are being widely distributed as tabulated values disconnected from the models used to generate them. Classical statistical theory assumes that the error in the population being modeled is independent and identically distributed (IID), but this is often not actually the case. Such inhomogeneous error (heteroskedasticity) can be addressed by providing an individualized estimate of predictive uncertainty for each particular new object <it>u</it>: the standard error of prediction <it>s</it><sub>u </sub>can be estimated as the non-cross-validated error <it>s</it><sub>t* </sub>for the closest object <it>t</it>* in the training set adjusted for its separation <it>d </it>from <it>u </it>in the descriptor space relative to the size of the training set.</p> <p><display-formula><graphic file="1758-2946-1-11-i1.gif"/></display-formula></p> <p>The predictive uncertainty factor <it>γ</it><sub>t* </sub>is obtained by distributing the internal predictive error sum of squares across objects in the training set based on the distances between them, hence the acronym: <it>D</it>istributed <it>PR</it>edictive <it>E</it>rror <it>S</it>um of <it>S</it>quares (DPRESS). Note that <it>s</it><sub>t* </sub>and <it>γ</it><sub>t*</sub>are characteristic of each training set compound contributing to the model of interest.</p> <p>Results</p> <p>The method was applied to partial least-squares models built using 2D (molecular hologram) or 3D (molecular field) descriptors applied to mid-sized training sets (<it>N </it>= 75) drawn from a large (<it>N </it>= 304), well-characterized pool of cyclooxygenase inhibitors. The observed variation in predictive error for the external 229 compound test sets was compared with the uncertainty estimates from DPRESS. Good qualitative and quantitative agreement was seen between the distributions of predictive error observed and those predicted using DPRESS. Inclusion of the distance-dependent term was essential to getting good agreement between the estimated uncertainties and the observed distributions of predictive error. The uncertainty estimates derived by DPRESS were conservative even when the training set was biased, but not excessively so.</p> <p>Conclusion</p> <p>DPRESS is a straightforward and powerful way to reliably estimate individual predictive uncertainties for compounds outside the training set based on their distance to the training set and the internal predictive uncertainty associated with its nearest neighbor in that set. It represents a sample-based, <it>a posteriori </it>approach to defining applicability domains in terms of localized uncertainty.</p

    Corporate Social Responsibility Strategies of Spanish Listed Firms and Controlling Shareholders’ Representatives

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    This article aims at analyzing how controlling shareholders’ representatives on boards affect corporate social responsibility (CSR) strategies (disclosing CSR matters) in Spain, a context characterized by high ownership concentration, one-tier boards, little board independence, weak legal protection for investors, and the presence of large shareholders, especially institutional shareholders. Furthermore, among controlling shareholders’ representatives, we can distinguish between those appointed by insurance companies and banks and those appointed by mutual funds, investment funds, and pension funds. The effect of these categories of directors on CSR strategies is, therefore, also analyzed. Our findings suggest that controlling shareholders’ representatives have a positive effect on CSR strategies, as do directors appointed by investment funds, pension funds, and mutual funds, while directors appointed by banks and insurance companies have no impact on CSR strategies. This analysis offers new insights into the role played by certain types of directors on CSR strategies

    Synthesising Corporate Responsibility on Organisational and Societal Levels of Analysis: An Integrative Perspective

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    This article develops an integrative perspective on corporate responsibility by synthesising competing perspectives on the responsibility of the corporation at the organisational and societal levels of analysis. We review three major corporate responsibility perspectives, which we refer to as economic, critical, and politico-ethical. We analyse the major potential uses and pitfalls of the perspectives, and integrate the debate on these two levels. Our synthesis concludes that when a society has a robust division of moral labour in place, the responsibility of a corporation may be economic (as suggested under the economic perspective) without jeopardising democracy and sustainability (as reported under the critical perspective). Moreover, the economic role of corporations neither signifies the absence of deliberative democratic mechanisms nor business practices extending beyond compliance (as called for under the politico-ethical perspective). The study underscores the value of integrating different perspectives and multiple levels of analysis to present comprehensive descriptions and prescriptions of the responsibility phenomenon

    The Communication of Corporate-NGO Partnerships: Analysis of Sainsbury’s Collaboration with Comic Relief.

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    This study focuses on CSR communication using the example of Corporate-NGO partnership between British supermarket chain Sainsbury’s and Comic Relief. Questionnaires were distributed to 40 participants asking them about their consumer behaviour and opinion on partnerships. Using thematic analysis, two main themes have been identified in the data set: some consumers are sceptical towards cross sector partnerships because they assume selfish reasons behind the collaboration and view them as corporate PR tool. On the other hand, the majority of consumers evaluate Corporate-NGO Partnerships as appropriate and a gain for society at large. The analysis showed that Sainsbury’s customers know about the partnership with Comic Relief while non-customers lack awareness, and that the most successful means of communication of partnerships is the supermarket promotion
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