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Profile: Aris Spanos (Virginia Tech)
  1. Aris Spanos (2013). A Frequentist Interpretation of Probability for Model-Based Inductive Inference. Synthese 190 (9):1555-1585.
    The main objective of the paper is to propose a frequentist interpretation of probability in the context of model-based induction, anchored on the Strong Law of Large Numbers (SLLN) and justifiable on empirical grounds. It is argued that the prevailing views in philosophy of science concerning induction and the frequentist interpretation of probability are unduly influenced by enumerative induction, and the von Mises rendering, both of which are at odds with frequentist model-based induction that dominates current practice. The differences between (...)
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  2. Aris Spanos (2013). Who Should Be Afraid of the Jeffreys-Lindley Paradox? Philosophy of Science 80 (1):73-93.
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  3. Aris Spanos (2011). Foundational Issues in Statistical Modeling : Statistical Model Specification. Philosophy of Science 2 (47):146-178.
    Statistical model specification and validation raise crucial foundational problems whose pertinent resolution holds the key to learning from data by securing the reliability of frequentist inference. The paper questions the judiciousness of several current practices, including the theory-driven approach, and the Akaike-type model selection procedures, arguing that they often lead to unreliable inferences. This is primarily due to the fact that goodness-of-fit/prediction measures and other substantive and pragmatic criteria are of questionable value when the estimated model is statistically misspecified. Foisting (...)
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  4. Deborah G. Mayo & Aris Spanos (eds.) (2010). Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science. Cambridge University Press.
    Explores the nature of error and inference, drawing on exchanges on experimental reasoning, reliability, and the objectivity of science.
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  5. Deborah G. Mayo & Aris Spanos (2010). Introduction and Background. In Deborah G. Mayo & Aris Spanos (eds.), Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science. Cambridge University Press.
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  6. Oron Shagrir, John D. Norton, Holger Andreas, Jouni-Matti Kuukkanen, Aris Spanos, Eckhart Arnold, Elliott Sober, Peter Gildenhuys & Adela Helena Roszkowski (2010). 1. Marr on Computational-Level Theories Marr on Computational-Level Theories (Pp. 477-500). Philosophy of Science 77 (4).
     
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  7. Aris Spanos (2010). Graphical Causal Modeling and Error Statistics : Exchanges with Clark Glymour. In Deborah G. Mayo & Aris Spanos (eds.), Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science. Cambridge University Press. 364.
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  8. Aris Spanos (2010). Is Frequentist Testing Vulnerable to the Base-Rate Fallacy? Philosophy of Science 77 (4):565-583.
    This article calls into question the charge that frequentist testing is susceptible to the base-rate fallacy. It is argued that the apparent similarity between examples like the Harvard Medical School test and frequentist testing is highly misleading. A closer scrutiny reveals that such examples have none of the basic features of a proper frequentist test, such as legitimate data, hypotheses, test statistics, and sampling distributions. Indeed, the relevant error probabilities are replaced with the false positive/negative rates that constitute deductive calculations (...)
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  9. Aris Spanos (2010). On a New Philosophy of Frequentist Inference : Exchanges with David Cox and Deborah G. Mayo. In Deborah G. Mayo & Aris Spanos (eds.), Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science. Cambridge University Press. 315.
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  10. Aris Spanos (2010). The Discovery of Argon: A Case for Learning From Data? Philosophy of Science 77 (3):359-380.
    Rayleigh and Ramsay discovered the inert gas argon in the atmospheric air in 1895 using a carefully designed sequence of experiments guided by an informal statistical analysis of the resulting data. The primary objective of this article is to revisit this remarkable historical episode in order to make a case that the error‐statistical perspective can be used to bring out and systematize (not to reconstruct) these scientists' resourceful ways and strategies for detecting and eliminating error, as well as dealing with (...)
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  11. Aris Spanos (2010). Theory Testing in Economics and the Error-Statistical Perspective. In Deborah G. Mayo & Aris Spanos (eds.), Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science. Cambridge University Press.
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  12. Aris Spanos (2009). Error in Economics and the Error Statistical Approach. Economics and Philosophy 25 (02):206-.
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  13. Aris Spanos (2009). Error in Economics and the Error Statistical Approach Error in Economics. Towards a More Evidence-Based Methodology , Julian Reiss, Routledge, 2007, XXIV + 246 Pages. [REVIEW] Economics and Philosophy 25 (2):206-210.
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  14. Aris Spanos (2008). Review of ST Ziliak and DN McCloskey's The Cult of Statistical Significance. [REVIEW] Erasmus Journal for Philosophy and Economics 1 (1):154-164.
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  15. Aris Spanos (2007). Curve Fitting, the Reliability of Inductive Inference, and the Error-Statistical Approach. Philosophy of Science 74 (5):1046-1066.
    The main aim of this paper is to revisit the curve fitting problem using the reliability of inductive inference as a primary criterion for the ‘fittest' curve. Viewed from this perspective, it is argued that a crucial concern with the current framework for addressing the curve fitting problem is, on the one hand, the undue influence of the mathematical approximation perspective, and on the other, the insufficient attention paid to the statistical modeling aspects of the problem. Using goodness-of-fit as the (...)
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  16. Deborah G. Mayo & Aris Spanos (2006). Severe Testing as a Basic Concept in a Neyman–Pearson Philosophy of Induction. British Journal for the Philosophy of Science 57 (2):323-357.
    Despite the widespread use of key concepts of the Neyman–Pearson (N–P) statistical paradigm—type I and II errors, significance levels, power, confidence levels—they have been the subject of philosophical controversy and debate for over 60 years. Both current and long-standing problems of N–P tests stem from unclarity and confusion, even among N–P adherents, as to how a test's (pre-data) error probabilities are to be used for (post-data) inductive inference as opposed to inductive behavior. We argue that the relevance of error probabilities (...)
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  17. Aris Spanos (2006). Revisiting the Omitted Variables Argument: Substantive Vs. Statistical Adequacy. Journal of Economic Methodology 13 (2):179-218.
    The problem of omitted variables is commonly viewed as a statistical misspecification issue which renders the inference concerning the influence of X t on yt unreliable, due to the exclusion of certain relevant factors W t . That is, omitting certain potentially important factors W t may confound the influence of X t on yt . The textbook omitted variables argument attempts to assess the seriousness of this unreliability using the sensitivity of the estimator to the inclusion/exclusion of W t (...)
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  18. Deborah G. Mayo & Aris Spanos (2004). Methodology in Practice: Statistical Misspecification Testing. Philosophy of Science 71 (5):1007-1025.
    The growing availability of computer power and statistical software has greatly increased the ease with which practitioners apply statistical methods, but this has not been accompanied by attention to checking the assumptions on which these methods are based. At the same time, disagreements about inferences based on statistical research frequently revolve around whether the assumptions are actually met in the studies available, e.g., in psychology, ecology, biology, risk assessment. Philosophical scrutiny can (...)
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  19. Aris Spanos (2000). Revisiting Data Mining: 'Hunting' with or Without a License. Journal of Economic Methodology 7 (2):231-264.
    The primary objective of this paper is to revisit a number of empirical modelling activities which are often characterized as data mining, in an attempt to distinguish between the problematic and the non-problematic cases. The key for this distinction is provided by the notion of error-statistical severity. It is argued that many unwarranted data mining activities often arise because of inherent weaknesses in the Traditional Textbook (TT) methodology. Using the Probabilistic Reduction (PR) approach to empirical modelling, it is argued that (...)
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