1,721,074 research outputs found
Effects of Causes and Causes of Effects
No full textWe describe and contrast two distinct problem areas for statistical causality: studying the likely effects of an intervention (effects of causes) and studying whether there is a causal link between the observed exposure and outcome in an individual case (causes of effects). For each of these, we introduce and compare various formal frameworks that have been proposed for that purpose, including the decision-Theoretic approach, structural equations, structural and stochastic causal models, and potential outcomes. We argue that counterfactual concepts are unnecessary for studying effects of causes but are needed for analyzing causes of effects. They are, however, subject to a degree of arbitrariness, which can be reduced, though not in general eliminated, by taking account of additional structure in the problem
Theory and Applications of Proper Scoring Rules
No full textAscoring rule S(x; q) provides away of judging the quality of a quoted probability density q for a random variable X in the light of its outcome x. It is called proper if honesty is your best policy, i.e., when you believe X has density p, your expected score is optimised by the choice q = p. The most celebrated proper scoring rule is the logarithmic score, S(x; q) = -log q(x): this is the only proper scoring rule that is local, in the sense of depending on the density function q only through its value at the observed value x. It is closely connected with likelihood inference, with communication theory, and with minimum description length model selection. However, every statistical decision problem induces a proper scoring rule, so there is a very wide variety of these. Many of them have additional interesting structure and properties. At a theoretical level, any proper scoring rule can be used as a foundational basis for the theory of subjective probability. At an applied level a proper scoring can be used to compare and improve probability forecasts, and, in a parametric setting, as an alternative tool for inference. In this article we give an overview of some uses of proper scoring rules in statistical inference, including frequentist estimation theory and Bayesian model selection with improper priors
A statistical treatment of biases affecting the estimation of mutation rates
No full textWe consider the estimation of mutation rates, using family data obtained from disputed paternity cases. It is shown how to take appropriate account of a number of complicating features—in particular, hidden mutation, differential mutation, and
uncertain paternity—which can necessitate large corrections to simple estimates
Remarks on: "Paternity analysis in special fatherless cases without direct testing of alleged father" [Forensic Science International 146S (2004) S159-S161]
No full text
Object-oriented Bayesian networks for complex forensic DNA profiling problems
No full textWe describe a flexible computational toolkit, based on object-oriented Bayesian networks, that can be used to model and solve a wide variety of
complex problems of relationship testing using DNA profiles. In particular this can account for such complicating features as missing individuals,
mutation and null alleles. We illustrate the use of this toolkit with several examples, including disputed paternity with missing or additional
measurements, and criminal identification.We investigate the effects on likelihood ratios of introducing mutation and/or null alleles, and show that
this can be substantial even when the underlying perturbations are small
Rényi’s Scoring Rules = Regole di Punteggio di Renyi
No full textMostriamo come lo studio dell’ entropia di Renyi dal punto di vista della teoria delle regole di punteggio proprie, conduca alla definizione di una nuova funzione divergenza. Usiamo poi la regola di punteggio associata per costruire stimatori parametrici di cui vengono studiate le proprietà di B-robustezza nel caso di famiglie di locazioneWe consider the Renyi entropy from the point of view of the theory of proper scoring rules. We show that this leads to a new definition of the Renyi divergence. We also apply the associated scoring rule to construct parameter estimators, and consider their robustness properties in the case of a location family
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