In: Bioinformatics, 2016, vol. 32, no. 13, p. 1990-2000
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Thèse de doctorat : Università della Svizzera italiana, 2020 ; 2020INFO023.
As energy efficiency becomes a crucial concern in almost every kind of digital application, Approximate Computing gains popularity as a potential answer to this ever-growing energy quest. Approximate Computing is a design paradigm particularly suited for error- resilient applications, where small losses in accuracy do not represent a significant reduction in the quality of the result. In these...
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In: Journal of Business and Economic Statistics, 2020, vol. 38, no. 1, p. 183-200
This article investigates the finite sample properties of a range of inference methods for propensity score-based matching and weighting estimators frequently applied to evaluate the average treatment effect on the treated. We analyze both asymptotic approximations and bootstrap methods for computing variances and confidence intervals in our simulation designs, which are based on German...
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In: Computational Statistics, 2014, vol. 29, no. 5, p. 1345-1363
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In: Methodology and Computing in Applied Probability, 2014, vol. 16, no. 3, p. 561-582
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In: Foundations of Computational Mathematics, 2014, vol. 14, no. 3, p. 419-456
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In: Extremes, 2009, vol. 12, no. 2, p. 107-127
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In: Numerische Mathematik, 2010, vol. 115, no. 3, p. 475-509
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In: Methodology and Computing in Applied Probability, 2005, vol. 7, no. 2, p. 139-148
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In: Pediatric Nephrology, 2007, vol. 22, no. 9, p. 1243-1250
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