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Sökning: WFRF:(de Luna Xavier) > Annan publikation

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  • Grüning, Björn, et al. (författare)
  • Bioconda: A sustainable and comprehensive software distribution for the life sciences
  • 2017
  • Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
    • We present Bioconda (https://bioconda.github.io), a distribution of bioinformatics software for the lightweight, multi-platform and language-agnostic package manager Conda. Currently, Bioconda offers a collection of over 3000 software packages, which is continuously maintained, updated, and extended by a growing global community of more than 200 contributors. Bioconda improves analysis reproducibility by allowing users to define isolated environments with defined software versions, all of which are easily installed and managed without administrative privileges.
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  • de Luna, Xavier, et al. (författare)
  • Proxy variables and nonparametric identification of causal effects
  • Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
    • Proxy variables are often used in linear regression models with the aim of removing potential confounding bias. In this paper we formalise proxy variables within the potential outcome framework, giving conditions under which it can be shown that causal effects are nonparametrically identified. We characterise two types of proxy variables and give concrete examples where the proxy conditions introduced may hold by design.
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  • Elezovic, Suad, 1965-, et al. (författare)
  • A note on the estimation of functional autoregressive models
  • Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
    • Consider situations where a real valued function is observed over time and has a dynamic dependence structure. Linear autoregressive models, which have been proven useful to model dynamics of "pointwise" time series, can be generalized to such a functional time series situation. We call such models functional autoregressive models. Their parameters are functions of a real valued argument (as the data) and we consider a two-step estimation procedure inspired by Fan and Zhang's (2000) proposal for functional linear models. The latter proposal is based on a first step where the ordinary least squares is used to estimate pointwise linear models for given values of the argument of the functions observed. The second step smoothes the first-step estimates, regressing the latter on the mentioned arguments. The second step does not only yield smooth estimates of the functional parameters but also provides less variable pointwise estimates at the price of a bias. We do not only contribute  by presenting an autoregressive model for functional data but also by proposing a two-stage estimator where the first step takes into account the contemporaneous correlation structure through a multivariate generalized least squares estimator. Some of the properties of the resulting two-step procedure are given. Financial functional data is used as an illustration.
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  • Genbäck, Minna, 1985-, et al. (författare)
  • Bounds and sensitivity analysis when estimating average treatment effects with imputation and double robust estimators
  • Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
    • When estimating average causal effects of treatments with observational data, scientists often rely on the assumption of unconfoundedness. We propose a sensitivity analysis for imputation estimators and doubly robust estimators, based on bounds (defining an identification interval) for the causal effect of interest, which allow for unobserved confounders. The bounds are derived from the bias of the estimators, expressed as a function of a sensitivity parameter. We describe how such bounds can take into account sampling variation, thereby yielding an uncertainty interval. We are also able to contrast the size of potential bias due to violation of the unconfoundedness assumption, to the misspecification of the models used to explain outcome with the observed covariates. While the latter bias can in principle be made arbitrarily small with increasing sample size (by increasing the flexibility of the models used), the bias due to unobserved confounding does not disappear with increasing sample size. Through numerical experiments we illustrate the relative size of the biases due to unobserved confounders and model misspecification, as well as the empirical coverage of the uncertainty interval on which the proposed sensitivity analysis is based.
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  • Resultat 1-10 av 16

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