5,691 research outputs found

    quantreg. nonpar: An R Package for performing nonparametric series quantile regression

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    The R package quantreg.nonpar implements nonparametric quantile regression methods to estimate and make inference on partially linear quantile models. quantreg.nonpar obtains point estimates of the conditional quantile function and its derivatives based on series approximations to the nonparametric part of the model. It also provides pointwise and uniform confidence intervals over a region of covariate values and/or quantile indices for the same functions using analytical and resampling methods. This paper serves as an introduction to the package and displays basic functionality of the functions contained within.https://arxiv.org/abs/1610.08329Published and Accepted manuscript versions

    quantreg. nonpar: An R Package for performing nonparametric series quantile regression

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    The R package quantreg.nonpar implements nonparametric quantile regression methods to estimate and make inference on partially linear quantile models. quantreg.nonpar obtains point estimates of the conditional quantile function and its derivatives based on series approximations to the nonparametric part of the model. It also provides pointwise and uniform confidence intervals over a region of covariate values and/or quantile indices for the same functions using analytical and resampling methods. This paper serves as an introduction to the package and displays basic functionality of the functions contained within.https://arxiv.org/abs/1610.08329Published and Accepted manuscript versions

    During elections, candidates who discuss the same issues can end up confusing voters

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    With the 2014 mid-term elections looming ever closer, it is only a matter of time before the calls for candidates to have greater dialogue on issues begins. But is this dialogue, or ‘issue convergence’ helpful for voters? New research by Keena Lipsitz into what happens when candidates address the same issues at the same time finds that individuals hear from both candidates, they are often easily confused, especially if their level of political interest is not high

    Emergence of Complex Dynamics in a Simple Model of Signaling Networks

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    A variety of physical, social and biological systems generate complex fluctuations with correlations across multiple time scales. In physiologic systems, these long-range correlations are altered with disease and aging. Such correlated fluctuations in living systems have been attributed to the interaction of multiple control systems; however, the mechanisms underlying this behavior remain unknown. Here, we show that a number of distinct classes of dynamical behaviors, including correlated fluctuations characterized by 1/f1/f-scaling of their power spectra, can emerge in networks of simple signaling units. We find that under general conditions, complex dynamics can be generated by systems fulfilling two requirements: i) a ``small-world'' topology and ii) the presence of noise. Our findings support two notable conclusions: first, complex physiologic-like signals can be modeled with a minimal set of components; and second, systems fulfilling conditions (i) and (ii) are robust to some degree of degradation, i.e., they will still be able to generate 1/f1/f-dynamics
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