1,721,080 research outputs found
Bootstrap Confidence Intervals for Sequences of Missing Values in Multivariate Time Series
No full textThis paper is aimed at deriving some specific-oriented bootstrap confidence intervals for missing sequences of observations in multivariate time series. The procedure is based on a spatial-dynamic model and imputes the missing values using a linear combination of the neighbor contemporary observations and their lagged values. The resampling procedure implements a residual bootstrap approach which is then used to approximate the sampling distribution of the estimators of the missing values. The normal based and the percentile bootstrap confidence intervals have been computed. A Monte Carlo simulation study shows the good empirical coverage performance of the proposal, even in the case of long sequences of missing values
A two-layer, shallow-water model for 3D gravity currents
No full textA two-layer, shallow-water model for three-dimensional (3D) gravity currents is proposed. The formulation results from the shallow-water-equations for two layers of immiscible liquids, subjected by the rigid-lid condition, so that the upper surface of the lighter layer remains perfectly flat during the motion. The arising pressure must be determined by solving the equations of motion, which is no problem for two-dimensional and axisymmetric gravity currents because the pressure is easily eliminated. In 3D gravity currents, the pressure is determined by solving a Poisson equation, together with momentum and mass balance equations. By means of a suitable scaling and a perturbation expansion, the equations are uncoupled from each other so that the problem is considerably simplified. Numerical results are compared with 3D lock-exchange release experiments. A comparison between numerical and experimental results of the gravity current indicates a fairly good agreement, whereas the results concerning the upper layer field variables shows that the numerical results are consistent with the experiments. Copyright © 2012 International Association for Hydro-Environment Engineering and Research
Segmenting toroidal time series by nonhomogeneous hidden semi-Markov models
Full text linkMotivated by classification issues in marine studies, we propose a hidden semi-Markov model to segment toroidal time series according to a finite number of latent regimes. The time spent in a given regime and the chances of a regime switching event are separately modeled by a battery of regression models that depend on time-varying covariates
Latent Bayesian clustering for topic modelling
No full textThe main objective in topic modelling is uncovering the underlying
themes present in a corpus of text data. This process is generally constituted by two
phases: (i) identifying the main words associated with each topic; (ii) grouping documents that contain similar sets of words together. In this work, we exploit recent
advances in Bayesian factor models to represent the high-dimensional space of the
observed words through a set of low-dimensional latent variables, and to jointly cluster the documents according to their distribution over such latent constructs. Groups and underlying constructs are interpreted as document topics and language concepts,
respectively, with the number of such dimensions that is not required in advance. We
apply the proposed approach to a data set of newspaper headlines
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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