7 research outputs found

    Sequential Change-point Detection for High-dimensional and non-Euclidean Data

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    In many modern applications, high-dimensional/non-Euclidean data sequences are collected to study complicated phenomena over time and it is of scientific importance to detect anomaly events as the data are being collected. We studied a nonparametric framework that utilizes nearest neighbor information among the observations and can be applied to such sequences. We considered new test statistics under this framework that can make more positive detections and can detect anomaly events sooner than the existing test under many common scenarios with the false discovery rate controlled at the same level. Analytic formulas for approximate the average run lengths of the new approaches are derived to make them fast applicable to large datasets

    A Robust Framework for Graph-based Two-Sample Tests Using Weights

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    Graph-based tests are a class of non-parametric two-sample tests useful for analyzing high-dimensional data. The framework offers both flexibility and power in a wide-range of testing scenarios. The test statistics are constructed from similarity graphs (such as KK-nearest neighbor graphs) and consequently, their performance is sensitive to the structure of the graph. When the graph has problematic structures, as is common for high-dimensional data, this can result in poor or unstable performance among existing graph-based tests. We address this challenge and develop graph-based test statistics that are robust to problematic structures of the graph. The limiting null distribution of the robust test statistics is derived. We illustrate the new tests via simulation studies and a real-world application on Chicago taxi trip-data

    Session 6 - Nonparametric Methods for Data Science: \u3cem\u3e Asymptotically Distribution-free Change-Point Detection for Non-Euclidean and Multivariate Data\u3c/em\u3e

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    Chair: Dr. Danica Ommen - Iowa State Universit

    Asymptotic distribution-free change-point detection for multivariate and non-Euclidean data

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    Father Involvement in Feeding Interactions with Their Young Children

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    OBJECTIVE: To examine the associations of father-child feeding and physical interactions with dietary practices and weight status in children. METHODS: A nationally representative sample of children, mothers, and fathers who participated in the Early Childhood Longitudinal Study Birth cohort study (N = 2441) was used to explore the relationship of father-child feeding and physical activity interactions with child dietary practices and weight status. Logistic multivariable regression analyses were adjusted for child, father, mother, and socio-demographic characteristics. RESULTS: Approximately 40% of fathers reported having a great deal of influence on their preschool child’s nutrition and about 50% reported daily involvement in preparing food for their child and assisting their child with eating. Children had over 2 times the odds of consuming fast food at least once a week if fathers reported eating out with their child a few times a week compared to fathers who reported rarely or never eating out with their child (OR, 2.89; 95% CI, 1.94–4.29), adjusting for all covariates. Whether fathers reported eating out with their children was also significantly associated with children’s sweetened beverage intake. CONCLUSIONS: Potentially modifiable behaviors that support healthy dietary practices in children may be supported by targeting fathers
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