22 research outputs found

    Outlier Detection in Adaptive Functional-Coefficient Autoregressive Models Based on Extreme Value Theory

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    This paper proposes several test statistics to detect additive or innovative outliers in adaptive functional-coefficient autoregressive (AFAR) models based on extreme value theory and likelihood ratio tests. All the test statistics follow a tractable asymptotic Gumbel distribution. Also, we propose an asymptotic critical value on a fixed significance level and obtain an asymptotic p-value for testing, which is used to detect outliers in time series. Simulation studies indicate that the extreme value method for detecting outliers in AFAR models is effective both for AO and IO, for a lone outlier and multiple outliers, and for separate outliers and outlier patches. Furthermore, it is shown that our procedure can reduce possible effects of masking and swamping

    Chemoenzymatic modular assembly of O-GalNAc glycans for functional glycomics

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    O-GalNAc glycans are essential in many biological and pathological processes, but difficult to access due to their structural complexity and synthetic challenges. Here, the authors report an efficient chemoenzymatic modular assembly strategy to construct structurally diverse O-GalNAc glycans, use the synthesised glycans to generate a synthetic mucin O-glycan microarray and profile binding specificities of glycan-binding proteins
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