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EndNote 21.2.17387 download the new1/15/2024 ![]() You can help correct errors and omissions. Suggested CitationĪll material on this site has been provided by the respective publishers and authors. In addition, we use this in the analysis of three real datasets related to gait data, flu activity, and casual bike rentals. Through simulations, we illustrate our method, and its performance is compared to existing competitors. We obtain its asymptotic distribution under the null and propose a bootstrap algorithm to compute the p-values in practice. Then, we allow one to quantify the effect of a group of covariates or to apply covariates selection one by one. In particular, global as well as partial dependence tests are introduced. As a result, this statistic measures the departure from the conditional mean independence in the concurrent model framework, considering the information of all observed time instants. ![]() We develop a suitable test statistic using the martingale difference divergence coefficient. ![]() No tuning parameters are involved either. As a novelty, our proposal does not require a preliminary model or error structure estimation. This paper presents new specification tests for a general synchronous additive concurrent model formulation.
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