Linear Serial Rank Tests for Randomness Against Arma Alternatives Article Swipe

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Mathematics Contiguity Rank (graph theory) Asymptotic distribution Autocorrelation Statistics Autoregressive–moving-average model Null hypothesis Statistical hypothesis testing Local asymptotic normality Applied mathematics Asymptotic analysis White noise Null (SQL) Alternative hypothesis Asymptotically optimal algorithm Autoregressive model Mathematical optimization Computer science Combinatorics Estimator Operating system Database

Marc Hallin , Jean‐François Ingenbleek , Madan L. Puri ·

YOU? · · 2018 · Open Access · · OA: W2085980309

In this paper we introduce a class of linear serial rank statistics for the problem of testing white noise against alternatives of ARMA serial dependence. The asymptotic normality of the proposed statistics is established, both under the null as well as alternative hypotheses, using LeCam's notion of contiguity. The efficiency properties of the proposed statistics are investigated, and an explicit formulation of the asymptotically most efficient score-generating functions is provided. Finally, we study the asymptotic relative efficiency of the proposed procedures with respect to their normal theory counterparts based on sample autocorrelations.