Locally asymptotically rank-based procedures for testing autoregressive moving average dependence

AUTOR(ES)

Hallin, Marc

RESUMO

The problem of testing a given autoregressive moving average (ARMA) model (in which the density of the generating white noise is unspecified) against other ARMA models is considered. A distribution-free asymptotically most powerful test, based on a generalized linear serial rank statistic, is provided against contiguous ARMA alternatives with specified coefficients. In the case in which the ARMA model in the alternative has unspecified coefficients, the asymptotic sufficiency (in the sense of Hájek) of a finite-dimensional vector of rank statistics is established. This asymptotic sufficiency is used to derive an asymptotically maximin most powerful test, based on a generalized quadratic serial rank statistic. The asymptotically maximin optimal test statistic can be interpreted as a rank-based, weighted version of the classical Box-Pierce portmanteau statistic, to which it reduces, in some particular problems, under gaussian assumptions.

Documentos Relacionados

• Non-Gaussian autoregressive moving average processes.
• Predictive determinants of scorpion stings in a tropical zone of south Iran: use of mixed seasonal autoregressive moving average model
• EFICIÊNCIA DOS GRÁFICOS DE CONTROLE NA DETECÇÃO DE OUTLIERS EM PROCESSOS AUTORREGRESSIVOS E DE MÉDIAS MÓVEIS
• MONITORING TRACTOR PERFORMANCE USING SHEWHART AND EXPONENTIALLY WEIGHTED MOVING AVERAGE CHARTS
• In vitro susceptibility testing procedures for fosfomycin tromethamine.