{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,5,29]],"date-time":"2024-05-29T14:10:01Z","timestamp":1716991801942},"reference-count":32,"publisher":"World Scientific Pub Co Pte Ltd","issue":"02n03","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Adv. Adapt. Data Anal."],"published-print":{"date-parts":[[2014,4]]},"abstract":"<jats:p>The estimation and significance testing of the first-order autoregressive (AR1) coefficient in short time series with trends are examined. The purpose is to identify the difficulties to which analysis procedures need to adjust for better results. The delta recursive AR1 estimator r<jats:sub>\u03b4<\/jats:sub>and the Sen\u2013Theil trend estimator are viable for short sequence application. Significance testing for r<jats:sub>\u03b4<\/jats:sub>has low power. But the existence of trend has negligible influence in estimation and testing. The common practice of trend removal before AR1 estimation gives poorer results. Application to air quality data showed this could greatly change conclusions. Implication to analysis is discussed.<\/jats:p>","DOI":"10.1142\/s1793536914500071","type":"journal-article","created":{"date-parts":[[2014,7,10]],"date-time":"2014-07-10T05:52:37Z","timestamp":1404971557000},"page":"1450007","source":"Crossref","is-referenced-by-count":0,"title":["AUTOCORRELATION IN SHORT TIME SERIES WITH TRENDS: A SIMULATION STUDY OF ESTIMATION AND SIGNIFICANCE TESTING WITH APPLICATION TO AIR QUALITY DATA"],"prefix":"10.1142","volume":"06","author":[{"given":"RAYMOND K. 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