Approximations to the Expected Sample Size of Certain Sequential Tests

Pollak, Moshe; Siegmund, David

doi:10.1214/aos/1176343284

Cited by 106 publications

(66 citation statements)

References 9 publications

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“…The MLR method suggested for example by Pollak and Siegmund (1975) involves priors for the unknown parameters. Priors are also used by Radaelli (1996) for the Sets method and by Lai (1998) for the CUSUM method.…”

Section: Change Between Unknown Parametersmentioning

confidence: 99%

“…However, the price for this additional feature is high as the detection ability of methods with this property declines rapidly with the value of time τ of the change. The expected delay of the detection of a change will be very large, as pointed out by Pollak and Siegmund (1975) and Frisén (2003). A demonstration of the drawbacks is also made by Bock (2004) by a simulation study.…”

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confidence: 96%

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Properties and Use of the Shewhart Method and Its Followers

Frisén¹

2007

Sequential Analysis

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Section: Change Between Unknown Parametersmentioning

confidence: 99%

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confidence: 96%

Properties and Use of the Shewhart Method and Its Followers

Frisén¹

2007

Sequential Analysis

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“…This is convenient since ordinary statements of hypothesis testing can be made. It was however pointed out by Pollak and Siegmund (1975) and Frisén (1994) that the ability to detect a change deteriorates rapidly with the time of the change. Consequences of this were illustrated in Bock (2006).…”

Section: Measures Of Evaluation and Optimality Criteriamentioning

confidence: 99%

Evaluations of Likelihood‐Based Surveillance of Volatility

Bock

2007

Financial Surveillance

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The volatility of asset returns are important in finance. Different likelihood based methods of statistical surveillance for detecting a change in the variance are evaluated. The differences are how the partial likelihood ratios are weighted. The full likelihood ratio, Shiryaev-Roberts, Shewhart and the CUSUM methods are derived in case of an independent and identically distributed Gaussian process. The behavior of the methods is studied both when there is no change and when the change occurs at different time points. The false alarms are controlled by the median run length. Differences and limiting equalities of the methods are shown. The performances when the process parameters for which the methods are optimized for differ from the true values of the parameters are evaluated. The methods are illustrated on a period of Standard and Poor's 500 stock market index.

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“…In that case, ordinary statements for hypotheses testing can be made. However, the price for this additional feature is high as the expected delay of the detection of a change will be very large as pointed out by Pollak and Siegmund (1975).…”

Section: Error Probabilitiesmentioning

confidence: 99%