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Browsing Articles by Author "Frisén, Marianne"

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    Optimal Surveillance Based on Exponentially Weighted Moving Averages
    (Taylor & Francis, 2006) Frisén, Marianne; Sonesson, Christian
    Statistical surveillance is used to detect an important change in a process as soon as possible after it has occurred. The EWMA method is used in industry, economics and medicine. Three optimality criteria of surveillance are studied. The ARL criterion violates commonly accepted inference principles and the drawbacks are demonstrated. The ED criterion is based on the minimal expected delay from change to detection. The full likelihood ratio method is optimal according to this criterion. Approximations of this method turn out to be modifications of the EWMA method. The approximations lead to a formula for the optimal value of the smoothing parameter of the EWMA statistic. The usefulness of this formula is shown. It is demonstrated that, for EWMA, the minimax criterion agrees well with that of the ED criterion but not with that of the ARL criterion.
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    Properties and Use of the Shewhart Method and Its Followers
    (Taylor & Francis, 2007) Frisén, Marianne
    After the Shewhart method was suggested for industrial applications, other applications, such as surveillance for bioterrorism and financial transactions, came into focus. Other methods for surveillance have also followed. The relation between the Shewhart method and the followers is examined. A uniform presentation of methods, by expressions of likelihood ratios, facilitates the comparisons between methods. The situations for which the Shewhart method has optimality properties are thus determined. The uses of the Shewhart method and its followers for complicated situations are reviewed.
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    Some statistical aspects of methods for detection of turning points in business cycles
    (Taylor & Francis, 2006) Andersson, Eva; Bock, David; Frisén, Marianne
    Methods for on-line turning point detection in business cycles are discussed. The statistical properties of three likelihood based methods are compared. One is based on a Hidden Markov Model, another includes a non-parametric estimation procedure and the third combines features of the other two. The methods are illustrated by monitoring a period of the Swedish industrial production. Evaluation measures that reflect timeliness are used. The effects of smoothing, seasonal variation, autoregression and multivariate issues on methods for timely detection are discussed
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