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- Andersson, Eva M., 1968, et al.
(author)
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Detection of Turning Points in Business Cycles
- 2004
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In: Journal of Business Cycle Measurement and Analysis. ; 1:1, s. 93-108
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Journal article (peer-reviewed)abstract
- Methods for continuously monitoring business cycles are compared. A turn in a leading index can be used to predict a turn in the business cycle. Three likelihood based methods for turning point detection are compared in detail by using the theory of statistical surveillance and by simulations. One of the methods is a parametric likelihood ratio method. Another includes a non-parametric estimation procedure. The third is based on a Hidden Markov Model. Evaluations are made of several features such as knowledge of shape and parameters of the curve, types and probabilities of transitions and smoothing. Results on the expected delay time [of](to) a correct alarm and the predictive value of an alarm are discussed. The three methods are also used to analyze an actual data set (of) [for] a period of (the) Swedish industrial production. The relative merits of evaluation of methods by one real data set or by simulations are discussed.
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- Andersson, Eva M., 1968
(author)
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Effect of dependency in systems for multivariate surveillance.
- 2007
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Reports (other academic/artistic)abstract
- In many situations we need a system for detecting changes early. Examples are early detection of disease outbreaks, of patients at risk and of financial instability. In influenza outbreaks, for example, we want to detect an increase in the number of cases. Important indicators might be the number of cases of influenza-like illness and pharmacy sales (e.g. aspirin). By continually monitoring these indicators, we can early detect a change in the process of interest. The methodology of statistical surveillance is used. Often, the conclusions about the process(es) of interest is improved if the surveillance is based on several indicators. Here three systems for multivariate surveillance are compared. One system, called LRpar, is based on parallel likelihood ratio methods, since the likelihood ratio has been shown to have several optimality properties. In LRpar, the marginal density of each indicator is monitored and an alarm is called as soon as one of the likelihood ratios exceeds its alarm limit. The LRpar is compared to an optimal alarm system, called LRjoint, which is derived from the full likelihood ratio for the joint density. The performances of LRpar and LRjoint are compared to a system where the Hotellings T2 is monitored. The evaluation is made using the delay of a motivated alarm, as a function of the times of the changes. The effect of dependency is investigated: both dependency between the monitored processes and correlation between the time points when the changes occur. When the first change occurs immediately, the three methods work rather similarly, for independent processes and zero correlation between the change times. But when all processes change later, the T2 has much longer delay than LRjoint and LRpar. This holds both when the processes are independent and when they have a positive covariance. When we assume a positive correlation between the change times, the LRjoint yields a shorter delay than LRpar when the changes actually do occur simultaneously, whereas the opposite is true when the changes do actually occur at different time point.
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| 5. |
- Andersson, Eva M., 1968
(author)
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Effect of Dependency in Systems for Multivariate Surveillance
- 2009
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In: Communications in Statistics - Simulation and Computation. - : Informa UK Limited. - 0361-0918 .- 1532-4141. ; 38:3, s. 454 - 472
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Journal article (peer-reviewed)abstract
- Systems for multivariate on-line surveillance (e.g., outbreak detection) are investigated. Optimal systems for statistical surveillance are based on likelihood ratios. Three systems are compared: based on each marginal density, based on the joint density, and based on the Hotelling's T2. The effect of dependency between the monitored processes is investigated, and the effect of correlation between the change times. When the first change occurs immediately, the three methods give similar delay of an alarm, in the situation with independency. For late changes, T2 has the longest delay, both for independent processes and for processes with a positive covariance.
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| 6. |
- Andersson, Eva M., 1968
(author)
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Hotelling´s T2 Method in Multivariate On-line Surveillance. On the Delay of an Alarm
- 2008
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Reports (other academic/artistic)abstract
- A system for detecting changes in an on-going process is needed in many situations. On-line monitoring (surveillance) is used in early detection of disease outbreaks, of patients at risk and of financial instability. By continually monitoring one or several indicators, we can, early, detect a change in the processes of interest. There are several suggested methods for multivariate surveillance, one of which is the Hotelling’s T2. Since one aim in surveillance is quick detection of a change, it is important to use evaluation measures that reflect the timeliness of an alarm. One suggested measure is the expected delay of an alarm, in relation to the time of change () in the process. Here we investigate a delay measure for the bivariate situation. Generally, the measure depends on both change times (i.e. 1 and 2). We show that, for a bivariate situation using the T2 method, the delay only depends on 1 and 2 through the distance 1-2.
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| 7. |
- Andersson, Eva M., 1968
(author)
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Hotelling's T2 Method in Multivariate On-Line Surveillance: On the Delay of an Alarm
- 2009
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In: Communications in Statistics - Theory and Methods. - : Informa UK Limited. - 0361-0926 .- 1532-415X. ; 38:16 & 17, s. 2625-2633
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Journal article (peer-reviewed)abstract
- A system for detecting changes in an on-going process is needed in many situations. On-line monitoring (surveillance) is used in early detection of disease outbreaks, of patients at risk, and of financial instability. By continually monitoring one or several indicators, we can, early, detect a change in the processes of interest. There are several suggested methods for multivariate surveillance, one of which is the Hotelling's T2. Since one aim in surveillance is quick detection of a change, it is important to use evaluation measures that reflect the timeliness of an alarm. One suggested measure is the expected delay of an alarm, in relation to the time of change (τ) in the process. Here we investigate a delay measure for the bivariate situation. Generally, the measure depends on both change times (i.e. τ1 and τ2). We show that, for a bivariate situation using the T2 method, the delay only depends on τ1 and τ2 through the distance τ1-τ2.
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| 9. |
- Andersson, Eva M., 1968, et al.
(author)
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Modeling influenza incidence for the purpose of on-line monitoring
- 2008
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In: Statistical Methods in Medical Research. - : SAGE Publications. - 0962-2802 .- 1477-0334. ; 17:4, s. 421-438
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Journal article (peer-reviewed)abstract
- We describe and discuss statistical models of Swedish influenza data, with special focus on aspects which are important in on-line monitoring. Earlier suggested statistical models are reviewed and the possibility of using them to describe the variation in influenza-like illness (ILI) and laboratory diagnoses (LDI) is discussed. Exponential functions were found to work better than earlier suggested models for describing the influenza incidence. However, the parameters of the estimated functions varied considerably between years. For monitoring purposes we need models which focus on stable indicators of the change at the outbreak and at the peak. For outbreak detection we focus on ILI data. Instead of a parametric estimate of the baseline (which could be very uncertain,), we suggest a model utilizing the monotonicity property of a rise in the incidence. For ILI data at the outbreak, Poisson distributions can be used as a first approximation. To confirm that the peak has occurred and the decline has started, we focus on LDI data. A Gaussian distribution is a reasonable approximation near the peak. In view of the variability of the shape of the peak, we suggest that a detection system use the monotonicity properties of a peak.
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| 10. |
- Andersson, Eva M., 1968, et al.
(author)
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Modeling influenza incidence for the purpose of on-line monitoring
- 2007
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Reports (other academic/artistic)abstract
- We describe and discuss statistical models of Swedish influenza data, with special focus on aspects which are important in on-line monitoring. Earlier suggested statistical models are reviewed and the possibility of using them to describe the variation in influenza-like illness (ILI) and laboratory diagnoses (LDI) is discussed. Exponential functions were found to work better than earlier suggested models for describing the influenza incidence. However, the parameters of the estimated functions varied considerably between years. For monitoring purposes we need models which focus on stable indicators of the change at the outbreak and at the peak. For outbreak detection we focus on ILI data. Instead of a parametric estimate of the baseline (which could be very uncertain,), we suggest a model utilizing the monotonicity property of a rise in the incidence. For ILI data at the outbreak, Poisson distributions can be used as a first approximation. To confirm that the peak has occurred and the decline has started, we focus on LDI data. A Gaussian distribution is a reasonable approximation near the peak. In view of the variability of the shape of the peak, we suggest that a detection system use the monotonicity properties of a peak.
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