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- Andersson, Eva M., 1968, et al.
(författare)
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Detection of Turning Points in Business Cycles
- 2004
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Ingår i: Journal of Business Cycle Measurement and Analysis. ; 1:1, s. 93-108
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Tidskriftsartikel (refereegranskat)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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| 2. |
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| 3. |
- Andersson, Eva M., 1968, et al.
(författare)
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Modeling influenza incidence for the purpose of on-line monitoring
- 2008
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Ingår i: Statistical Methods in Medical Research. - : SAGE Publications. - 0962-2802 .- 1477-0334. ; 17:4, s. 421-438
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Tidskriftsartikel (refereegranskat)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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| 4. |
- Andersson, Eva M., 1968, et al.
(författare)
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Modeling influenza incidence for the purpose of on-line monitoring
- 2007
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Rapport (övrigt vetenskapligt/konstnärligt)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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| 5. |
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| 6. |
- Andersson, Eva M., 1968, et al.
(författare)
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Predictions by early indicators of the time and height of the peaks of yearly influenza outbreaks in Sweden
- 2008
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Ingår i: Scandinavian Journal of Public Health. - : SAGE Publications. - 1403-4948 .- 1651-1905. ; 36:5, s. 475-482
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Tidskriftsartikel (refereegranskat)abstract
- Aims: Methods for prediction of the peak of the influenza from early observations are suggested. These predictions can be used for planning purposes. Methods: In this study, new robust methods are described and applied to weekly Swedish data on influenza-like illness (ILI) and weekly laboratory diagnoses of influenza (LDI). Both simple and advanced rules for how to predict the time and height of the peak of LDI are suggested. The predictions are made using covariates calculated from data in early LDI reports. The simple rules are based on the observed LDI values, while the advanced ones are based on smoothing by unimodal regression. The suggested predictors were evaluated by cross-validation and by application to the observed seasons. Results: The relationship between ILI and LDI was investigated, and it was found that the ILI variable is not a good proxy for the LDI variable. The advanced prediction rule regarding the time of the peak of LDI had a median error of 0.9 weeks, and the advanced prediction rule for the height of the peak had a median deviation of 28%. Conclusions: The statistical methods for predictions have practical usefulness.
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| 7. |
- Andersson, Eva M., 1968, et al.
(författare)
-
Predictions by early indicators of the time and height of yearly influenza outbreaks in Sweden
- 2007
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Aims: Methods for prediction of the peak of the influenza from early observations are suggested. These predictions can be used for planning purposes. Methods: In this study, new robust methods are described and applied on weekly Swedish data on influenza-like illness (ILI) and weekly laboratory diagnoses of influenza (LDI). Both simple and advanced rules for how to predict the time and height of the peak of LDI are suggested. The predictions are made using covariates calculated from data in early LDI reports. The simple rules are based on the observed LDI values while the advanced ones are based on smoothing by unimodal regression. The suggested predictors were evaluated by cross-validation and by application to the observed seasons. Results: The relation between ILI and LDI was investigated and it was found that the ILI variable is not a good proxy for the LDI variable. The advanced prediction rule regarding the time of the peak of LDI had a median error of 0.9 weeks, and the advanced prediction rule for the height of the peak had a median deviation of 28%. Conclusions: The statistical methods for predictions have practical usefulness.
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| 8. |
- Andersson, Eva M., 1968, et al.
(författare)
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Some statistical aspects of methods for detection of turning points in business cycles
- 2006
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Ingår i: Journal of Applied Statistics. - : Informa UK Limited. - 0266-4763 .- 1360-0532. ; 33:3, s. 257 - 278
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Tidskriftsartikel (refereegranskat)abstract
- Methods for online 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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| 9. |
- Andersson, Eva M., 1968, et al.
(författare)
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Statistical surveillance of cyclical processes with application to turns in business cycles
- 2005
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Ingår i: Journal of Forecasting. - : Wiley. - 1099-131X .- 0277-6693. ; 24:7, s. 465-490
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Tidskriftsartikel (refereegranskat)abstract
- On-line monitoring of cyclical processes is studied. An important application is early prediction of the next turn in business cycles by an alarm for a turn in a leading index. Three likelihood-based methods for detection of a turn are compared in detail. One of the methods is based on a hidden Markov model. The two others are based on the theory of statistical surveillance. One of these is free from parametric assumptions of the curve. Evaluations are made of the effect of different specifications of the curve and the transitions. The methods are made comparable by alarm limits, which give the same median time to the first false alarm, but also other approaches for comparability are discussed. Results are given on the expected delay time to a correct alarm, the probability of detection of a turning point within a specified time, and the predictive value of an alarm.
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| 10. |
- Andersson, Eva M., 1968, et al.
(författare)
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Statistiska varningssystem för hälsorisker
- 2008
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Varningssystem behövs t.ex. vid intensivövervakning, smittskydd, miljörisker och kvalitetskontroll av vården. Statistiska varningssystem signalerar när det skett en väsentlig ändring och man vet vilka egenskaper systemet har. För varningssystem är det viktigt att larmet kommer snabbt efter förändringen utan att det blir många falsklarm. I ett varningssystem kan inte hypotesprövning på vanligt sätt användas. Det behövs istället speciell metodik. Enbart subjektiv övervakning av data medför stor bedömarvariation varför en kombination med ett statistiskt system kan vara av värde. Metodiken exemplifieras för influensaövervakning.
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