| 1. |
- Andersson, Eva M., 1968, et al.
(författare)
-
Detection of Turning Points in Business Cycles
- 2004
-
Ingår i: Journal of Business Cycle Measurement and Analysis. ; 1:1, s. 93-108
-
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.
|
|
| 2. |
|
|
| 3. |
- Andersson, Eva M., 1968, et al.
(författare)
-
Modeling influenza incidence for the purpose of on-line monitoring
- 2008
-
Ingår i: Statistical Methods in Medical Research. - : SAGE Publications. - 0962-2802 .- 1477-0334. ; 17:4, s. 421-438
-
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.
|
|
| 4. |
- Andersson, Eva M., 1968, et al.
(författare)
-
Modeling influenza incidence for the purpose of on-line monitoring
- 2007
-
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.
|
|
| 5. |
|
|
| 6. |
- Andersson, Eva M., 1968, et al.
(författare)
-
Some statistical aspects of methods for detection of turning points in business cycles
- 2006
-
Ingår i: Journal of Applied Statistics. - : Informa UK Limited. - 0266-4763 .- 1360-0532. ; 33:3, s. 257 - 278
-
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.
|
|
| 7. |
- Andersson, Eva M., 1968, et al.
(författare)
-
Statistical surveillance of cyclical processes with application to turns in business cycles
- 2005
-
Ingår i: Journal of Forecasting. - : Wiley. - 1099-131X .- 0277-6693. ; 24:7, s. 465-490
-
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.
|
|
| 8. |
- Bock, David, 1976
(författare)
-
Aspects on the Control of False Alarms in Statistical Surveillance and the Impact on the Return of Financial Decision systems
- 2008
-
Ingår i: Journal of Applied Statistics. ; 35:2, s. 213-227
-
Tidskriftsartikel (refereegranskat)abstract
- In systems for on-line detection of regime shifts, a process is continually observed. Based on the data available an alarm is given when there is enough evidence of a change. There is a risk of a false alarm and here two different ways of controlling the false alarms are compared: a fixed average run length until the first false alarm and a fixed probability of any false alarm (fixed size). The two approaches are evaluated in terms of the timeliness of alarms. A system with a fixed size is found to have a drawback: the ability to detect a change deteriorates with the time of the change. Consequently, the probability of successful detection will tend to zero and the expected delay of a motivated alarm tends to infinity. This drawback is present even when the size is set to be very large (close to 1). Utility measures expressing the costs for a false or a too late alarm are used in the comparison. How the choice of the best approach can be guided by the parameters of the process and the different costs of alarms is demonstrated. The technique is illustrated by financial transactions of the Hang Seng Index.
|
|
| 9. |
|
|
| 10. |
- Bock, David, 1976
(författare)
-
Consequences of using the probability of a false alarm as the false alarm measure
- 2007
-
Ingår i: Proceedings of the International Workshop in Sequential Methologies, Auburn, Alabama.
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- In systems for on-line detection of regime shifts, a process is continually observed. Based on the data available, we make repeated decisions such that when there is enough evidence of a change, an alarm is given. There is a risk of a false alarm and here two different ways of controlling the false alarms are compared: systems with a fixed average run length until the first false alarm, and systems with a fixed probability (<1) of any false alarm (fixed size). The effects of the two approaches are evaluated in terms of the timeliness of alarms. A system with a fixed size is found to have a drawback: the ability to detect a change deteriorates rapidly with the time of the change. Consequently, the probability of successful detection will tend to zero and the expected delay of a motivated alarm tends to infinity. This drawback is present even when the size is set to be very large (close to 1). Utility measures expressing the different costs for a false or a too late alarm are used in the comparison. How the choice of the best approach can be guided by the parameters of the process and the different costs of alarms is demonstrated. The technique is illustrated by a case study.
|
|
