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Kidney Transplantation, Kermanshah University of Medical Sciences, Kermanshah, ... artificial neural network models in prediction of kidney transplant survival.
nonparametric instance-based k-nearest neighbour (k-NN) approach to estimate single-tree biomass with predictions from linear mixed-effect regression models ...
Comparison of regression models based on nonparametric estimation techniques: Prediction of GDP in Turkey. Dursun Aydın. Abstract- In this study, it has been ...
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ABSTRACT. Test-day milk yields of first-lactation Black and. White cows were used to select the model for routine genetic evaluation of dairy cattle in Poland.
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May 23, 2011 - comparison of statistical packages for binary and ordinal outcomes ...... Journal of Computational and Graphical Statistics. 1998, 7:434-455. 31.
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Nov 9, 2000 - email: [email protected]. FAX: +49 2 34 70 94 559. Natalie Neumeyer. Ruhr-Universit at Bochum. Fakult at f ur Mathematik.
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During flu season, residuals were modestly lower for regression overall and at mean observed counts >20 per facility per day. Table: Mean Absolute Residuals ...
Comparison of Regression Models with Modified Time Series Methods for BioSurveillance Jian Xing1, Howard Burkom2, Jerome Tokars1 1
Centers for Disease Control and Prevention, 2The Johns Hopkins Applied Physics Lab,
OBJECTIVE To compare regression models with the modified C2 algorithm for analysis of time series data and real time outbreak detection. BACKGROUND Previous studies using BioSense System data showed that adjusting for total visits in regression models improves accuracy of predicted value calculations [1]; and modification of the EARS C2 algorithm, including adjusting for total facility visits and a longer baseline for stable thresholding, improves the accuracy of expected value calculation and overall performance [2]. A previous study of data aggregated from 1 metropolitan area showed that Poisson regression modeling performed better than C2; however, this comparison did not include the adjustments above [3]. Therefore, we compare regression model estimation methods with the modified EARS C2 method. METHODS The study data source was BioSense national hospital emergency department chief complaint data that included records from >400 facilities. These records were classified into the standard 11 BioSense syndrome groups. We present here preliminary analyses using respiratory syndrome counts during May 1, 2007 to April 30, 2008. We calculated expected values using the modified EARS C2 method with a sliding 28-day baseline and adjustment for total visits. We also used a linear regression model controlling for total visits, day of week, and 14-day time period (a separate indicator variable for each 14-day period); the model was run separately for each facility, with the expected value for each day in the study period calculated from regression coefficients using the previous 90 days of data, with a 2-day buffer. We compared mean absolute residuals during the full year; and stratifying by mean observed count during 3months periods in the flu season (Jan-Mar 2008) and non-flu season (May-July 2007). RESULTS The mean observed count per facility per day was 19.95 (range 0.14 to 98.27). Over the full year, the mean absolute residual was lower for modified C2 than regression (3.52 vs. 3.76). During the non-flu season, residuals were lower for modified C2 overall
and across all observed count categories (Table). During flu season, residuals were modestly lower for regression overall and at mean observed counts >20 per facility per day.
Table: Mean Absolute Residuals by Observed Count, Method, and Time Period. May-July, 2007
Jan-Mar, 2008
% of
% of Mean Observed Counts
total case
Regression
Modified C2
total case
Regression
Modified C2
3.32
3.02*
4.24**
4.33
0 to 5
17.09
1.16
1.00*
9.38
1.21
1.07*
Over all
6 to 10
20.49
2.47
2.22*
14.04
2.54
2.37*
11 to 20
33.25
3.44
3.07*
26.13
3.40
3.31*
21 to 30
15.43
4.41
3.99*
20.60
4.40**
4.49
31 to 40
9.54
5.42
5.18*
12.03
5.62**
5.89
41 to 50
3.09
5.98
5.64*
7.64
6.26**
6.61
51 to 60
0.28
5.75
6.07*
4.17
6.78**
7.18
61 to 70
0.28
8.31
7.39*
1.62
7.59**
7.76
> 70
0.56
9.55
9.40*
4.40
9.44**
10.72
* Modified C2 better ** Regression better
CONCLUSIONS These preliminary results demonstrate that modified C2 produces lower residuals during non-flu season and for facilities with mean observed counts
