Ronald D. Fricker
Introduction to Statistical Methods for Biosurveillance
Ronald D. Fricker
Introduction to Statistical Methods for Biosurveillance
- Gebundenes Buch
- Merkliste
- Auf die Merkliste
- Bewerten Bewerten
- Teilen
- Produkt teilen
- Produkterinnerung
- Produkterinnerung
Presents basic and advanced methods with a focus on demonstrated added value for a broad class of public health surveillance problems.
Andere Kunden interessierten sich auch für
- Simon T. BateThe Design and Statistical Analysis of Animal Experiments129,99 €
- James D. MalleyStatistical Learning for Biomedical Data49,99 €
- Mohammed Amin MohammedStatistical Process Control75,99 €
- William D. DupontStatistical Modeling for Biomedical Researchers99,99 €
- Alex BottleStatistical Methods for Healthcare Performance Monitoring123,99 €
- Mitchell KatzStudy Design and Statistical Analysis81,99 €
- Modern Statistical Methods for Health Research103,99 €
-
-
-
Presents basic and advanced methods with a focus on demonstrated added value for a broad class of public health surveillance problems.
Produktdetails
- Produktdetails
- Verlag: Cambridge University Press
- Seitenzahl: 416
- Erscheinungstermin: 25. April 2013
- Englisch
- Abmessung: 240mm x 161mm x 27mm
- Gewicht: 789g
- ISBN-13: 9780521191340
- ISBN-10: 0521191343
- Artikelnr.: 37439984
- Verlag: Cambridge University Press
- Seitenzahl: 416
- Erscheinungstermin: 25. April 2013
- Englisch
- Abmessung: 240mm x 161mm x 27mm
- Gewicht: 789g
- ISBN-13: 9780521191340
- ISBN-10: 0521191343
- Artikelnr.: 37439984
Ronald D. Fricker, Jr is an Associate Professor of Operations Research and the Associate Chair for Research at the Naval Postgraduate School (NPS). Prior to joining NPS, Dr Fricker was a Senior Statistician at the RAND Corporation and the Associate Director of the National Security Research Division. Published widely in leading professional journals, he is a Fellow of the American Statistical Association, an Elected Member of the International Statistical Institute, and a former chair of the ASA Section on Statistics in Defense and National Security. He is a contributing editor to Interfaces and is on the editorial boards of Statistics, Politics, and Policy and the International Journal of Quality Engineering and Technology. Fricker's current research is focused on studying the performance of various statistical methods for use in biosurveillance, particularly syndromic surveillance, and statistical process control methodologies more generally.
Part I. Introduction to Biosurveillance: 1. Overview
2. Biosurveillance data
Part II. Situational Awareness: 3. Situational awareness for biosurveillance
4. Descriptive statistics for displaying the situation
5. Statistical models for evaluating the situation
Part III. Early Event Detection: 6. Design and performance evaluation
7. Univariate temporal methods
8. Multivariate temporal methods
9. Spatio-temporal methods
Part IV. Putting It All Together: 10. Simulating biosurveillance data
11. Applying the temporal methods to real data
12. Comparing methods to better understand and improve
13. Frontiers, open questions, and future research.
2. Biosurveillance data
Part II. Situational Awareness: 3. Situational awareness for biosurveillance
4. Descriptive statistics for displaying the situation
5. Statistical models for evaluating the situation
Part III. Early Event Detection: 6. Design and performance evaluation
7. Univariate temporal methods
8. Multivariate temporal methods
9. Spatio-temporal methods
Part IV. Putting It All Together: 10. Simulating biosurveillance data
11. Applying the temporal methods to real data
12. Comparing methods to better understand and improve
13. Frontiers, open questions, and future research.
Part I. Introduction to Biosurveillance: 1. Overview
2. Biosurveillance data
Part II. Situational Awareness: 3. Situational awareness for biosurveillance
4. Descriptive statistics for displaying the situation
5. Statistical models for evaluating the situation
Part III. Early Event Detection: 6. Design and performance evaluation
7. Univariate temporal methods
8. Multivariate temporal methods
9. Spatio-temporal methods
Part IV. Putting It All Together: 10. Simulating biosurveillance data
11. Applying the temporal methods to real data
12. Comparing methods to better understand and improve
13. Frontiers, open questions, and future research.
2. Biosurveillance data
Part II. Situational Awareness: 3. Situational awareness for biosurveillance
4. Descriptive statistics for displaying the situation
5. Statistical models for evaluating the situation
Part III. Early Event Detection: 6. Design and performance evaluation
7. Univariate temporal methods
8. Multivariate temporal methods
9. Spatio-temporal methods
Part IV. Putting It All Together: 10. Simulating biosurveillance data
11. Applying the temporal methods to real data
12. Comparing methods to better understand and improve
13. Frontiers, open questions, and future research.