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| A Framework for Statistical Simulation of Physiological Responses (SSPR).
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Source |
International Journal of Scientific and Statistical Computing (IJSSC) |
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Table of Contents |
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Volume: 3 Issue: 1 |
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Pages: NULL |
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Publication
Date: June 2012 |
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ISSN
(Online): 2180-1339 |
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9 - 19 |
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Published
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16-04-2012 |
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Publisher |
CSC
Journals, Kuala Lumpur,
Malaysia |
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ADDITIONAL
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| Keywords Abstract References Cited by Related Articles Collaborative
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KEYWORDS: Simple Random Sampling With Replacement (SRSWR), Simple Random Sampling Without Replacement (SRSWOR, Probability Proportional to the Size With Replacem, Probability Proportional to the Size Without Repla |
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The problem of variable selection from a large number of variables to predict certain important dependent variables has been of interest to both applied statisticians and other researchers in applied physiology. For this purpose, various statistical techniques have been developed. This framework embedded various statistical techniques of sampling and resampling and help in Statistical Simulation for Physiological Responses under different Environmental condition. The population generation and other statistical calculations are based on the inputs provided by the user as mean vector and covariance matrix and the data. This framework is developed in a way that it can work for the original data as well as for simulated data generated by the software. Approach: The mean vector and covariance matrix are sufficient statistics when the underlying distribution is multivariate normal. This framework uses these two inputs and is able to generate simulated multivariate normal population for any number of variables. The software changes the manual operation into a computer-based system to automate the study, provide efficiency, accuracy, timelessness, and economy. Result: A complete framework that can statistically simulate any type and any number of responses or variables. If the simulated data is analyzed using statistical techniques; the results of such analysis will be the same as that using the original data. If the data is missing for some of the variables, in that case the system will also help. Conclusion: The proposed system makes it possible to carry out the physiological studies and statistical calculations even if the actual data is not present.
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E. Z. Erson, M. C. Cavusoglu. "Design of a Framework for Modeling, Integration and Simulation of Physiological Models" .In Proceedings of the International Conference of the IEEE Engineering in Medicine and Biology Society(EMBC '10),August 31 - September 4, 2010, Buenos Aires, Argentina.
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Krishnamurty Muralidhar and R. Sarathy. “Generating Sufficiency based Non Synthetic Perturbed Data”. Transations on Data Privacy 117-23, 2008.
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Therese McGinn ,2004. “Instructions for Probability Proportional to Size Sampling Technique”. RHRC Consortium monitoring and evaluation toolkit , October 2004. www.rhrc.org/.../55b%20PPS%20sampling%20technique.doc
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P. Bratley ,Bennett L.Fox, Linus E. Schrage.”A Guide to Simulation” ,Second Edition .Springer, Eds. Publisher, New York, 164-165, 1987.
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P.V. Sukhatme, B.V.Sukhatme ,S.Sukhatme ,C. Asok . “Sampling Theory of surveys with application.Indian society of agricultural statistics”, new delhi India , and IOWA State University Press Ames , USA, 21-25, 1984.
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William G.Cochran. “Sampling Techniques”, Third Edition. Wiley Eastern Limited Publication New Delhi, 1985, Pp-18-30, 250-259
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Wu, C.F.J. “Jackknife, Bootstrap and other resampling methods in regression analysis”. The Annals of Statistics. Vol. 14, 4, pp. 1261–1295, 1986.
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D.L. Rubin, D.Grossman, M. Neal, D.L. Cook. “Ontology-Based Representation of Simulation Models of Physiology”. AMIA 2006 Symposium Proceedings Page – 664-668.
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Matlab help Demo
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W. D.Kelton ,”Statistical Analysis of Simulation Output”. Proceedings of the 1997 Winter Simulation Conference, Atlanta, GA, USA , 1997,Page-23-30.
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R. S. Thomas, W. E. Lytle, T. J. Keefe, Alexander A. Constan, And R. S. H. Yang “Incorporating Monte Carlo Simulation into Physiologically Based Pharmacokinetic Models Using Advanced Continuous Simulation Language (ACSL): A Computational Method”. Fundamental and applied Toxicology 31, 1996.Page-19-28.
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| Pavitra r. Gautam : Colleagues
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| YK Sharma : Colleagues
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| Shashi Bala Singh : Colleagues
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