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Some Statistical Perspectives of Growth Models in Health Care Plans

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Advances in Growth Curve Models

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 46))

Abstract

Growth (and wear) curve models, having genesis in epidemiology and system biology, have cropped up in every walk of life and science. In statistics, such growth curve models have led to an evolution of multivariate analysis with better performance characteristics and enhanced scope of applications in many interdisciplinary field of research. Recent advances in bioinformatics and genomic science have opened the Pandora’s box with high-dimensional data models, often with relatively smaller sample sizes. Growth curve models are especially useful in such contexts. There are also other areas where growth curve model-based analyses are in high demand. In this vein, the scope and perspectives of growth models are appraised with special emphasis on some health care and health study plans.

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Selected Reference

  • Box, G.E.P. (1950). Problem in the analysis of growth and wear curves. Biometrics, 6, 362–389.

    Article  Google Scholar 

  • Box, G.E.P., & Cox, D.R. (1964). An analysis of transformations. Journal of the Royal Statistical Society, Series, 26, 211–252.

    MathSciNet  MATH  Google Scholar 

  • Chatterjee, S.K., & Sen, P.K. (1964). Nonparametric tests for the bivariate two-sample problem. Calcutta Statistical Association Bulletin, 22, 13–50.

    MathSciNet  Google Scholar 

  • Chen, C.C., Sen, P.K., & Wu, K.-Y.(2012). Permutation tests for homogeneity of fingerprint patterns of dioxin congener profiles. Environmetrics, in press.

    Google Scholar 

  • Chen, L.H.Y. (1975). Poisson approximation for dependent trials. Annals of Probability, 3, 534–545.

    Article  MATH  Google Scholar 

  • Cole, J.W.L., & Grizzle, J.E. (1966) Applications of multivariate analysis of variance to repeated measurements experiments. Biometrics, 22, 810–828.

    Article  Google Scholar 

  • Geisser, S. (1970). Bayesian analysis of growth curves. Sankhya, Ser. A, 32, 53–64.

    Google Scholar 

  • Geisser, G. (1981). Growth curve analysis. In P.R. Krishnaiah (Ed.) Handbook of Statistics, Vol. 1: Analysis of Variance (pp. 89–115). Amsterdam: North Holland.

    Google Scholar 

  • Ghosh, M., Grizzle, J.E., & Sen, P.K. (1973). Nonparametric methods in longitudinal studies. Journal of the American Statistical Association, 68, 29–36.

    Article  Google Scholar 

  • Gnanadesikan, R., Srivastava, J.N., Roy, S.N., Foulkes, E.B., & Lee, E.T. (1971). Analysis and design of certain quantitative multiresponse experiments. New York: Pergamon Press.

    MATH  Google Scholar 

  • Gompertz, B. (1825). Philosophical Transactions of the Royal Society, Ser. A, 115, 513–580.

    Google Scholar 

  • Grizzle, J.E., & Allen, D.M. (1969). Analysis of growth and dose-response curves. Biometrics, 25, 357–381.

    Article  Google Scholar 

  • Johnson, N.L., & Kotz, S. (1970). Distributions in statistics: continuous univariate distributions. New York: Wiley.

    MATH  Google Scholar 

  • Jurečková, J., & sen, P.K. (1996). Robust statistical procedures: asymptotics and interrelations. New York: Wiley.

    Google Scholar 

  • Khatri, C.G. (1966). A note on a MANOVA model applied to problems in growth curves. Annals of the Instutute of Statistical Mathematics, 18, 75–86.

    Article  MathSciNet  MATH  Google Scholar 

  • Mandal, S., Sen, P.K., & Peddada, S.D. (2012). Statistical inference for dynamic systems governed by differential equations with application to toxicology. (under preparation).

    Google Scholar 

  • Potthoff, R.F., & Roy, S.N. (1964). Generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika, 51, 313–326.

    MathSciNet  MATH  Google Scholar 

  • Preisser, J., Sen, P.K., & Offenbacher, S. (2011). Multiple hypothesis testing for experimental gingivitis based on Wilcoxon signed rank statistics. Statistics in Biopharmaceutical Research, 3, 372–384.

    Article  Google Scholar 

  • Puri, M.L., & Sen, P.K. (1971). Nonparametric methods in multivariate analysis. New York: Wiley.

    MATH  Google Scholar 

  • Puri, M.L., & Sen, P.K. (1985). Nonparametric methods in general linear models. New York: Wiley.

    MATH  Google Scholar 

  • Rao, C.R. (1958). Comparison of growth curves. Biometrics, 14, 1–16.

    Article  MATH  Google Scholar 

  • Rao, C.R. (1959). Some problems involving linear hypotheses in multivariate analysis. Biometrika, 46, 49–58.

    MathSciNet  MATH  Google Scholar 

  • Rao, C.R. (1965). Theory of least squares when the parameters are stochastic and its application to the analysis of growth curves. Biometrika, 52, 447–458.

    MathSciNet  MATH  Google Scholar 

  • Sen, P.K. (1973). Some aspects of nonparametric procedures in multivariate statistical analysis. In D.G. Kabe, & R.P. Gupta (Eds.) Multivariate statistical analysis (pp. 231–240). Amsterdam: North Holland.

    Google Scholar 

  • Sen, P.K. (1985). Nonparametric procedures for some miscellaneous problems. In P.R. Krishnaiah, & P.K. Sen (Eds.) Handbook of statistics, Vol. 4: Nonparametric methods (pp. 699–739). Amsterdam: North Holland.

    Google Scholar 

  • Sen, P.K. (2006). Robust statistical procedures for high-dimensional data models with applications to genomics. Austrian Journal of Statistics, 35, 197–214.

    Google Scholar 

  • Sen, P.K. (2008). Kendall’s tau in high-dimensional genomic parsimony. In Institute of mathematical statistics, collection series, vol. 3 (pp. 251–266).

    Google Scholar 

  • Sen, P.K. (2012). Development and management of national health plans: Health economics and Statistical perspectives. In Y.P. Chaubey (Ed.) Some topics on current issues in mathematical and statistical methods. Singapore: World Scientific Press.

    Google Scholar 

  • Sen, P.K., Singer, J.M., & Pedroso de Lima, A.C. (2010). Finite Sample to Asymptotic Methods in Statistics. Cambridge University Press, New York.

    MATH  Google Scholar 

  • Sen, P.K., Jurečková, J., & Picek, J. (2013). Rank tests for corrupted linear models. Journal of the Indian Statistical Association, 51(1), in press.

    Google Scholar 

  • Sen, P.K., & Puri, M.L. (1967). On the theory of rank order tests for location in the multivariate one sample problem. Annals of Mathematical Statistics, 38, 1216–1228.

    Article  MathSciNet  MATH  Google Scholar 

  • Sen, P.K., & Rao, C.R. (Eds.) (2000). Handbook of statistics, vol. 18: Bioenvironmental and public health sciences. Amsterdam: North Holland.

    Google Scholar 

  • Sen, P.K., Tsai, M.-T., & Jou, Y.S. (2007). High-dimension low sample size perspectives in constrained statistical inference: The SARSCOVRNA genome in illustration. Journal of the American Statistical Association, 102, 685–694.

    Article  MathSciNet  Google Scholar 

  • Timm, N.H. (1981). Multivariate analysis of variance of repeated measurements. In P.R. Krishnaiah (Ed.) Handbook of statistics, vol. 1: Analysis of variance (pp. 41–87). Amsterdam: North-Holland.

    Google Scholar 

  • Zerbe, G.O., & Walker, S.H. (1977). A randomization test for comparison of groups of growth curves with different polynomial design matrices. Biometrics, 33, 653–659.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Departments of Biostatistics, and Statistics & Operations Research, University of North Carolina, Chapel Hill, NC, 27599-7420, USA

    Pranab K. Sen

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  1. Pranab K. Sen
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Correspondence to Pranab K. Sen .

Editor information

Editors and Affiliations

  1. Indian Statistical Institute Professor, Stat-Math Unit, Calcutta, India

    Ratan Dasgupta

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Sen, P.K. (2013). Some Statistical Perspectives of Growth Models in Health Care Plans. In: Dasgupta, R. (eds) Advances in Growth Curve Models. Springer Proceedings in Mathematics & Statistics, vol 46. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6862-2_2

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