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View all- Nummi T(2021)Some Models and Their Extensions for Longitudinal AnalysesData Science and SDGs10.1007/978-981-16-1919-9_2(15-30)Online publication date: 14-Aug-2021
APL as a tool for computations in growth studies
Author: 
T. NummiAuthors Info & Claims
Pages 293 - 298
Abstract
The aim of this paper is to introduce some computational ideas for carrying out the multivariate growth curve analysis. In longitudinal studies missing data is often involved. Therefore handling of incomplete data is an important part of the analysis. Here we introduce a technique for estimating growth curve parameters based on the least squares approach. It was shown that many computational problems in growth curve analysis can be solved easily using APL.
References
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Anscombe, F. (1981). Computing in Statistical Science through APL. Springer Series in Statistics. New York: Springer-Verlag.
[2]
Bohman J. and Froberg C. (1980). Programmera i APL. Lund: Studentlitteratur.
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Box, G.E.P. and Jenkins, G.M. (1970). Time Series Analysis, Forecasting and Control, San Francisco: Holden-Day.
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Grenander, Ulf (1982). Mathematical experiments on the computer. New York: Academic Press.
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Helzer, G. (1983). Linear Algebra with APL. Boston: Little Brown.
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Khatri, C.G. (1966). A note on a MANOVA model applied to problems in growth curves. Annuls of the Institute of Statistical Mathematics, 18,75-86.
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Kleinbaum, D.G. (1973). A generalization of the growth curve model which allows missing data. Journal of Multivariate Analysis, 3, 117-124.
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Liski, E.P. (1985). Estimation from incomplete data in growth curves models. Communications in Statistics - Computation and Simulation, 14, No.1,13-27.
[9]
Liski, E.P. and Nummi, T. (1986). An APL-system for analyzing multivariate data. Proceedings of Compstat 1986. Heidelberg: Physica-Verlag.
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Liski, E. P. and Nummi, T. (1988). Comparing sensitivity of models to missing data in the GMANOVA. Proceedings of Compstat 1988. Heidelberg: Physica-Verlag.
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Potthoff, R.F. and Roy, S.N. (1964). A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika, 5 1,3 13-326.
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Rao, C.R. (1965). The theory of least squares when the parameters are stochastic and its application to the analysis of growth curves. Biometrika, 52,447-458.
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Rao, C.R. (1966). Covariance adjustment and related problems in multivariate analysis. Multivariate Analysis, 87-103. New York: Academic Press.
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Rao, C.R. (1967). Least squares theory using an estimated dispersion matrix and its application to measurement of signals. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, 1,355-372.
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Schwertman, N.C. and Allen D.M. (1979). Smoothing an Indefinite Variance-Covariance Matrix. Journal of Sfutistical Computing and Simulation, 9, 183-194.
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Aug. 1989376 pages
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Published: 01 July 1989
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View all- Nummi T(2021)Some Models and Their Extensions for Longitudinal AnalysesData Science and SDGs10.1007/978-981-16-1919-9_2(15-30)Online publication date: 14-Aug-2021
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