Analysis of multivariate repeated measures data with a Kronecker product structured covariance matrix

In this article we consider a set of t repeated measurements on p variables (or characteristics) on each of the n individuals. Thus, data on each individual is a p ×t matrix. The n individuals themselves may be divided and randomly assigned to g groups. Analysis of these data using a MANOVA model, assuming that the data on an individual has a covariance matrix which is a Kronecker product of two positive definite matrices, is considered. The well-known Satterthwaite type approximation to the distribution of a quadratic form in normal variables is extended to the distribution of a multivariate quadratic form in multivariate normal variables. The multivariate tests using this approximation are developed for testing the usual hypotheses. Results are illustrated on a data set. A method for analysing unbalanced data is also discussed.     

A Gompertz model for birth interval analysis

During recent years birth intervals have been analysed on a life table basis. This method retains both closed and open intervals, and so reflects behaviour that deliberately avoids the next birth entirely. When life tables are prepared separately for each birth order, markedly different patterns of movement toward the next birth can appear from one parity to the next. This is illustrated for Korean survey data, with historical trends given across marriage cohorts.

A Gompertz model is found to fit the family of curves that show the cumulative proportion giving birth within each interval closely. Its three parameters have direct intuitive interpretations, one being equal to the parity progression ratio and the other two controlling the pace of childbearing before and after the point of peak activity within the interval. The model is useful for interpolation and projection, and provides an efficient summary of the otherwise cumbersome detail given in a life table. Testing against additional data sets is suggested.