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Time Series Model for Predicting the Mean Death Rate of a Disease

100%

Shangodoyin D. K. , Ojo J. F. , Olaomi J. O. , Adebile A. O.

Statistics in Transition new series

2012

tom 13

nr 2

405-418

This study develops a time series model to estimate the mean death rate of either an emerging disease or re-emerging disease with a bilinear induced model. The estimated death rate converges rapidly to the true parameter value for a given mean death at time t. The derived model could be used in predicting the m-step future death rate value of a given disease. We illustrated the new concept with real life data.

An Approximation To The Optimal Subsample Allocation For Small Areas

100%

Molefe W. B. , Shangodoyin D. K. , Clark R. G.

Statistics in Transition new series

2015

tom 16

nr 2

163-182

This paper develops allocation methods for stratified sample surveys in which small area estimation is a priority. We assume stratified sampling with small areas as the strata. Similar to Longford (2006), we seek efficient allocation that minimizes a linear combination of the mean squared errors of composite small area estimators and of an estimator of the overall mean. Unlike Longford, we define mean-squared error in a model-assisted framework, allowing a more natural interpretation of results using an intra-class correlation parameter. This allocation has an analytical form for a special case, and has the unappealing property that some strata may be allocated no sample. We derive a Taylor approximation to the stratum sample sizes for small area estimation using composite estimation giving priority to both small area and national estimation.