By Ray Chambers, Robert Clark
This article brings jointly very important principles at the model-based method of pattern survey, which has been built over the past two decades. appropriate for graduate scholars statisticians, it strikes from uncomplicated rules primary to sampling to extra rigorous mathematical modelling and knowledge research and comprises routines and recommendations.
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Extra resources for An Introduction to Model-Based Survey Sampling with Applications
It follows that there are considerable gains to be had in stratifying so that the resulting strata are more homogeneous than the original population. 5 Optimal Allocation Can we do better than size stratiﬁcation and proportional allocation? 2) subject to an overall sample size of n, that is h nh = n, the answer is yes. 3) of this estimator. We see that it can be decomposed into two terms, Var tˆSy − ty = h Nh2 σh2 /nh − h Nh σh2 Only the ﬁrst term depends on the nh , and minimising Var tˆSy − ty is therefore equivalent to choosing nh in order to minimise h Nh2 σh2 /nh subject to the restriction h nh = n.
9 which would be rounded to n1 = n2 = 8. On the other hand, for random stratiﬁcation N1 Sy1 = 3654 and N2 Sy2 = 11078, leading to an optimal allocation deﬁned by n1 = 4 and n2 = 12, that is proportional allocation. This result is hardly surprising given that the stratum variances in the two random strata are approximately the same and equal to the overall population variance. 3) of the stratiﬁed expansion estimator (with σh2 replaced by Syh ) for the Cities’ population is 2,200,908 under size stratiﬁcation and 10,172,572 under random stratiﬁcation.
For example, Fisher and Yates (1963) provide a list of two digit random numbers, together with instructions on how to use them to select a simple random sample. When sample sizes and populations are large, it is usually most convenient to use a computer to select the sample. Most computer packages include a pseudorandom number generator, which can be used in this regard. A simple way of selecting a SRS of size n, using a computer-based random number generator, is to randomly order the population units on the sampling frame, then take the ﬁrst n of these randomly ordered units to be the sample.
An Introduction to Model-Based Survey Sampling with Applications by Ray Chambers, Robert Clark