Parameter Estimation in Systematic Sampling | Zendy

Schneeberger Hans | Zendy

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Parameter Estimation in Systematic Sampling

Author(s) -

Schneeberger Hans

Publication year - 1993

Publication title -

biometrical journal

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.108

H-Index - 63

eISSN - 1521-4036

pISSN - 0323-3847

DOI - 10.1002/bimj.4710350807

Subject(s) - statistics , mathematics , unbiased estimation , variance (accounting) , systematic sampling , sampling (signal processing) , best linear unbiased prediction , econometrics , bias of an estimator , sample (material) , sample size determination , population , minimum variance unbiased estimator , mean squared error , estimator , computer science , economics , demography , selection (genetic algorithm) , chemistry , accounting , filter (signal processing) , chromatography , artificial intelligence , sociology , computer vision

First it is shown that an estimate of the variance of the sample‐mean in systematic sampling from a non‐autocorrelated population with linear trend, which is published in textbooks, isn't a suitable estimate: It is biased and not dependent on the essential parameter, the slope of the linear trend. In section 2 an unbiased estimate of the variance is given. As estimate of the sample‐mean we take the same as usually used in literature. In section 3 a centric estimate of the sample‐mean is introduced, which takes into consideration the slope of the trendline. It is shown that this estimate is unbiased; an unbiased estimate of its variance is given.

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