Abstract
A new variant of the CLT is established for random fields defined on ℝdwhich are strictly stationary, with a finite second moment and weakly dependent (comprising cases of positive or negative association). The summation domains grow in the van Hove sense. At the same time the indices of observations form more and more dense grids in these domains. Thus the effect of combining two scaling procedures is studied. A statistical version of this CLT is also proved. Some stochastic models in radiobiology based on dependent functional subunits are discussed as well.
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The research is partially supported by RFBR grant 07-01-00373a.
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Bulinski, A. (2010). Central Limit Theorem for Random Fields and Applications. In: Skiadas, C. (eds) Advances in Data Analysis. Statistics for Industry and Technology. Birkhäuser Boston. https://doi.org/10.1007/978-0-8176-4799-5_13
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DOI: https://doi.org/10.1007/978-0-8176-4799-5_13
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