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Complex Sample Survey Estimation in Static State-Space - Raymond Czaplewski

angļu valoda
2015-02-13
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Increased use of remotely sensed data is a key strategy adopted by the Forest Inventory and Analysis Program. However, multiple sensor technologies require complex sampling units and sampling designs. The Recursive Restriction Estimator (RRE) accommodates this complexity. It is a design-consistent Empirical Best Linear Unbiased Prediction for the state-vector, which contains all suffi cient statistics for t ... Pilns apraksts

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Increased use of remotely sensed data is a key strategy adopted by the Forest Inventory and Analysis Program. However, multiple sensor technologies require complex sampling units and sampling designs. The Recursive Restriction Estimator (RRE) accommodates this complexity. It is a design-consistent Empirical Best Linear Unbiased Prediction for the state-vector, which contains all suffi cient statistics for the sampled population. RRE reduces a complex estimator into a sequence of simpler estimators. Also included are model-based pseudo-estimators and multivariate Taylor series approximations for covariance matrices. Together, these provide a unifi ed approach to detailed estimation in large, complex sample surveys.

Vairāk informācijas

Autors Raymond Czaplewski
Izdevējs CreateSpace Independent Publishing Platform
Izlaides gads 2015
Vāka tips Mīkstais vāks
EAN 9781506140056
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16,94 € 28,24 €