{ "item_title" : "Recursive Restriction Estimation", "item_author" : [" Czaplewski "], "item_description" : "Numerous government surveys of natural resources use Post-Stratifi cation to improve statistical effi ciency, where strata are defi ned by full-coverage, remotely sensed data and geopolitical boundaries. Recursive Restriction Estimation, which may be considered a special case of the static Kalman filter, is an attractive alternative. It decomposes a complex estimation problem into simple components that are sequentially processed. Compared to Post-Stratification, it more efficiently uses remotely sensed data, both continuous and categorical. It is less constrained by sample size, which is especially important with panel surveys. It produces a conditionally unbiased covariance matrix for the vector estimate of population totals without approximations or ad hoc assumptions. This facilitates variance estimates for non-linear pseudo-estimators. A robust sequential algorithm controls numerical errors inherent with Recursive Restriction Estimator, which can otherwise cause unreliable results. Analysis of residuals can detect other anomalies.", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/1/50/766/617/1507666179_b.jpg", "price_data" : { "retail_price" : "15.99", "online_price" : "15.99", "our_price" : "15.99", "club_price" : "15.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }

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