A ratio estimator tR and a regression estimator tRG, as alternatives to the classical ratio estimator and classical regression estimator respectively, have been developed.A comparison between chain-type and predictive-type estimators shows that the chain regression estimator is unconditionally more efficient than the predictive regression estimator. On the other hand, the chain ratio and product estimators are conditionally more efficient than their respective predictive estimators. The chain regression method of estimation is then employed to estimate the population mean on the current occasion when the two-stage sampling with SRSWOR is adopted on the current and a previous occasion. For simplicity, we consider the partial matching among FSUs only although the matching problems among SSUs and both FSUs and SSUs can be easily tackled. we consider a general class of estimators of Y. This class is very flexible in the sense of being reduced to a class of separate variety of estimators when we do not consider z and to a combined variety of estimators when we do not consider x. The design-based properties of the class of estimators have also been studied.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -A ratio estimator tR and a regression estimator tRG, as alternatives to the classical ratio estimator and classical regression estimator respectively, have been developed.A comparison between chain-type and predictive-type estimators shows that the chain regression estimator is unconditionally more efficient than the predictive regression estimator. On the other hand, the chain ratio and product estimators are conditionally more efficient than their respective predictive estimators. The chain regression method of estimation is then employed to estimate the population mean on the current occasion when the two-stage sampling with SRSWOR is adopted on the current and a previous occasion. For simplicity, we consider the partial matching among FSUs only although the matching problems among SSUs and both FSUs and SSUs can be easily tackled. we consider a general class of estimators of Y. This class is very flexible in the sense of being reduced to a class of separate variety of estimators when we do not consider z and to a combined variety of estimators when we do not consider x. The design-based properties of the class of estimators have also been studied. 80 pp. Englisch. N° de réf. du vendeur 9786203930436
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Sahoo Ranjan KumarRanjan Kumar Sahoo, Ph.DAssociate Professor & Head,School of Statistics, Gangadhar Meher UniversityA ratio estimator tR and a regression estimator tRG, as alternatives to the classical ratio estimator and classi. N° de réf. du vendeur 506531199
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Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -A ratio estimator tR and a regression estimator tRG, as alternatives to the classical ratio estimator and classical regression estimator respectively, have been developed.A comparison between chain-type and predictive-type estimators shows that the chain regression estimator is unconditionally more efficient than the predictive regression estimator. On the other hand, the chain ratio and product estimators are conditionally more efficient than their respective predictive estimators. The chain regression method of estimation is then employed to estimate the population mean on the current occasion when the two-stage sampling with SRSWOR is adopted on the current and a previous occasion. For simplicity, we consider the partial matching among FSUs only although the matching problems among SSUs and both FSUs and SSUs can be easily tackled. we consider a general class of estimators of Y. This class is very flexible in the sense of being reduced to a class of separate variety of estimators when we do not consider z and to a combined variety of estimators when we do not consider x. The design-based properties of the class of estimators have also been studied.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 80 pp. Englisch. N° de réf. du vendeur 9786203930436
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Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - A ratio estimator tR and a regression estimator tRG, as alternatives to the classical ratio estimator and classical regression estimator respectively, have been developed.A comparison between chain-type and predictive-type estimators shows that the chain regression estimator is unconditionally more efficient than the predictive regression estimator. On the other hand, the chain ratio and product estimators are conditionally more efficient than their respective predictive estimators. The chain regression method of estimation is then employed to estimate the population mean on the current occasion when the two-stage sampling with SRSWOR is adopted on the current and a previous occasion. For simplicity, we consider the partial matching among FSUs only although the matching problems among SSUs and both FSUs and SSUs can be easily tackled. we consider a general class of estimators of Y. This class is very flexible in the sense of being reduced to a class of separate variety of estimators when we do not consider z and to a combined variety of estimators when we do not consider x. The design-based properties of the class of estimators have also been studied. N° de réf. du vendeur 9786203930436
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Vendeur : preigu, Osnabrück, Allemagne
Taschenbuch. Etat : Neu. Some Contributions in Two-Stage Sampling Using Auxiliary Information | A Hand Book | Ranjan Kumar Sahoo | Taschenbuch | Englisch | 2021 | LAP LAMBERT Academic Publishing | EAN 9786203930436 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. N° de réf. du vendeur 120524317
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