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India's Statistical System: Past, Present, Future - Carnegie Endowment for International Peace

India's Statistical System: Past, Present, Future - Carnegie Endowment for International Peace
carnegieendowment.org - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from carnegieendowment.org Daily Mail and Mail on Sunday newspapers.

Carnegie Endowment For International Peace , District Of Columbia , United States , South Africa , City Of , United Kingdom , India General , West Bengal , New York , Harvard University , World Bank , Tamil Nadu , Madhya Pradesh , New Delhi , Ashok Rudra , Dadabhai Naoroji , Prasanta Chandra Mahalanobis , Theodore Gregory , Manish Pandya , Mekhala Krishnamurthy , Vernica Gupta , W Edwards Deming , Somesh Jha , Bernard Weinraub , Pulak Ghosh , Lok Sabha ,

India's Statistical System: Past, Present, Future - Carnegie Endowment for International Peace

India's Statistical System: Past, Present, Future - Carnegie Endowment for International Peace
carnegieendowment.org - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from carnegieendowment.org Daily Mail and Mail on Sunday newspapers.

City Of , United Kingdom , World Bank , District Of Columbia , United States , West Bengal , Harvard University , New Delhi , New York , Carnegie Endowment For International Peace , Tamil Nadu , South Africa , India General , Madhya Pradesh , W Edwards Deming , Anujit Mitra , Sunil Mitra Kumar , Rajiv Kumar , Simon Kuznets , Pronab Sen , Richard Stone , Manish Pandya , Anguss Deaton , Amitabh Kant , Dinyar Patel , Suresh Tendulkar ,

"Sample design for analysis using high-influence probability sampling" by Robert G. Clark and David G. Steel

Sample designs are typically developed to estimate summary statistics such as means, proportions and prevalences. Analytical outputs may also be a priority but there are fewer methods and results on how to efficiently design samples for the fitting and estimation of statistical models. This paper develops a general approach for determining efficient sampling designs for probability-weighted maximum likelihood estimators and considers application to generalized linear models. We allow for non-ignorable sampling, including outcome-dependent sampling. The new designs have probabilities of selection closely related to influence statistics such as dfbeta and Cook's distance. The new approach is shown to perform well in a simulation based on data from the New Zealand Health Survey. ....

New Zealand , Zealand Health , New Zealand Health , Design Based , Maximum Likelihood , Model Assisted , Poisson Sampling , Seudo Likelihood , Sample Design , Tratified Sampling ,