document: Efficient search engine measurements We address the problem of measuring global quality met-rics of search engines , like corpus size , index freshness , anddensity of duplicates in the corpus . The recently proposedestimators for such metrics ( 2 , 6 ) suffer from significant biasand\/or poor performance , due to inaccurate approximationof the so called . document degrees . . We present two new estimators that are able to overcomethe bias introduced by approximate degrees . Our estimatorsare based on a careful implementation of an approximateimportance sampling procedure . Comprehensive theoreti-cal and empirical analysis of the estimators demonstratesthat they have essentially no bias even in situations wheredocument degrees are poorly approximated . Building on an idea from ( 6 ) , we discuss Rao Blackwelliza-tion as a generic method for reducing variance in searchengine estimators . We show that Rao-Blackwellizing ourestimators results in significant performance improvements , while not compromising accuracy .
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