Investigating the Properties of a Sample Mean by Employing Random Subsample Means

Jones, Howard L.

doi:10.1080/01621459.1956.10501311

Cited by 25 publications

(5 citation statements)

References 4 publications

(3 reference statements)

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“…We construct this average to minimize the contributions from any significant variations that may, by chance, fall along any individual projection. For each set of jackknife samples coming from each of the three projections of the mock catalogues, we exclude individual jackknife cells in turn, and compute the jackknife contribution in the usual manner (Jones 1956;Quenouille 1956;Tukey 1958;Wall & Jenkins 2003).…”

Section: Covariancementioning

confidence: 99%

How to optimally constrain galaxy assembly bias: supplement projected correlation functions with count-in-cells statistics

Wang

Mao

Zentner

et al. 2019

Monthly Notices of the Royal Astronomical Society

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Most models for the statistical connection between galaxies and their haloes ignore the possibility that galaxy properties may be correlated with halo properties other than halo mass, a phenomenon known as galaxy assembly bias. And yet, it is known that such correlations can lead to systematic errors in the interpretation of survey data that are analyzed using traditional halo occupation models. At present, the degree to which galaxy assembly bias may be present in the real Universe, and the best strategies for constraining it remain uncertain. We study the ability of several observables to constrain galaxy assembly bias from redshift survey data using the decorated halo occupation distribution (dHOD), an empirical model of the galaxyhalo connection that incorporates assembly bias. We cover an expansive set of observables, including the projected two-point correlation function w p (r p ), the galaxy-galaxy lensing signal ∆Σ(r p ), the void probability function VPF(r), the distributions of counts-in-cylinders P(N CIC ), and counts-in-annuli P(N CIA ), and the distribution of the ratio of counts in cylinders of different sizes P(N 2 /N 5 ). We find that despite the frequent use of the combination w p (r p ) + ∆Σ(r p ) in interpreting galaxy data, the count statistics, P(N CIC ) and P(N CIA ), are generally more efficient in constraining galaxy assembly bias when combined with w p (r p ). Constraints based upon w p (r p ) and ∆Σ(r p ) share common degeneracy directions in the parameter space, while combinations of w p (r p ) with the count statistics are more complementary. Therefore, we strongly suggest that count statistics should be used to complement the canonical observables in future studies of the galaxy-halo connection.

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Section: Covariancementioning

confidence: 99%

How to optimally constrain galaxy assembly bias: supplement projected correlation functions with count-in-cells statistics

Wang

Mao

Zentner

et al. 2019

Monthly Notices of the Royal Astronomical Society

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“…The underlying principle is quite obvious: if the random sample was produced by virtue of the “tendency” to follow a certain distribution in the original population, then the random sampling of the sample itself ( i.e. the re-sampling) would reasonably replicate the very same tendency ( 26 , 27 ).…”

Section: The Resampling-based Non-parametric (Bootstrap) CI Methodsmentioning

confidence: 99%

Confidence interval for quantiles and percentiles

Ialongo

2018

Biochem. med. (Online)

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Quantiles and percentiles represent useful statistical tools for describing the distribution of results and deriving reference intervals and performance specification in laboratory medicine. They are commonly intended as the sample estimate of a population parameter and therefore they need to be presented with a confidence interval (CI). In this work we discuss three methods to estimate CI on quantiles and percentiles using parametric, nonparametric and resampling (bootstrap) approaches. The result of our numerical simulations is that parametric methods are always more accurate regardless of sample size when the procedure is appropriate for the distribution of results for both extreme (2.5th and 97.5th) and central (25th, 50th and 75th) percentiles and corresponding quantiles. We also show that both nonparametric and bootstrap methods suit well the CI of central percentiles that are used to derive performance specifications through quality indicators of laboratory processes whose underlying distribution is unknown.

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“…While a number of well-known and highly qualiÿed OTC people-for example, Howard L. Jones (1956) of Illinois Bell and Elbert T. Magruder (1955) of C&P Telephone-studied sampling, over the years there was a need for Bell Labs statisticians to provide additional technical support.…”

Section: Sample Surveys For Engineering and Cost Analysismentioning

confidence: 99%

Operations Research at Bell Laboratories through the 1970s: Part III

Dawson¹,

McCallum²,

Murphy³

et al. 2000

Operations Research

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This historical account of operations research at Bell Laboratories was drafted in the late 1970s when the authors were part of the Operations Research Center within Bell Laboratories at AT&T; it has not previously appeared in the open literature. We have added a few references to later publications that describe particular aspects of the period covered. Discussions of technological practices and organizational arrangements expressed in the present tense represent a viewpoint of about 1980, before “divestiture” split up the Bell System. A comprehensive table of contents for parts I, II, and III can be found in the Online Collection of the Operations Research Home Page, at http://or.pubs.informs.org .

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Investigating the Properties of a Sample Mean by Employing Random Subsample Means

Cited by 25 publications

References 4 publications

How to optimally constrain galaxy assembly bias: supplement projected correlation functions with count-in-cells statistics

How to optimally constrain galaxy assembly bias: supplement projected correlation functions with count-in-cells statistics

Confidence interval for quantiles and percentiles

Operations Research at Bell Laboratories through the 1970s: Part III

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