Estimating the Number of Classes in a Finite Population

Haas, Peter J.; Stokes, Lynne

doi:10.2307/2670061

Cited by 23 publications

(46 citation statements)

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“…A standard trick described in [18] improves on (2) and also the two other estimators. The idea is to separate the observed haplotypes into two groups: abundant and rare .…”

Section: Discussionmentioning

confidence: 99%

Estimating Haplotype Frequency and Coverage of Databases

Egeland

Salas

2008

PLoS ONE

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A variety of forensic, population, and disease studies are based on haploid DNA (e.g. mitochondrial DNA or Y-chromosome data). For any set of genetic markers databases of conventional size will normally contain only a fraction of all haplotypes. For several applications, reliable estimates of haplotype frequencies, the total number of haplotypes and coverage of the database (the probability that the next random haplotype is contained in the database) will be useful. We propose different approaches to the problem based on classical methods as well as new applications of Principal Component Analysis (PCA). We also discuss previous proposals based on saturation curves. Several conclusions can be inferred from simulated and real data. First, classical estimates of the fraction of unseen haplotypes can be seriously biased. Second, there is no obvious way to decide on required sample size based on traditional approaches. Methods based on testing of hypotheses or length of confidence intervals may appear artificial since no single test or parameter stands out as particularly relevant. Rather the coverage may be more relevant since it indicates the percentage of different haplotypes that are contained in a database; if the coverage is low, there is a considerable chance that the next haplotype to be observed does not appear in the database and this indicates that the database needs to be expanded. Finally, freeware and example data sets accompany the methods discussed in this paper: http://folk.uio.no/thoree/nhap/.

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“…A standard trick described in [18] improves on (2) and also the two other estimators. The idea is to separate the observed haplotypes into two groups: abundant and rare .…”

Section: Discussionmentioning

confidence: 99%

Estimating Haplotype Frequency and Coverage of Databases

Egeland

Salas

2008

PLoS ONE

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“…In general, there is no known efficient (i.e., sampling-based) method to estimate a priori the number of rows in an agg-tablei.e., the number of groups that the query's GROUP BY clause produces-with guaranteed error bounds [10]. However, by carefully defining the estimation problem, we can sidestep these issues so that a sampling-based technique will meet our needs.…”

Section: Query Parameter Estimationmentioning

confidence: 99%

“…Efficiently predicting the working set size of a query, e.g, by sampling, is a non-trivial problem. For example, with a group-by query, if we adopt a simple bound on the working set -the number of distinct group-by values, we have to solve the infamous sample-based distinct count estimation problem [10].…”

Section: Introductionmentioning

confidence: 99%

Main-memory scan sharing for multi-core CPUs

et al. 2008

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Computer architectures are increasingly based on multi-core CPUs and large memories. Memory bandwidth, which has not kept pace with the increasing number of cores, has become the primary processing bottleneck, replacing disk I/O as the limiting factor. To address this challenge, we provide novel algorithms for increasing the throughput of Business Intelligence (BI) queries, as well as for ensuring fairness and avoiding starvation among a concurrent set of such queries. To maximize throughput, we propose a novel FullSharing scheme that allows all concurrent queries, when performing base-table I/O, to share the cache belonging to a given core. We then generalize this approach to a BatchSharing scheme that avoids thrashing on "agg-tables"-hash tables that are used for aggregation processing-caused by execution of too many queries on a core. This scheme partitions queries into batches such that the working-set of agg-table entries for each batch can fit into a cache; an efficient sampling technique is used to estimate selectivities and working-set sizes for purposes of query partitioning. Finally, we use lottery-scheduling techniques to ensure fairness and impose a hard upper bound on staging time to avoid starvation. On our 8-core testbed, we were able to completely remove the memory I/O bottleneck, increasing throughput by a factor of 2 to 2.5, while also maintaining fairness and avoiding starvation.

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“…Estimators of species richness may differ in sensitivity to the sampling design due to differential reliance on the number and relative incidence of species observed in a sample (Haas & Stokes, 1998;Mao & Lindsay, 2007;Walther & Morand, 1998); thus the four estimators can only provide an example of how the sample design and choice of sampling unit can affect estimators of richness. On the other hand, their appeal to forest tree species richness estimation will increase if they also demonstrate insensitivity to the design for sample collection (EQ, STR, SRS) and choice of sampling unit (CLU, T1).…”

Section: Estimators Of Species Richnessmentioning

confidence: 99%

Sample-based Estimation of Regional Forest Tree Species Richness

Magnussen¹

2011

ENRR

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Sample-based estimates of species richness for large heterogeneous regions may be affected by interactions between the sampling design and sub-regional heterogeneity in species richness and diversity. To investigate this issue a Monte Carlo simulation study was conducted with actual forest inventory tree species incidence data from two large regions, four estimators of species richness, three sampling designs, two sampling units (fixed area plots and tree nearest to plot center), and five sample sizes. The regions represent forested areas in central and eastern Canada and three states in the USA. Design effects in estimates of regional species richness were generally weak, of little practical import, and limited to settings with the smallest sample sizes. The study shows that accurate regional estimates of richness can be obtained by pooling sub-regional species incidence data. Sampling with fixed area plots was, with few estimator-dependent exceptions, considerably more efficient than sampling with one tree per sample location. A proposed ratio-based procedure for combining sub-regional estimates of richness broadens the options for regional estimation.

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Estimating the Number of Classes in a Finite Population

Cited by 23 publications

References 0 publications

Estimating Haplotype Frequency and Coverage of Databases

Estimating Haplotype Frequency and Coverage of Databases

Main-memory scan sharing for multi-core CPUs

Sample-based Estimation of Regional Forest Tree Species Richness

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