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count data
, 2008
"... A Bayesian moving average model for correlated count data In this paper we propose a new regression modeling approach for responses that are a correlated time series of counts. The approach is based on a hierarchical Bayesian model that incorporates a latent moving average process with gamma random ..."
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A Bayesian moving average model for correlated count data In this paper we propose a new regression modeling approach for responses that are a correlated time series of counts. The approach is based on a hierarchical Bayesian model that incorporates a latent moving average process with gamma random
COUNTING DATA
, 1997
"... I describe a new timedomain algorithm for detecting localized structures (bursts), revealing pulse shapes, and generally characterizing intensity variations. The input is raw counting data, in any of three forms: timetagged photon events (TTE), binned counts, or timetospill (TTS) data. The outpu ..."
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I describe a new timedomain algorithm for detecting localized structures (bursts), revealing pulse shapes, and generally characterizing intensity variations. The input is raw counting data, in any of three forms: timetagged photon events (TTE), binned counts, or timetospill (TTS) data
Count Data Models in SAS®
, 2008
"... Poisson regression has been widely used to model count data. However, it is often criticized for its restrictive assumption of equidispersion, meaning equality between the variance and the mean. In reallife applications, count data often exhibits overdispersion and excess zeroes. While Negative b ..."
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Poisson regression has been widely used to model count data. However, it is often criticized for its restrictive assumption of equidispersion, meaning equality between the variance and the mean. In reallife applications, count data often exhibits overdispersion and excess zeroes. While Negative
Dynamic Itemset Counting and Implication Rules for Market Basket Data
, 1997
"... We consider the problem of analyzing marketbasket data and present several important contributions. First, we present a new algorithm for finding large itemsets which uses fewer passes over the data than classic algorithms, and yet uses fewer candidate itemsets than methods based on sampling. We in ..."
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Cited by 599 (6 self)
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We consider the problem of analyzing marketbasket data and present several important contributions. First, we present a new algorithm for finding large itemsets which uses fewer passes over the data than classic algorithms, and yet uses fewer candidate itemsets than methods based on sampling. We
Count Data Models With Selectivity
, 1996
"... This paper shows how truncated, censored, hurdle, zero in ated and underreported count models can be interpreted as models with selectivity. Until recently, such count data models have commonly imposed independence between the count generating mechanism and the selection mechanism. Such an assumptio ..."
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Cited by 7 (0 self)
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This paper shows how truncated, censored, hurdle, zero in ated and underreported count models can be interpreted as models with selectivity. Until recently, such count data models have commonly imposed independence between the count generating mechanism and the selection mechanism
SeibergWitten prepotential from instanton counting
, 2002
"... In my lecture I consider integrals over moduli spaces of supersymmetric gauge field configurations (instantons, Higgs bundles, torsion free sheaves). The applications are twofold: physical and mathematical; they involve supersymmetric quantum mechanics of Dparticles in various dimensions, direct co ..."
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Cited by 496 (9 self)
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In my lecture I consider integrals over moduli spaces of supersymmetric gauge field configurations (instantons, Higgs bundles, torsion free sheaves). The applications are twofold: physical and mathematical; they involve supersymmetric quantum mechanics of Dparticles in various dimensions, direct computation of the celebrated SeibergWitten prepotential, sum rules for the solutions of the Bethe ansatz equations and their relation to the Laumon’s nilpotent cone. As a byproduct we derive some combinatoric identities involving the sums over Young tableaux.
Regression Models for Count Data in R
"... The classical Poisson, geometric and negative binomial regression models for count data belong to the family of generalized linear models and are available at the core of the statistics toolbox in the R system for statistical computing. After reviewing the conceptual and computational features of th ..."
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Cited by 66 (4 self)
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The classical Poisson, geometric and negative binomial regression models for count data belong to the family of generalized linear models and are available at the core of the statistics toolbox in the R system for statistical computing. After reviewing the conceptual and computational features
Probabilistic Counting Algorithms for Data Base Applications
, 1985
"... This paper introduces a class of probabilistic counting lgorithms with which one can estimate the number of distinct elements in a large collection of data (typically a large file stored on disk) in a single pass using only a small additional storage (typically less than a hundred binary words) a ..."
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Cited by 449 (6 self)
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This paper introduces a class of probabilistic counting lgorithms with which one can estimate the number of distinct elements in a large collection of data (typically a large file stored on disk) in a single pass using only a small additional storage (typically less than a hundred binary words
Data Security
, 1979
"... The rising abuse of computers and increasing threat to personal privacy through data banks have stimulated much interest m the techmcal safeguards for data. There are four kinds of safeguards, each related to but distract from the others. Access controls regulate which users may enter the system and ..."
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Cited by 611 (3 self)
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The rising abuse of computers and increasing threat to personal privacy through data banks have stimulated much interest m the techmcal safeguards for data. There are four kinds of safeguards, each related to but distract from the others. Access controls regulate which users may enter the system
Implementing data cubes efficiently
 In SIGMOD
, 1996
"... Decision support applications involve complex queries on very large databases. Since response times should be small, query optimization is critical. Users typically view the data as multidimensional data cubes. Each cell of the data cube is a view consisting of an aggregation of interest, like total ..."
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Cited by 545 (1 self)
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Decision support applications involve complex queries on very large databases. Since response times should be small, query optimization is critical. Users typically view the data as multidimensional data cubes. Each cell of the data cube is a view consisting of an aggregation of interest, like
Results 1  10
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1,632,861