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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 SASĀ®
, 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 615 (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
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 69 (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 444 (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
Approximate Frequency Counts over Data Streams
 VLDB
, 2002
"... We present algorithms for computing frequency counts exceeding a userspecified threshold over data streams. Our algorithms are simple and have provably small memory footprints. Although the output is approximate, the error is guaranteed not to exceed a userspecified parameter. Our algorithms can e ..."
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Cited by 418 (1 self)
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We present algorithms for computing frequency counts exceeding a userspecified threshold over data streams. Our algorithms are simple and have provably small memory footprints. Although the output is approximate, the error is guaranteed not to exceed a userspecified parameter. Our algorithms can
Probabilistic Latent Semantic Indexing
, 1999
"... Probabilistic Latent Semantic Indexing is a novel approach to automated document indexing which is based on a statistical latent class model for factor analysis of count data. Fitted from a training corpus of text documents by a generalization of the Expectation Maximization algorithm, the utilized ..."
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Cited by 1225 (10 self)
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Probabilistic Latent Semantic Indexing is a novel approach to automated document indexing which is based on a statistical latent class model for factor analysis of count data. Fitted from a training corpus of text documents by a generalization of the Expectation Maximization algorithm, the utilized
Essentials of Count Data Regression
, 1999
"... In many economic contexts the dependent or response variable of interest (y) is a nonnegative integer or count which we wish to explain or analyze in terms of a set of covariates (x). Unlike the classical regression model, the response variable is discrete with a distribution that places probability ..."
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Cited by 5 (0 self)
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In many economic contexts the dependent or response variable of interest (y) is a nonnegative integer or count which we wish to explain or analyze in terms of a set of covariates (x). Unlike the classical regression model, the response variable is discrete with a distribution that places
Results 1  10
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14,884