Results 271 to 280 of about 26,443,777 (315)
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2015
This paper surveys panel data methods for count dependent variable that takes nonnegative integer values, such as number of doctor visits. The focus is on short panels, as the literature has concentrated on this case. The survey covers both static and dynamic models with random and xed e ects. The paper surveys quasi-ML methods based on the Poisson, as
Cameron, Colin, Trivedi, Pravin K.
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This paper surveys panel data methods for count dependent variable that takes nonnegative integer values, such as number of doctor visits. The focus is on short panels, as the literature has concentrated on this case. The survey covers both static and dynamic models with random and xed e ects. The paper surveys quasi-ML methods based on the Poisson, as
Cameron, Colin, Trivedi, Pravin K.
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Regression Analysis of Count Data
1998Students in both social and natural sciences often seek regression methods to explain the frequency of events, such as visits to a doctor, auto accidents, or new patents awarded. This book, now in its second edition, provides the most comprehensive and up-to-date account of models and methods to interpret such data.
Cameron, A. Colin, Trivedi, Pravin K.
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Predictive Model Assessment for Count Data
Biometrics, 2009Summary We discuss tools for the evaluation of probabilistic forecasts and the critique of statistical models for count data. Our proposals include a nonrandomized version of the probability integral transform, marginal calibration diagrams, and proper scoring rules ...
Czado, C, Gneiting, T, Held, L
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Journal of the American Statistical Association, 2006
In this article we characterize all two-parameter count distributions (satisfying very general conditions) that are partially closed under addition. We also find those for which the maximum likelihood estimator of the population mean is the sample mean. Mixed Poisson models satisfying these properties are completely determined.
Puig, Pedro, Valero, Jordi
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In this article we characterize all two-parameter count distributions (satisfying very general conditions) that are partially closed under addition. We also find those for which the maximum likelihood estimator of the population mean is the sample mean. Mixed Poisson models satisfying these properties are completely determined.
Puig, Pedro, Valero, Jordi
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Bootstrapping with Models for Count Data
Journal of Biopharmaceutical Statistics, 2011Two methods of bootstrap resampling are discussed with log-linear models for count data. The first involves the resampling of observations and the second involves the resampling of Pearson residuals taking into account changes in the distribution of residuals associated with the expected values of counts.
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OD Count Estimation Based on Link Count Data
2008TM (Traffic Matrix) estimation is a hot research area recently. Current TM estimation methods are generally designed for backbone and ISP networks. They estimate complete TM which is unnecessary for many IP networks in reality and especially unsuitable for the networks that have many entries.
Yi Jin +5 more
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Counting Distinct Elements in a Data Stream
2002We present three algorithms to count the number of distinct elements in a data stream to within a factor of 1 ± ?. Our algorithms improve upon known algorithms for this problem, and offer a spectrum of time/space tradeoffs.
Bar-Yossef, Ziv +4 more
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Regression Analysis of Count Data
Technometrics, 1999Students in both social and natural sciences often seek regression methods to explain the frequency of events, such as visits to a doctor, auto accidents, or new patents awarded. This book, now in its second edition, provides the most comprehensive and up-to-date account of models and methods to interpret such data.
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Analysis of overlapping count data
Communications in Statistics - Simulation and Computation, 2022Kenneth Joseph Ryan +3 more
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The American Statistician, 2017
Zhu, L., Sellers, K. F., Morris, D. S., and Shmueli, G. (2017), “Bridging the Gap: A Generalized Stochastic Process for Count Data,” The American Statistician, 71, 71–80.
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Zhu, L., Sellers, K. F., Morris, D. S., and Shmueli, G. (2017), “Bridging the Gap: A Generalized Stochastic Process for Count Data,” The American Statistician, 71, 71–80.
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