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Journal articles on the topic 'Count data models'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

MacDonald, Iain L. "Models for count data." American Statistician 71, no. 2 (2017): 187–90. http://dx.doi.org/10.1080/00031305.2017.1291449.

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2

Winkelmann, Rainer, and Klaus F. Zimmermann. "Count data models for demographic data∗." Mathematical Population Studies 4, no. 3 (1994): 205–21. http://dx.doi.org/10.1080/08898489409525374.

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3

Kasyoki Muoka, Alexander. "Statistical Models for Count Data." Science Journal of Applied Mathematics and Statistics 4, no. 6 (2016): 256. http://dx.doi.org/10.11648/j.sjams.20160406.12.

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4

Winkelmann, R. "Count data models with selectivity." Econometric Reviews 17, no. 4 (1998): 339–59. http://dx.doi.org/10.1080/07474939808800422.

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5

Saha, Atanu, and Diansheng Dong. "Estimating Nested Count Data Models." Oxford Bulletin of Economics and Statistics 59, no. 3 (1997): 423–30. http://dx.doi.org/10.1111/1468-0084.00074.

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6

P. Singh, Brijesh. "Statistical Investigation of Household Size using Count Data Regression Models." Journal of Advanced Research in Applied Mathematics and Statistics 2, no. 1&2 (2017): 10–21. http://dx.doi.org/10.24321/2455.7021.201702.

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7

van Ophem, Hans. "Modeling Selectivity in Count-Data Models." Journal of Business & Economic Statistics 18, no. 4 (2000): 503. http://dx.doi.org/10.2307/1392231.

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8

Lee, Hyun Suk. "Random effects models for count data." Communications in Statistics - Theory and Methods 26, no. 8 (1997): 1893–904. http://dx.doi.org/10.1080/03610929708832020.

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9

Jang, Tae Youn. "Count Data Models for Trip Generation." Journal of Transportation Engineering 131, no. 6 (2005): 444–50. http://dx.doi.org/10.1061/(asce)0733-947x(2005)131:6(444).

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10

Ophem, Hans van. "Modeling Selectivity in Count-Data Models." Journal of Business & Economic Statistics 18, no. 4 (2000): 503–11. http://dx.doi.org/10.1080/07350015.2000.10524889.

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11

Gurmu, Shiferaw, and John Elder. "Flexible Bivariate Count Data Regression Models." Journal of Business & Economic Statistics 30, no. 2 (2012): 265–74. http://dx.doi.org/10.1080/07350015.2011.638816.

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12

Manly, Bryan F. J. "Bootstrapping with Models for Count Data." Journal of Biopharmaceutical Statistics 21, no. 6 (2011): 1164–76. http://dx.doi.org/10.1080/10543406.2011.607748.

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13

Gurmu, Shiferaw, and John Elder. "Generalized bivariate count data regression models." Economics Letters 68, no. 1 (2000): 31–36. http://dx.doi.org/10.1016/s0165-1765(00)00225-1.

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14

Gurmu, Shiferaw. "Generalized hurdle count data regression models." Economics Letters 58, no. 3 (1998): 263–68. http://dx.doi.org/10.1016/s0165-1765(97)00295-4.

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15

Wedel, Michel, Ulf Böckenholt, and Wagner A. Kamakura. "Factor models for multivariate count data." Journal of Multivariate Analysis 87, no. 2 (2003): 356–69. http://dx.doi.org/10.1016/s0047-259x(03)00020-4.

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16

Zhang, Yiwen, Hua Zhou, Jin Zhou, and Wei Sun. "Regression Models for Multivariate Count Data." Journal of Computational and Graphical Statistics 26, no. 1 (2017): 1–13. http://dx.doi.org/10.1080/10618600.2016.1154063.

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17

Peyhardi, Jean, Pierre Fernique, and Jean-Baptiste Durand. "Splitting models for multivariate count data." Journal of Multivariate Analysis 181 (January 2021): 104677. http://dx.doi.org/10.1016/j.jmva.2020.104677.

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18

Lee, Keunbaik, and Yongsung Joo. "Marginalized models for longitudinal count data." Computational Statistics & Data Analysis 136 (August 2019): 47–58. http://dx.doi.org/10.1016/j.csda.2019.01.001.

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19

Silveira de Andrade, Breno, Marinho G. Andrade, and Ricardo S. Ehlers. "Bayesian GARMA models for count data." Communications in Statistics: Case Studies, Data Analysis and Applications 1, no. 4 (2015): 192–205. http://dx.doi.org/10.1080/23737484.2016.1190307.

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20

Montesinos-López, Osval A., Abelardo Montesinos-López, Paulino Pérez-Rodríguez, et al. "Genomic Prediction Models for Count Data." Journal of Agricultural, Biological, and Environmental Statistics 20, no. 4 (2015): 533–54. http://dx.doi.org/10.1007/s13253-015-0223-4.

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21

Rodrigues-Motta, Mariana, Hildete P. Pinheiro, Eduardo G. Martins, Márcio S. Araújo, and Sérgio F. dos Reis. "Multivariate models for correlated count data." Journal of Applied Statistics 40, no. 7 (2013): 1586–96. http://dx.doi.org/10.1080/02664763.2013.789098.

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22

Nelson, Kerrie P., and Brian G. Leroux. "Statistical models for autocorrelated count data." Statistics in Medicine 25, no. 8 (2006): 1413–30. http://dx.doi.org/10.1002/sim.2274.

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23

Yeong, Nain Chi, and Andy Chang Guang-Hwa. "Modeling Trip Count Data with Excess Zeros for U.S. Saltwater Recreational Fishing." Journal of Economics and Business 2, no. 3 (2019): 773–85. https://doi.org/10.31014/aior.1992.02.03.126.

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Count data, such as recreational fishing trips taken by anglers, is increasingly common in recreational fishing demand analysis. Because of the non-negative integer nature of the recreational fishing trip data, some over-dispersion problems, and truncation of the data at zero trips, count data models are more appropriate for estimating the recreational fishing demand function. This study employed count data models to analyze U.S. saltwater recreational fishing trips with excess zeros, using a cross-sectional data extracted from the 2011 National Survey of Fishing, Hunting, and Wildlife Associa

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24

Vagelas, Ioannis. "Analysis of Over-Dispersed Count Data: Application to Obligate Parasite Pasteuria Penetrans." WSEAS TRANSACTIONS ON ENVIRONMENT AND DEVELOPMENT 18 (March 1, 2022): 333–39. http://dx.doi.org/10.37394/232015.2022.18.33.

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In this article we present with STATA regression models suitable for analyzing over-dispersed count outcomes. Specifically, the Negative Binomial regression can be an appropriate choice for modeling count variables, usually for over-dispersed count outcome variables. The common problem with count data with zeroes is that the empirical data often show more zeroes than would be expected under either Poisson or the Negative Binomial model. We concluded, this publications showcases that Zero-inflated models can be used to model count data that has excessive zero counts.

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25

Alam, Morshed, Naim Al Mahi, and Munni Begum. "Zero-Inflated Models for RNA-Seq Count Data." Journal of Biomedical Analytics 1, no. 2 (2018): 55–70. http://dx.doi.org/10.30577/jba.2018.v1n2.23.

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One of the main objectives of many biological studies is to explore differential gene expression profiles between samples. Genes are referred to as differentially expressed (DE) if the read counts change across treatments or conditions systematically. Poisson and negative binomial (NB) regressions are widely used methods for non-over-dispersed (NOD) and over-dispersed (OD) count data respectively. However, in the presence of excessive number of zeros, these methods need adjustments. In this paper, we consider a zero-inflated Poisson mixed effects model (ZIPMM) and zero-inflated negative binomi

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26

Habeeb Hashim, Luay, and Ahmad Naeem Flaih. "Modeling the Rainfall Count data Using Some Zero Type models with application." Journal of Al-Qadisiyah for computer science and mathematics 11, no. 2 (2019): 14–27. http://dx.doi.org/10.29304/jqcm.2019.11.2.554.

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Count data, including zero counts arise in a wide variety of application, hence models for counts have become widely popular in many fields. In the statistics field, one may define the count data as that type of observation which takes only the non-negative integers value. Sometimes researchers may Counts more zeros than the expected. Excess zero can be defined as Zero-Inflation. Data with abundant zeros are especially popular in health, marketing, finance, econometric, ecology, statistics quality control, geographical, and environmental fields when counting the occurrence of certain behaviora

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27

Tuydes-Yaman, Hediye, Oruc Altintasi, and Nuri Sendil. "Better estimation of origin–destination matrix using automated intersection movement count data." Canadian Journal of Civil Engineering 42, no. 7 (2015): 490–502. http://dx.doi.org/10.1139/cjce-2014-0555.

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Intersection movements carry more disaggregate information about origin–destination (O–D) flows than link counts in a traffic network. In this paper, a mathematical formulation is presented for O–D matrix estimation using intersection counts, which is based on an existing linear programming model employing link counts. The proposed model estimates static O–D flows for uncongested networks assuming no a priori information on the O–D matrix. Both models were tested in two hypothetical networks previously used in O–D matrix studies to monitor their performances assuming various numbers of count l

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28

Hellerstein, Daniel, and Robert Mendelsohn. "A Theoretical Foundation for Count Data Models." American Journal of Agricultural Economics 75, no. 3 (1993): 604–11. http://dx.doi.org/10.2307/1243567.

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29

Blundell, Richard, Rachel Griffith, and John Van Reenen. "Dynamic Count Data Models of Technological Innovation." Economic Journal 105, no. 429 (1995): 333. http://dx.doi.org/10.2307/2235494.

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30

Ali, Zamir, Yu Feng, Ali Choo, and Munsir Ali. "Statistical Diagnostics of Models for Count Data." International Journal of Mathematics Trends and Technology 63, no. 1 (2018): 9–15. http://dx.doi.org/10.14445/22315373/ijmtt-v63p502.

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31

BOES, STEFAN. "Count Data Models with Correlated Unobserved Heterogeneity." Scandinavian Journal of Statistics 37, no. 3 (2010): 382–402. http://dx.doi.org/10.1111/j.1467-9469.2010.00689.x.

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32

De Oliveira, Victor. "Hierarchical Poisson models for spatial count data." Journal of Multivariate Analysis 122 (November 2013): 393–408. http://dx.doi.org/10.1016/j.jmva.2013.08.015.

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33

Gouriéroux, Christian, and Thierry Magnac. "Duration, transition and count data models Introduction." Journal of Econometrics 79, no. 2 (1997): 195–99. http://dx.doi.org/10.1016/s0304-4076(97)00019-5.

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34

Greene, William. "Models for count data with endogenous participation." Empirical Economics 36, no. 1 (2008): 133–73. http://dx.doi.org/10.1007/s00181-008-0190-5.

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35

Böhning, Dankmar, Ekkehart Dietz, Ronny Kuhnert, and Dieter Schön. "Mixture models for capture-recapture count data." Statistical Methods & Applications 14, no. 1 (2005): 29–43. http://dx.doi.org/10.1007/bf02511573.

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36

Brännäs, Kurt, and Gunnar Rosenqvist. "Semiparametric estimation of heterogeneous count data models." European Journal of Operational Research 76, no. 2 (1994): 247–58. http://dx.doi.org/10.1016/0377-2217(94)90105-8.

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37

Hasebe, Takuya. "Treatment effect estimators for count data models." Health Economics 27, no. 11 (2018): 1868–73. http://dx.doi.org/10.1002/hec.3790.

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38

Burnett, R. T., J. Shedden, and D. Krewski. "Nonlinear regression models for correlated count data." Environmetrics 3, no. 2 (1992): 211–22. http://dx.doi.org/10.1002/env.3170030206.

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39

Oketch, Godrick, and Filiz Karaman. "Maximum likelihood function for fuzzy count data models (using heaped data as fuzzy)." Journal of Intelligent & Fuzzy Systems 39, no. 5 (2020): 6891–901. http://dx.doi.org/10.3233/jifs-192094.

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Count data models are based on definite counts of events as dependent variables. But there are practical situations in which these counts may fail to be specific and are seen as imprecise. In this paper, an assumption that heaped data points are fuzzy is used as a way of identifying counts that are not definite since heaping can result from imprecisely reported counts. Because it is practically unlikely to report all counts in an entire dataset as imprecise, this paper proposes a likelihood function that not only considers both precise and imprecisely reported counts but also incorporates α -

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40

Habeeb Hashim, Luay, and Ahmad Naeem Flaih. "Selecting the best model to fit the Rainfall Count data Using Some Zero Type models with application." Journal of Al-Qadisiyah for computer science and mathematics 11, no. 2 (2019): 28–41. http://dx.doi.org/10.29304/jqcm.2019.11.2.555.

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        28         Counts data models cope with the response variable counts, where the number of times that a certain event occurs in a fixed point is called count data, its observations consists of non-negative integers values {0,1,2,…}. Because of the nature of count data, the response variables are usually considered doing not follow normal distribution. Therefore, linear regression is not an appropriate method to analysis count data due to the skewed distribution. Hence, using linear regr

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41

Faris, Richard, and Neil Paton. "121 Statistical Analysis Method Counts for Sow Count Data Responses." Journal of Animal Science 99, Supplement_1 (2021): 56. http://dx.doi.org/10.1093/jas/skab054.094.

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Abstract Several statistical analysis methods are typically employed to analyze sow reproductive count data. The research objective was to compare analysis methods of pig birth counts to determine their robustness in identifying simulated treatment differences. Counts of stillborn (SB), born alive (BA) and sow parity differences were simulated using descriptive statistics from a sow farm. Different scenarios were tested: 1) Effect of a 0.5, 1.0, 1.5, and 2.0 percentage point change in treatment difference in SB and BA and, 2) Replicates of 20 to 200 experimental units (EU) in increments of 20

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42

Noh, Maengseok, and Youngjo Lee. "Extended negative binomial hurdle models." Statistical Methods in Medical Research 28, no. 5 (2018): 1540–51. http://dx.doi.org/10.1177/0962280218766567.

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Poisson models are widely used for statistical inference on count data. However, zero-inflation or zero-deflation with either overdispersion or underdispersion could occur. Currently, there is no available model for count data, that allows excessive occurrence of zeros along with underdispersion in non-zero counts, even though there have been reported necessity of such models. Furthermore, given an excessive zero rate, we need a model that allows a larger degree of overdispersion than existing models. In this paper, we use a random-effect model to produce a general statistical model for accomm

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43

Shapovalova, Yuliya, Nalan Baştürk, and Michael Eichler. "Multivariate Count Data Models for Time Series Forecasting." Entropy 23, no. 6 (2021): 718. http://dx.doi.org/10.3390/e23060718.

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Count data appears in many research fields and exhibits certain features that make modeling difficult. Most popular approaches to modeling count data can be classified into observation and parameter-driven models. In this paper, we review two models from these classes: the log-linear multivariate conditional intensity model (also referred to as an integer-valued generalized autoregressive conditional heteroskedastic model) and the non-linear state-space model for count data. We compare these models in terms of forecasting performance on simulated data and two real datasets. In simulations, we

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44

Fé, Eduardo, and Richard Hofler. "sfcount: Command for count-data stochastic frontiers and underreported and overreported counts." Stata Journal: Promoting communications on statistics and Stata 20, no. 3 (2020): 532–47. http://dx.doi.org/10.1177/1536867x20953566.

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In this article, we introduce a new command, sfcount, to fit count-data stochastic frontier models. Although originally designed to estimate production and production-cost functions, this new command can be used to estimate mean regression functions when count data are suspected to be underreported or over-reported.

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45

Y. Algamal, Zakariya. "Re-sampling Techniques in Count Data Regression Models." IRAQI JOURNAL OF STATISTICAL SCIENCES 12, no. 22 (2012): 15–25. http://dx.doi.org/10.33899/iqjoss.2012.67727.

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46

Winkelmann, Rainer. "Duration Dependence and Dispersion in Count-Data Models." Journal of Business & Economic Statistics 13, no. 4 (1995): 467. http://dx.doi.org/10.2307/1392392.

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47

Cepeda-Cuervo, Edilberto, Michel Córdoba, and Vicente Núñez-Antón. "Conditional overdispersed models: Application to count area data." Statistical Methods in Medical Research 27, no. 10 (2017): 2964–88. http://dx.doi.org/10.1177/0962280217689968.

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This paper proposes alternative models for the analysis of count data featuring a given spatial structure, which corresponds to geographical areas. We assume that the overdispersion data structure partially results from the existing and well justified spatial correlation between geographical adjacent regions, so an extension of existing overdispersion models that include spatial neighborhood structures within a Bayesian framework is proposed. These models allow practitioners to quantify the association explained by the considered neighborhood structures and the one modelled by additional facto

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48

Winkelmann, Rainer. "Duration Dependence and Dispersion in Count-Data Models." Journal of Business & Economic Statistics 13, no. 4 (1995): 467–74. http://dx.doi.org/10.1080/07350015.1995.10524620.

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49

Leckie, George, William J. Browne, Harvey Goldstein, Juan Merlo, and Peter C. Austin. "Partitioning variation in multilevel models for count data." Psychological Methods 25, no. 6 (2020): 787–801. http://dx.doi.org/10.1037/met0000265.

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50

Todem, David, KyungMann Kim, and Wei-Wen Hsu. "Marginal mean models for zero-inflated count data." Biometrics 72, no. 3 (2016): 986–94. http://dx.doi.org/10.1111/biom.12492.

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