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Journal articles on the topic 'Count Data'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Zhang, Ying, and Jon A. Wellner. "count data." Annals of Statistics 28, no. 3 (2000): 779–814. http://dx.doi.org/10.1214/aos/1015951998.

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Codding, Brian F., and Simon C. Brewer. "Regression with Archaeological Count Data." Advances in Archaeological Practice 12, no. 2 (2024): 163–72. http://dx.doi.org/10.1017/aap.2024.7.

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AbstractArchaeological data often come in the form of counts. Understanding why counts of artifacts, subsistence remains, or features vary across time and space is central to archaeological inquiry. A central statistical method to model such variation is through regression, yet despite sophisticated advances in computational approaches to archaeology, practitioners do not have a standard approach for building, validating, or interpreting the results of count regression. Drawing on advances in ecology, we outline a framework for evaluating regressions with archaeological count data that include

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3

Lennon, Z. J., M. S. Stanbury, D. M. Suckling, and A. M. El-Sayed. "Making data count." New Zealand Plant Protection 68 (January 8, 2015): 450. http://dx.doi.org/10.30843/nzpp.2015.68.5862.

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The plethora of hightech data collection methods available to the modern biologist presents opportunities but also challenges While tools such as automated camera traps can vastly increase the efficiency of data collection and storage allowing for the collection of massive amounts of photographic data extracting quantitative data from these images manually can be a timeconsuming and tedious task Prototype counting software currently in development at Plant Food Research that can be used to automatically process large numbers of photographs or scanned images is presented The user trains the sof

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4

Puig, Pedro, and Jordi Valero. "Count Data Distributions." Journal of the American Statistical Association 101, no. 473 (2006): 332–40. http://dx.doi.org/10.1198/016214505000000718.

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5

Cohn, Jonathan B., Zack Liu, and Malcolm I. Wardlaw. "Count (and count-like) data in finance." Journal of Financial Economics 146, no. 2 (2022): 529–51. http://dx.doi.org/10.1016/j.jfineco.2022.08.004.

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6

Holmes, John A., George M. W. Cronkite, Hermann J. Enzenhofer, and Timothy J. Mulligan. "Accuracy and precision of fish-count data from a “dual-frequency identification sonar” (DIDSON) imaging system." ICES Journal of Marine Science 63, no. 3 (2006): 543–55. http://dx.doi.org/10.1016/j.icesjms.2005.08.015.

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Abstract The reliability of sockeye-salmon (Oncorhynchus nerka) count data collected by a dual-frequency, identification sonar (DIDSON) system is evaluated on the basis of comparisons with visual counts of unconstrained migrating salmon and visual counts of salmon constrained to passing through an enumeration fence. Regressions fitted to the DIDSON count data and the visual count data from the enumeration fence were statistically indistinguishable from a line with slope = 1.0 passing through the origin, which we interpret as agreement in both counts. In contrast, the regressions fitted to the

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Segal, Jodi B., and Alison R. Molterno. "Platelet Counts Vary by Ethnicity, Sex, and Age: Analysis of NHANES III Data." Blood 104, no. 11 (2004): 3937. http://dx.doi.org/10.1182/blood.v104.11.3937.3937.

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Abstract Background: The possibility of variation in platelet count by age and genetic background has not been examined. Identification of subpopulations with elevated platelet counts, within what is traditionally considered a normal range, may identify a group with excessive morbidity or mortality. Furthermore, platelet count differences may suggest populations in which genetic polymorphisms in regulatory proteins such as the thrombopoietin receptor influence platelet production. We hypothesized that there were differences in platelet count by ethnicity, sex and age not explained by environme

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8

Nassif, Hani, Kaan Ozbay, and Sami Demiroluk. "Mapping of truck traffic in New Jersey using weigh-in-motion data." IET Intelligent Transport Systems 12, no. 9 (2018): 1053–61. https://doi.org/10.1049/iet-its.2018.0055.

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This study presents an innovative hierarchical Bayesian model for mapping of county level truck traffic in New Jersey. First, the model is estimated using truck counts. Then, using overweight truck counts from weigh-in-motion data as the response variable, the model is re-estimated. The goal in using the overweight trucks in the spatial model is to demonstrate the importance of representing their spatial variation due to their impact on the life of the roadway network elements. Finally, truck count maps are developed based on modelling results to visualise the effects of spatial covariates. Th

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9

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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10

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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11

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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12

Beck, E. M., and Stewart E. Tolnay. "Analyzing Historical Count Data." Historical Methods: A Journal of Quantitative and Interdisciplinary History 28, no. 3 (1995): 125–31. http://dx.doi.org/10.1080/01615440.1995.9956360.

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13

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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14

Cummings, Tammy H., James W. Hardin, Alexander C. McLain, James R. Hussey, Kevin J. Bennett, and Gina M. Wingood. "Modeling Heaped Count Data." Stata Journal: Promoting communications on statistics and Stata 15, no. 2 (2015): 457–79. http://dx.doi.org/10.1177/1536867x1501500207.

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15

Mustillo, Sarah, Lawrence R. Landerman, and Kenneth C. Land. "Modeling Longitudinal Count Data." Sociological Methods & Research 41, no. 3 (2012): 467–87. http://dx.doi.org/10.1177/0049124112452397.

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16

The Lancet. "Making cancer data count." Lancet 383, no. 9933 (2014): 1946. http://dx.doi.org/10.1016/s0140-6736(14)60939-9.

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17

Yannaros, Nikos. "Analyzing Incomplete Count Data." Statistician 42, no. 2 (1993): 181. http://dx.doi.org/10.2307/2348982.

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18

Madsen, L., and D. Dalthorp. "Simulating correlated count data." Environmental and Ecological Statistics 14, no. 2 (2007): 129–48. http://dx.doi.org/10.1007/s10651-007-0008-1.

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19

Chandhiramowuli, Srravya, Alex S. Taylor, Sara Heitlinger, and Ding Wang. "Making Data Work Count." Proceedings of the ACM on Human-Computer Interaction 8, CSCW1 (2024): 1–26. http://dx.doi.org/10.1145/3637367.

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In this paper, we examine the work of data annotation. Specifically, we focus on the role of counting or quantification in organising annotation work. Based on an ethnographic study of data annotation in two outsourcing centres in India, we observe that counting practices and its associated logics are an integral part of day-to-day annotation activities. In particular, we call attention to the presumption of total countability observed in annotation - the notion that everything, from tasks, datasets and deliverables, to workers, work time, quality and performance, can be managed by applying th

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20

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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21

Cox, James L., Joseph F. Heyse, and John W. Tukey. "Efficacy Estimates from Parasite Count Data That Include Zero Counts." Experimental Parasitology 96, no. 1 (2000): 1–8. http://dx.doi.org/10.1006/expr.2000.4550.

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22

Kruijer, Willem, Alfred Stein, Willem Schaafsma, and Sanne Heijting. "Analyzing spatial count data, with an application to weed counts." Environmental and Ecological Statistics 14, no. 4 (2007): 399–410. http://dx.doi.org/10.1007/s10651-007-0027-y.

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23

Cheng, Jing, Nancy F. Cheng, Zijian Guo, Steven Gregorich, Amid I. Ismail, and Stuart A. Gansky. "Mediation analysis for count and zero-inflated count data." Statistical Methods in Medical Research 27, no. 9 (2017): 2756–74. http://dx.doi.org/10.1177/0962280216686131.

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Different conventional and causal approaches have been proposed for mediation analysis to better understand the mechanism of a treatment. Count and zero-inflated count data occur in biomedicine, economics, and social sciences. This paper considers mediation analysis for count and zero-inflated count data under the potential outcome framework with nonlinear models. When there are post-treatment confounders which are independent of, or affected by, the treatment, we first define the direct, indirect, and total effects of our interest and then discuss various conditions under which the effects of

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24

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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25

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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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

Alexander, Christina J., Sarah L. Manske, W. Brent Edwards, and Leigh Gabel. "Adapting the Intensity Gradient for Use with Count-Based Accelerometry Data in Children and Adolescents." Sensors 24, no. 10 (2024): 3019. http://dx.doi.org/10.3390/s24103019.

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The intensity gradient is a new cutpoint-free metric that was developed to quantify physical activity (PA) measured using accelerometers. This metric was developed for use with the ENMO (Euclidean norm minus one) metric, derived from raw acceleration data, and has not been validated for use with count-based accelerometer data. In this study, we determined whether the intensity gradient could be reproduced using count-based accelerometer data. Twenty participants (aged 7–22 years) wore a GT1M, an ActiGraph (count-based), and a GT9X, ActiGraph (raw accelerations) accelerometer during both in-lab

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28

Toth, Christopher, Wonho Suh, Vetri Elango, et al. "Tablet-Based Traffic Counting Application Designed to Minimize Human Error." Transportation Research Record: Journal of the Transportation Research Board 2339, no. 1 (2013): 39–46. http://dx.doi.org/10.3141/2339-05.

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Basic traffic counts are among the key elements in transportation planning and forecasting. As emerging data collection technologies proliferate, the availability of traffic count data will expand by orders of magnitude. However, availability of data does not always guarantee data accuracy, and it is essential that observed data are compared with ground truth data. Little research or guidance is available that ensures the quality of ground truth data with which the count results of automated technologies can be compared. To address the issue of ground truth data based on manual counts, a manua

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29

Sakthivel, K. M., and C. S. Rajitha. "Model Selection for Count Data with Excess Number of Zero Counts." American Journal of Applied Mathematics and Statistics 7, no. 1 (2019): 43–51. http://dx.doi.org/10.12691/ajams-7-1-7.

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30

Volpe, Richard J., Timothy A. Park, Fengxia Dong, and Helen H. Jensen. "Somatic cell counts in dairy marketing: quantile regression for count data." European Review of Agricultural Economics 43, no. 2 (2015): 331–58. http://dx.doi.org/10.1093/erae/jbv021.

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31

Safari-Katesari, Hadi, S. Yaser Samadi, and Samira Zaroudi. "Modelling count data via copulas." Statistics 54, no. 6 (2020): 1329–55. http://dx.doi.org/10.1080/02331888.2020.1867140.

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32

Snell, Joyce, and J. K. Lindsey. "Modelling Frequency and Count Data." Journal of the Royal Statistical Society. Series A (Statistics in Society) 159, no. 1 (1996): 188. http://dx.doi.org/10.2307/2983489.

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33

Lindsey, J. K. "Modelling Frequency and Count Data." Biometrics 54, no. 1 (1998): 397. http://dx.doi.org/10.2307/2534035.

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34

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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35

Strawderman, Robert L., A. Colin Cameron, and Pravin K. Trivedi. "Regression Analysis of Count Data." Journal of the American Statistical Association 94, no. 447 (1999): 984. http://dx.doi.org/10.2307/2670019.

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36

Brännäs, Kurt, and Per Johansson. "Time series count data regression." Communications in Statistics - Theory and Methods 23, no. 10 (1994): 2907–25. http://dx.doi.org/10.1080/03610929408831424.

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37

Rilstone, Paul. "Econometric Analysis of Count Data." Journal of the American Statistical Association 97, no. 457 (2002): 361–62. http://dx.doi.org/10.1198/jasa.2002.s458.

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38

Friedl, Herwig. "Econometric Analysis of Count Data." Journal of the American Statistical Association 97, no. 457 (2002): 362. http://dx.doi.org/10.1198/jasa.2002.s459.

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39

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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40

R. Conaway, Mark. "Regression Analysis of Count Data." Journal of Statistical Planning and Inference 100, no. 1 (2002): 85–86. http://dx.doi.org/10.1016/s0378-3758(01)00104-5.

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41

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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42

Owen, Willis L. "Modelling Frequency and Count Data." Technometrics 38, no. 3 (1996): 290–91. http://dx.doi.org/10.1080/00401706.1996.10484515.

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43

Peterson, John J. "Regression Analysis of Count Data." Technometrics 41, no. 4 (1999): 371. http://dx.doi.org/10.1080/00401706.1999.10485941.

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44

Wei, Wei, and Leonhard Held. "Calibration tests for count data." TEST 23, no. 4 (2014): 787–805. http://dx.doi.org/10.1007/s11749-014-0380-8.

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45

Carota, C. "Semiparametric regression for count data." Biometrika 89, no. 2 (2002): 265–81. http://dx.doi.org/10.1093/biomet/89.2.265.

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46

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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47

Ledru, E., S. Diagbouga, N. Meda, et al. "A proposal for basic management of HIV disease in West Africa: use of clinical staging and haemogram data." International Journal of STD & AIDS 9, no. 8 (1998): 463–70. http://dx.doi.org/10.1258/0956462981922638.

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Summary: Our objective was to propose a strategy to screen HIV infected African people for biological immunodeficiency easily. In a cross sectional study, we analysed the patterns of diseases and of CD4 counts among 266 HIV infected adults. Peripheral facial paralysis and chronic cutaneo mucous diseases were the earlier B stage diseases. Pulmonary tuberculosis was close to B stage diseases, and chronic diarrhoea was borderline between B and C stages. Cachexia was the most frequent C stage symptom 47.8 . Ninety per cent of CDC C stage people had CD4 counts below 350 mul, whereas only 75 had CD4

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48

Lopez-Kleine, Liliana, and Cristian Andres Gonzalez-Prieto. "Graphic Model-based Gene Regulatory Network Reconstruction using RNA Sequencing Count Data." JOURNAL OF ADVANCES IN BIOTECHNOLOGY 8 (May 30, 2019): 1078–85. http://dx.doi.org/10.24297/jbt.v8i0.8298.

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Interactions between genes, such as regulations are best represented by gene regulatory networks (GRN). These are often constructed based on gene expression data. Few methods for the construction of GRN exist for RNA sequencing count data. One of the most used methods for microarray data is based on graphical Gaussian networks. Considering that count data have different distributions, a method assuming RNA sequencing counts distribute Poisson has been proposed recently. Nevertheless, it has been argued that the most likely distribution of RNA sequencing counts is not Poisson due to overdispers

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49

Suh, Won Ho, James Anderson, Angshuman Guin, and Michael Hunter. "Evaluation of Traffic Data Collection Method." Applied Mechanics and Materials 764-765 (May 2015): 905–9. http://dx.doi.org/10.4028/www.scientific.net/amm.764-765.905.

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Traffic counts are one of the fundamental data sources for the Highway Performance Monitoring System (HPMS). Automatic Traffic Recorders (ATRs) are used to provide continuous traffic count coverage at selected locations to estimate annual average daily traffic (AADT). However, ATR data is often unavailable. This paper investigated the feasibility of using Video Detection System (VDS) technology when ATR data is not available. An Android Tablet-based manual traffic counting application was developed to acquire manual count based ground truth data. The performance of VDS was evaluated under vari

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50

Ceronio, A. D., and J. Haarhoff. "Dealing with large particle counting data sets." Water Supply 2, no. 5-6 (2002): 35–40. http://dx.doi.org/10.2166/ws.2002.0147.

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A particle count survey of 21 South African water treatment plants over a period of 15 months presented the authors with challenges similar to those experienced by other workers in the field. The amount of data generated was staggering and had to be dealt with in an orderly and structured way to derive the maximum benefit from the survey. Furthermore, the number of significant data points involved if the entire count is to be taken into consideration complicated interpretation of particle counting data. This led to the application of several data reduction techniques to reduce the number of si

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