Journal articles: 'Count data modelling'

1

Safari-Katesari, Hadi, S. Yaser Samadi, and Samira Zaroudi. "Modelling count data via copulas." Statistics 54, no. 6 (November 1, 2020): 1329–55. http://dx.doi.org/10.1080/02331888.2020.1867140.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

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

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

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

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Saei, Ayoub, and Ray Chambers. "MODELLING TRUNCATED AND CLUSTERED COUNT DATA." Australian New Zealand Journal of Statistics 47, no. 3 (September 2005): 339–49. http://dx.doi.org/10.1111/j.1467-842x.2005.00399.x.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Shaddick, G., L. L. Choo, and S. G. Walker. "Modelling correlated count data with covariates." Journal of Statistical Computation and Simulation 77, no. 11 (November 2007): 945–54. http://dx.doi.org/10.1080/10629360600851974.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Ullah, Shahid, Caroline F. Finch, and Lesley Day. "Statistical modelling for falls count data." Accident Analysis & Prevention 42, no. 2 (March 2010): 384–92. http://dx.doi.org/10.1016/j.aap.2009.08.018.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Wearden, John H. "Modelling Chronometric Counting." Timing & Time Perception 4, no. 3 (October 20, 2016): 271–98. http://dx.doi.org/10.1163/22134468-00002070.

Full text

Abstract:

Participants performed on a temporal generalization task with standard durations being either 4 or 8 s, and comparison durations ranging from 2.5 to 5.5, or 5 to 11 s. They were required to count during all stimulus presentations, and counts were recorded as spacebar presses. Generalization gradients around both standard values peaked at the standard, but the gradient from the 8-s condition was steeper. Measured counts had low variance, both within trials and between trials, and a start process, which was different from the counting sequence, could also be identified in data. A computer model assuming that a comparison duration was identified as the standard when the count value for the comparison was one that had previously occurred for a standard fitted the temporal generalization gradients well. The model was also applied to some published data on temporal reproduction with counting, and generally fitted data adequately. The model makes a distinction between the variance of the count unit from one trial to another, and the counts within the trial, and this distinction was related to the overall variance of behaviours resulting from counting, and the ways in which variability of timing measures change with the duration timed.

APA, Harvard, Vancouver, ISO, and other styles

9

Reboussin, Beth. "Book Review: Modelling frequency and count data." Statistical Methods in Medical Research 7, no. 3 (June 1998): 321. http://dx.doi.org/10.1177/096228029800700309.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Chee, Chew-Seng. "Modelling of count data using nonparametric mixtures." AStA Advances in Statistical Analysis 100, no. 3 (September 18, 2015): 239–57. http://dx.doi.org/10.1007/s10182-015-0255-7.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Dvorzak, Michaela, and Helga Wagner. "Sparse Bayesian modelling of underreported count data." Statistical Modelling: An International Journal 16, no. 1 (June 18, 2015): 24–46. http://dx.doi.org/10.1177/1471082x15588398.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

Trocóniz, Iñaki F., Elodie L. Plan, Raymond Miller, and Mats O. Karlsson. "Modelling overdispersion and Markovian features in count data." Journal of Pharmacokinetics and Pharmacodynamics 36, no. 5 (October 2009): 461–77. http://dx.doi.org/10.1007/s10928-009-9131-y.

Full text

APA, Harvard, Vancouver, ISO, and other styles

13

Barry, Simon C., and A. H. Welsh. "Generalized additive modelling and zero inflated count data." Ecological Modelling 157, no. 2-3 (November 2002): 179–88. http://dx.doi.org/10.1016/s0304-3800(02)00194-1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

14

Leonte, Daniela, and David J. Nott. "Bayesian Spatial Modelling of Gamma Ray Count Data." Mathematical Geology 38, no. 2 (February 2006): 135–54. http://dx.doi.org/10.1007/s11004-005-9008-6.

Full text

APA, Harvard, Vancouver, ISO, and other styles

15

Clark, Austina S. S., Erin E. Zydervelt, and Stephen R. Wing. "Modelling count and growth data with many zeros." Journal of Experimental Marine Biology and Ecology 365, no. 2 (October 2008): 86–95. http://dx.doi.org/10.1016/j.jembe.2008.07.043.

Full text

APA, Harvard, Vancouver, ISO, and other styles

16

Gschlößl, Susanne, and Claudia Czado. "Modelling count data with overdispersion and spatial effects." Statistical Papers 49, no. 3 (November 17, 2006): 531–52. http://dx.doi.org/10.1007/s00362-006-0031-6.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

Bodhisuwan, Winai, and Sirinapa Aryuyuen. "The Poisson-Transmuted Janardan Distribution for Modelling Count Data." Trends in Sciences 19, no. 5 (February 25, 2022): 2898. http://dx.doi.org/10.48048/tis.2022.2898.

Full text

Abstract:

In this paper, we introduce a new mixed Poisson distribution, called the Poisson-transmuted Janardan distribution. The Poisson-Janardan and Poisson-Lindley distributions are sub-model of the proposed distribution. Some mathematical properties of the proposed distribution, including the moments, moment generating function, probability generating function and generation of a Poisson-transmuted Janardan random variable, are presented. The parameter estimation is discussed based on the method of moments and the maximum likelihood estimation. In addition, we illustrated the application of the proposed distribution by fitting with 4 real data sets and comparing it with some other distributions based on the Kolmogorov-Smirnov test for criteria.

APA, Harvard, Vancouver, ISO, and other styles

18

Dobbie, Melissa J., and A. H. Welsh. "Theory & Methods: Modelling Correlated Zero‐inflated Count Data." Australian & New Zealand Journal of Statistics 43, no. 4 (December 2001): 431–44. http://dx.doi.org/10.1111/1467-842x.00191.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

Khan, A., S. Ullah, and J. Nitz. "Statistical modelling of falls count data with excess zeros." Injury Prevention 17, no. 4 (June 8, 2011): 266–70. http://dx.doi.org/10.1136/ip.2011.031740.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Winkelmann, Rainer, and Klaus F. Zimmermann. "RECENT DEVELOPMENTS IN COUNT DATA MODELLING: THEORY AND APPLICATION." Journal of Economic Surveys 9, no. 1 (March 1995): 1–24. http://dx.doi.org/10.1111/j.1467-6419.1995.tb00108.x.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

Chiogna, Monica, and Carlo Gaetan. "An interchangeable approach for modelling spatio-temporal count data." Environmetrics 21, no. 7-8 (November 2010): 849–67. http://dx.doi.org/10.1002/env.1078.

Full text

APA, Harvard, Vancouver, ISO, and other styles

22

Faddy, M. J. "Extended Poisson Process Modelling and Analysis of Count Data." Biometrical Journal 39, no. 4 (1997): 431–40. http://dx.doi.org/10.1002/bimj.4710390405.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

Asendorf, Thomas, Robin Henderson, Heinz Schmidli, and Tim Friede. "Modelling and sample size reestimation for longitudinal count data with incomplete follow up." Statistical Methods in Medical Research 28, no. 1 (June 21, 2017): 117–33. http://dx.doi.org/10.1177/0962280217715664.

Full text

Abstract:

We consider modelling and inference as well as sample size estimation and reestimation for clinical trials with longitudinal count data as outcomes. Our approach is general but is rooted in design and analysis of multiple sclerosis trials where lesion counts obtained by magnetic resonance imaging are important endpoints. We adopt a binomial thinning model that allows for correlated counts with marginal Poisson or negative binomial distributions. Methods for sample size planning and blinded sample size reestimation for randomised controlled clinical trials with such outcomes are developed. The models and approaches are applicable to data with incomplete observations. A simulation study is conducted to assess the effectiveness of sample size estimation and blinded sample size reestimation methods. Sample sizes attained through these procedures are shown to maintain the desired study power without inflating the type I error. Data from a recent trial in patients with secondary progressive multiple sclerosis illustrate the modelling approach.

APA, Harvard, Vancouver, ISO, and other styles

24

Park, Myung Hyun, and Joseph H. T. Kim. "Modelling Healthcare Demand Count Data with Excessive Zeros and Overdispersion." Global Economic Review 50, no. 4 (October 2, 2021): 358–81. http://dx.doi.org/10.1080/1226508x.2021.2004907.

Full text

APA, Harvard, Vancouver, ISO, and other styles

25

Faddy, M. J. "Modelling and analysis of count data using a renewal process." Statistics & Probability Letters 31, no. 2 (December 1996): 129–32. http://dx.doi.org/10.1016/s0167-7152(96)00023-5.

Full text

APA, Harvard, Vancouver, ISO, and other styles

26

Brida, Juan Gabriel, Juan Sebastián Pereyra, and Raffaele Scuderi. "Repeat tourism in Uruguay: modelling truncated distributions of count data." Quality & Quantity 48, no. 1 (September 30, 2012): 475–91. http://dx.doi.org/10.1007/s11135-012-9782-4.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

Nikoloulopoulos, Aristidis K., and Dimitris Karlis. "Modeling Multivariate Count Data Using Copulas." Communications in Statistics - Simulation and Computation 39, no. 1 (December 8, 2009): 172–87. http://dx.doi.org/10.1080/03610910903391262.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

Frydrych, David. "Rights Modelling." Canadian Journal of Law & Jurisprudence 30, no. 1 (February 2017): 125–57. http://dx.doi.org/10.1017/cjlj.2017.6.

Full text

Abstract:

This paper has four aims. First it distinguishes two kinds of philosophical accounts of the ‘formal’ features of rights: models and theories. Models outline the ‘conceptually basic’ types of rights (if indeed a given model deems there to be more than one), their differences, and their relationships with duties, liabilities, etc. Theories of rights posit a supposed ultimate purpose for all rights and provide criteria for determining what counts as ‘a right’ in the first place. Second, the paper argues that Monistic rights models (ones positing only a single basic type of right) are under-inclusive. They wrongly exclude and cannot explain relevant data, i.e., ordinary and legal linguistic practices. The third aim is to show that certain Pluralistic models are over-inclusive in terms of what they count as ‘rights’. Fourth, the paper begins to touch upon, but does not provide, criteria for determining what counts as ‘a right’. Two candidate factors will be addressed.

APA, Harvard, Vancouver, ISO, and other styles

29

Al-Balushi, Zainab Mohammed Darwish, and M. Mazharul Islam. "Geometric Regression for Modelling Count Data on the Time-to-First Antenatal Care Visit." Journal of Statistics: Advances in Theory and Applications 23, no. 1 (April 10, 2020): 35–57. http://dx.doi.org/10.18642/jsata_7100122148.

Full text

Abstract:

Geometric distribution belongs to the family of discrete distribution that deals with the count of trail needed for first occurrence or success of any event. However, little attention has been paid in applying the GLM for the geometric distribution, which has a very simple form for its probability mass function with a single parameter. In this study, an attempt has been made to introduce geometric regression for modelling the count data. We have illustrated the suitability of the geometric regression model for analyzing the count data on time to first antenatal care visit that displayed under-dispersion, and the results were compared with Poisson and negative binomial regressions. We conclude that the geometric regression model may provide a flexible model for fitting count data sets which may present over-dispersion or under-dispersion, and the model may serve as an alternative model to the very familiar Poisson and negative binomial models for modelling count data.

APA, Harvard, Vancouver, ISO, and other styles

30

Jung, Robert C., and Stephanie Glaser. "Modelling and Diagnostics of Spatially Autocorrelated Counts." Econometrics 10, no. 3 (September 13, 2022): 31. http://dx.doi.org/10.3390/econometrics10030031.

Full text

Abstract:

This paper proposes a new spatial lag regression model which addresses global spatial autocorrelation arising from cross-sectional dependence between counts. Our approach offers an intuitive interpretation of the spatial correlation parameter as a measurement of the impact of neighbouring observations on the conditional expectation of the counts. It allows for flexible likelihood-based inference based on different distributional assumptions using standard numerical procedures. In addition, we advocate the use of data-coherent diagnostic tools in spatial count regression models. The application revisits a data set on the location choice of single unit start-up firms in the manufacturing industry in the US.

APA, Harvard, Vancouver, ISO, and other styles

31

O’Hara, Robert B., and D. Johan Kotze. "Do not log-transform count data." Methods in Ecology and Evolution 1, no. 2 (March 24, 2010): 118–22. http://dx.doi.org/10.1111/j.2041-210x.2010.00021.x.

Full text

APA, Harvard, Vancouver, ISO, and other styles

32

George, Sebastian, and Ambily Jose. "Generalized Poisson Hidden Markov Model for Overdispersed or Underdispersed Count Data." Revista Colombiana de Estadística 43, no. 1 (January 1, 2020): 71–82. http://dx.doi.org/10.15446/rce.v43n1.77542.

Full text

Abstract:

The most suitable statistical method for explaining serial dependency in time series count data is that based on Hidden Markov Models (HMMs). These models assume that the observations are generated from a finite mixture of distributions governed by the principle of Markov chain (MC). Poisson-Hidden Markov Model (P-HMM) may be the most widely used method for modelling the above said situations. However, in real life scenario, this model cannot be considered as the best choice. Taking this fact into account, we, in this paper, go for Generalised Poisson Distribution (GPD) for modelling count data. This method can rectify the overdispersion and underdispersion in the Poisson model. Here, we develop Generalised Poisson Hidden Markov model (GP-HMM) by combining GPD with HMM for modelling such data. The results of the study on simulated data and an application of real data, monthly cases of Leptospirosis in the state of Kerala in South India, show good convergence properties, proving that the GP-HMM is a better method compared to P-HMM.

APA, Harvard, Vancouver, ISO, and other styles

33

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

34

N.K.G., Seck, Ngom A., and Noba K. "Modelling Underdispersed Count Data: Relative Performance of Poisson Model and its Alternatives." African Journal of Mathematics and Statistics Studies 5, no. 3 (August 23, 2022): 16–32. http://dx.doi.org/10.52589/ajmss-1wpjqhyt.

Full text

Abstract:

Count data are common in many fields and often modelled with the Poisson model. However, the equidispersion assumption (variance = mean) related to the Poisson model is often violated in practice. While much research has focused on modelling overdispersed count data, underdispersion has received relatively little attention. Alternative models are therefore needed to handle overdispersion (variance > mean) and underdispersion (variance < mean). This study assessed the relative performance of the Poisson model and its alternatives (COM-Poisson, Generalized Poisson Regression, Double Poisson and Gamma Count) to model underdispersed count data. Using a Monte Carlo experiment, the simulation plan considered various underdispersion levels (k (variance/mean) = 0.2, 0.5 and 0.81), k=1 as a control, and sample sizes (n=20, 50, 100, 300 and 500). Results showed that the Poisson model is not robust to handle underdispersion but it is the best performer when k=1. The COM-Poisson model best fitted severe underdispersed data (k=0.2). It is also the best performer model for moderate underdispersed count data (k=0.81). However, when k=0.5, the Double Poisson model and Generalized Poisson model outperformed other models for relatively large sample sizes (n=100, 300 and 500). Our finding suggests that none of the models suits all situations. Therefore, in practice, several of these models need to be tested to select the best one.

APA, Harvard, Vancouver, ISO, and other styles

35

Jonsson, Viktor, Tobias Österlund, Olle Nerman, and Erik Kristiansson. "Modelling of zero-inflation improves inference of metagenomic gene count data." Statistical Methods in Medical Research 28, no. 12 (November 25, 2018): 3712–28. http://dx.doi.org/10.1177/0962280218811354.

Full text

Abstract:

Metagenomics enables the study of gene abundances in complex mixtures of microorganisms and has become a standard methodology for the analysis of the human microbiome. However, gene abundance data is inherently noisy and contains high levels of biological and technical variability as well as an excess of zeros due to non-detected genes. This makes the statistical analysis challenging. In this study, we present a new hierarchical Bayesian model for inference of metagenomic gene abundance data. The model uses a zero-inflated overdispersed Poisson distribution which is able to simultaneously capture the high gene-specific variability as well as zero observations in the data. By analysis of three comprehensive datasets, we show that zero-inflation is common in metagenomic data from the human gut and, if not correctly modelled, it can lead to substantial reductions in statistical power. We also show, by using resampled metagenomic data, that our model has, compared to other methods, a higher and more stable performance for detecting differentially abundant genes. We conclude that proper modelling of the gene-specific variability, including the excess of zeros, is necessary to accurately describe gene abundances in metagenomic data. The proposed model will thus pave the way for new biological insights into the structure of microbial communities.

APA, Harvard, Vancouver, ISO, and other styles

36

Angers, Jean-François, Denise Desjardins, Georges Dionne, and François Guertin. "MODELLING AND ESTIMATING INDIVIDUAL AND FIRM EFFECTS WITH COUNT PANEL DATA." ASTIN Bulletin 48, no. 3 (May 2, 2018): 1049–78. http://dx.doi.org/10.1017/asb.2018.19.

Full text

Abstract:

AbstractWe propose a new parametric model for the modelling and estimation of event distributions for individuals in different firms. The analysis uses panel data and takes into account individual and firm effects in a non-linear model. Non-observable factors are treated as random effects. In our application, the distribution of accidents is affected by observable and non-observable factors from vehicles, drivers and fleets of vehicles. Observable and unobservable factors are significant to explain road accidents, which mean that insurance pricing should take into account all these factors. A fixed effects model is also estimated to test the consistency of the random effects model.

APA, Harvard, Vancouver, ISO, and other styles

37

Liesenfeld, Roman, Ingmar Nolte, and Winfried Pohlmeier. "Modelling financial transaction price movements: a dynamic integer count data model." Empirical Economics 30, no. 4 (September 27, 2005): 795–825. http://dx.doi.org/10.1007/s00181-005-0001-1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

38

Santos Silva, J. M. C. "Generalized poisson regression for positive count data." Communications in Statistics - Simulation and Computation 26, no. 3 (January 1997): 1089–102. http://dx.doi.org/10.1080/03610919708813428.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

Gill, G., T. Sakrani, W. Cheng, and J. Zhou. "COMPARISON OF ADJACENCY AND DISTANCE-BASED APPROACHES FOR SPATIAL ANALYSIS OF MULTIMODAL TRAFFIC CRASH DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 14, 2017): 1157–61. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-1157-2017.

Full text

Abstract:

Many studies have utilized the spatial correlations among traffic crash data to develop crash prediction models with the aim to investigate the influential factors or predict crash counts at different sites. The spatial correlation have been observed to account for heterogeneity in different forms of weight matrices which improves the estimation performance of models. But very rarely have the weight matrices been compared for the prediction accuracy for estimation of crash counts. This study was targeted at the comparison of two different approaches for modelling the spatial correlations among crash data at macro-level (County). Multivariate Full Bayesian crash prediction models were developed using Decay-50 (distance-based) and Queen-1 (adjacency-based) weight matrices for simultaneous estimation crash counts of four different modes: vehicle, motorcycle, bike, and pedestrian. The goodness-of-fit and different criteria for accuracy at prediction of crash count reveled the superiority of Decay-50 over Queen-1. Decay-50 was essentially different from Queen-1 with the selection of neighbors and more robust spatial weight structure which rendered the flexibility to accommodate the spatially correlated crash data. The consistently better performance of Decay-50 at prediction accuracy further bolstered its superiority. Although the data collection efforts to gather centroid distance among counties for Decay-50 may appear to be a downside, but the model has a significant edge to fit the crash data without losing the simplicity of computation of estimated crash count.

APA, Harvard, Vancouver, ISO, and other styles

40

Fotouhi, Ali Reza. "Modelling overdispersion in longitudinal count data in clinical trials with application to epileptic data." Contemporary Clinical Trials 29, no. 4 (July 2008): 547–54. http://dx.doi.org/10.1016/j.cct.2008.01.005.

Full text

APA, Harvard, Vancouver, ISO, and other styles

41

Yan, Yixin, Jiliang Hu, Xiding Chen, and A. P. Senthil Kumar. "Econometric Modelling Based on Dynamic Count Regression and China Power Supply Dataset." Mathematical Problems in Engineering 2022 (May 28, 2022): 1–5. http://dx.doi.org/10.1155/2022/6864015.

Full text

Abstract:

Traditionally, economic data of power supply is often analyzed through the count regression model due to the type of empirical data in the decision-making process. However, in reality, it is difficult to use count data model for data with autoregressive features. The main reason is that the time series features and autoregressive attributes cannot be controlled through the count regression model, which violates the assumptions set by the model. Therefore, there may be errors in the empirical analysis results. This letter firstly describes the characteristic of the count regression model and the problem, and then we refine the multiplicative autoregressive count model for dynamic count data. The model has desirable theoretical properties and is trivial to incorporate into existing models for the count data. In this study, the multiplicative autoregressive counting model for dynamic counting data is improved. The model has ideal theoretical properties and can be easily incorporated into existing economic models of counting data, especially for power supply policy analyses.

APA, Harvard, Vancouver, ISO, and other styles

42

Fernández, Arturo J. "Optimal lot disposition from Poisson–Lindley count data." Applied Mathematical Modelling 70 (June 2019): 595–604. http://dx.doi.org/10.1016/j.apm.2019.01.045.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

HEDLEY, Sharon L., Stephen T. BUCKLAND, and DAVID L. BORCHERS. "Spatial modelling from line transect data." J. Cetacean Res. Manage. 1, no. 3 (December 1, 1999): 255–64. http://dx.doi.org/10.47536/jcrm.v1i3.477.

Full text

Abstract:

In this paper, two new methods are presented that enable spatial models to be fitted from line transect data. Building on preliminary work by Cumberworth et al. (1996) and Hedley et al. (1997), the first method is based on a count model and involves dividing the survey effort into small segments then modelling the number of schools in each segment. In contrast, the second method uses a model based on the intervals between detections. Its formulation is derived in detail to obtain the likelihood function for the distances between detections, conditional on an estimated detection function. Both models can be fitted using standard statistical software, although variances must be estimated using computer intensive methods. We apply the methods to data from the 1992/93 IWC/IDCR Antarctic survey of Area III, fitting generalised additive models to obtain estimates of minke whale abundance, using the parametric bootstrap to estimate variance. The results from fitting these models are compared with the results of a previous analysis by Borchers and Cameron (1995}, which used conventional stratified methods.

APA, Harvard, Vancouver, ISO, and other styles

44

Stoklosa, Jakub, Rachel V. Blakey, and Francis K. C. Hui. "An Overview of Modern Applications of Negative Binomial Modelling in Ecology and Biodiversity." Diversity 14, no. 5 (April 21, 2022): 320. http://dx.doi.org/10.3390/d14050320.

Full text

Abstract:

Negative binomial modelling is one of the most commonly used statistical tools for analysing count data in ecology and biodiversity research. This is not surprising given the prevalence of overdispersion (i.e., evidence that the variance is greater than the mean) in many biological and ecological studies. Indeed, overdispersion is often indicative of some form of biological aggregation process (e.g., when species or communities cluster in groups). If overdispersion is ignored, the precision of model parameters can be severely overestimated and can result in misleading statistical inference. In this article, we offer some insight as to why the negative binomial distribution is becoming, and arguably should become, the default starting distribution (as opposed to assuming Poisson counts) for analysing count data in ecology and biodiversity research. We begin with an overview of traditional uses of negative binomial modelling, before examining several modern applications and opportunities in modern ecology/biodiversity where negative binomial modelling is playing a critical role, from generalisations based on exploiting its Poisson-gamma mixture formulation in species distribution models and occurrence data analysis, to estimating animal abundance in negative binomial N-mixture models, and biodiversity measures via rank abundance distributions. Comparisons to other common models for handling overdispersion on real data are provided. We also address the important issue of software, and conclude with a discussion of future directions for analysing ecological and biological data with negative binomial models. In summary, we hope this overview will stimulate the use of negative binomial modelling as a starting point for the analysis of count data in ecology and biodiversity studies.

APA, Harvard, Vancouver, ISO, and other styles

45

Lydia Jane G. and Seetha Hari. "Crime Prediction Using Twitter Data." International Journal of e-Collaboration 17, no. 3 (July 2021): 62–74. http://dx.doi.org/10.4018/ijec.2021070104.

Full text

Abstract:

As social media platforms are being increasingly used across the world, there are many prospects to using the data for prediction and analysis. In the Twitter platform, there are discussions about any events, passions, and many more topics. All these discussions are publicly available. This makes Twitter the ultimate source to use the data as an augmentation for the decision support systems. In this paper, the use of GPS tagged tweets for crime prediction is researched. The Twitter data is collected from Chicago and cleaned, and topic modelling is applied to the resultant set. Before topic modelling, an algorithm has been developed to identify tweets that are relevant to the crime prediction problem. Once the relevant tweets are identified, topic modelling is applied to find out the major crimes in the different beats of Chicago. Kernel density estimation (KDE) is applied to traditional data. The result of this and topic modelling are used to predict the crime count for each beat using logistic regression.

APA, Harvard, Vancouver, ISO, and other styles

46

Stapper, Manuel. "Count Data Time Series Modelling in Julia—The CountTimeSeries.jl Package and Applications." Entropy 23, no. 6 (May 25, 2021): 666. http://dx.doi.org/10.3390/e23060666.

Full text

Abstract:

A new software package for the Julia language, CountTimeSeries.jl, is under review, which provides likelihood based methods for integer-valued time series. The package’s functionalities are showcased in a simulation study on finite sample properties of Maximum Likelihood (ML) estimation and three real-life data applications. First, the number of newly infected COVID-19 patients is predicted. Then, previous findings on the need for overdispersion and zero inflation are reviewed in an application on animal submissions in New Zealand. Further, information criteria are used for model selection to investigate patterns in corporate insolvencies in Rhineland-Palatinate. Theoretical background and implementation details are described, and complete code for all applications is provided online. The CountTimeSeries package is available at the general Julia package registry.

APA, Harvard, Vancouver, ISO, and other styles

47

Lambert, Phillipe. "Modelling of Repeated Series of Count Data Measured at Unequally Spaced Times." Applied Statistics 45, no. 1 (1996): 31. http://dx.doi.org/10.2307/2986220.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Yusuf, Oyindamola B., Rotimi Felix Afolabi, and Ayoola S. Ayoola. "Modelling Excess Zeros in Count Data with Application to Antenatal Care Utilisation." International Journal of Statistics and Probability 7, no. 3 (April 17, 2018): 22. http://dx.doi.org/10.5539/ijsp.v7n3p22.

Full text

Abstract:

Poisson and negative binomial regression models have been used as a standard for modelling count outcomes; but these methods do not take into account the problems associated with excess zeros. However, zero-inflated and hurdle models have been proposed to model count data with excess zeros. The study therefore compared the performance of Zero-inflated (Zero-inflated Poisson (ZIP) and Zero-inflated negative binomial (ZINB)), and hurdle (Hurdle Poisson (HP) and Hurdle negative binomial (HNB)) models in determining the factors associated with the number of Antenatal Care (ANC) visits in Nigeria. Using the 2013 Nigeria Demographic and Health Survey dataset, a sample of 19 652 women of reproductive age who gave birth five years prior to the survey and provided information about ANC visits was utilised. Data were analysed using descriptive statistics, ZIP, ZINB, HP and HNB models, and information criteria (AIC/BIC) was used to assess model fit. Participants’ mean age was 29.5 ± 7.3 years and median number of ANC visits was 4 (range: 0 - 30). About half (54.9%) of the participants had at least 4 ANC visits while 33.9% had none. The ZINB (AIC = 83 039.4; BIC = 83 470.3) fitted the data better than the ZIP or HP; however, HNB (AIC = 83 041.4; BIC = 83 472.3) competed favorably well with it. The Zero-inflated negative binomial model provided the better fit for the data. We suggest the Zero-inflated negative binomial model for count data with excess zeros of unknown sources such as the number of ANC visits in Nigeria.

APA, Harvard, Vancouver, ISO, and other styles

49

Olayiwola, Saheed O., and Bayo L. O. Kazeem. "Count data modelling of health insurance and health care utilisation in Nigeria." Journal of Economics and Management 35 (2019): 106–23. http://dx.doi.org/10.22367/jem.2019.35.06.

Full text

APA, Harvard, Vancouver, ISO, and other styles

50

Yadav, Arvind Kumar, Susanta Nag, Pabitra Kumar Jena, and Kirtti Ranjan Paltasingh. "Determinants of Antenatal Care Utilisation in India: A Count Data Modelling Approach." Journal of Development Policy and Practice 6, no. 2 (July 2021): 210–30. http://dx.doi.org/10.1177/24551333211030349.

Full text

Abstract:

The article explores the micro-level factors (social, economic and demographic) that determine the utilisation of antenatal care (ANC) services in India using the Bayesian count data regression model. The primary purpose is to rectify the methodological loopholes in the existing literature using a count data regression model that overcomes the problems of overdispersion in the data. Using data from ‘National Family Health Survey’ (NFHS) data on women of reproductive age (15–49 years), we find that about 33% of pregnant women have not availed ANC during their pregnancy. The factors such as women’s education and partner/husband’s education, children’s birth order, household income, availability of basic amenities, like clean drinking water, media exposure, holding of bank accounts and use of mobile phones are statistically significant and positively affect ANC utilisation. Therefore, the study calls for prioritisation of and special attention to uneducated or less educated rural pregnant women. They should be incentivised adequately to utilise ANC services, which may drastically reduce inadequacy in ANC utilisation and improve mothers’ health before and after delivery. Awareness camps should be organised in every village in rural areas about pregnancy-related complications and the benefits of ANC check-ups. Massive infrastructure in the form of primary health centres or community health centres is the need of the hour in rural India.

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'Count data modelling'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles