Relevant bibliographies by topics / Random effect negative binomial models (RENB)

Academic literature on the topic 'Random effect negative binomial models (RENB)'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Random effect negative binomial models (RENB)"

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Shankar, Venkataraman N., Richard B. Albin, John C. Milton, and Fred L. Mannering. "Evaluating Median Crossover Likelihoods with Clustered Accident Counts: An Empirical Inquiry Using the Random Effects Negative Binomial Model." Transportation Research Record: Journal of the Transportation Research Board 1635, no. 1 (January 1998): 44–48. http://dx.doi.org/10.3141/1635-06.

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Insights into plausible methodological frameworks specifically with respect to two key issues—(1) mathematical formulation of the underlying process affecting median crossover accidents and (2) the factors affecting median crossover frequencies in Washington State—are provided in this study. Random effects negative binomial (RENB) and the cross-sectional negative binomial (NB) models are examined. The specification comparisons indicate benefits from using the RENB model only when spatial and temporal effects are totally unobserved. When spatial and temporal effects are explicitly included, the NB model is statistically adequate, while the RENB model appears to lose its distributional advantage. Such findings might be artifacts of the median crossover accident dataset used in this study. While the NB model appears to be the superior model in the present case of median crossover accidents, the marginally inferior performance of the RENB model warrants further examination through application to regular accident types in light of its flexibility to incorporate temporal and cross-sectional variations simultaneously in panel counts. From a predictive standpoint, RENB offers advantages in terms of model transferability and updating.
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Tulu, Getu Segni, M. Mazharul Haque, Simon Washington, and Mark J. King. "Investigating Pedestrian Injury Crashes on Modern Roundabouts in Addis Ababa, Ethiopia." Transportation Research Record: Journal of the Transportation Research Board 2512, no. 1 (January 2015): 1–10. http://dx.doi.org/10.3141/2512-01.

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Pedestrian crashes represent about 40% of total fatal crashes in low-income developing countries. Although many pedestrian crashes in these countries occur at unsignalized intersections such as roundabouts, studies focusing on this issue are limited. The objective of this study was to develop safety performance functions for pedestrian crashes at modern roundabouts to identify significant roadway geometric, traffic, and land use characteristics related to pedestrian safety. Detailed data, including various forms of exposure, geometric and traffic characteristics, and spatial factors such as proximity to schools and to drinking establishments were collected from a sample of 22 modern roundabouts in Addis Ababa, Ethiopia, representing about 56% of such roundabouts in Addis Ababa. To account for spatial correlation resulting from multiple observations at a roundabout, both the random effect Poisson (REP) and random effect negative binomial (RENB) regression models were estimated. Model goodness-of-fit statistics revealed a marginally superior fit of the REP model to the data compared with the RENB model. Pedestrian crossing volume and the product of traffic volumes along major and minor roads had significant and positive associations with pedestrian crashes at roundabouts. The presence of a public transport (bus or taxi) terminal beside a roundabout was associated with increased pedestrian crashes. Although the maximum gradient of an approach road was negatively associated with pedestrian safety, the provision of a raised median along an approach appeared to increase pedestrian safety at roundabouts. Remedial measures were identified for combating pedestrian safety problems at roundabouts in the context of a developing country.
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Noh, Maengseok, and Youngjo Lee. "Extended negative binomial hurdle models." Statistical Methods in Medical Research 28, no. 5 (April 11, 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 accommodating such phenomenon occurring in real data analyses.
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Mićić, Spasoje, Radoje Vujadinović, Goran Amidžić, Milanko Damjanović, and Boško Matović. "Accident Frequency Prediction Model for Flat Rural Roads in Serbia." Sustainability 14, no. 13 (June 24, 2022): 7704. http://dx.doi.org/10.3390/su14137704.

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Traffic accidents, by their nature, are random events; therefore, it is difficult to estimate the exact places and times of their occurrences and the true nature of their impacts. Although they are hard to precisely predict, preventative actions can be taken and their numbers (in a certain period) can be approximately predicted. In this study, we investigated the relationship between accident frequency and factors that affect accident frequency; we used accident data for events that occurred on a flat rural state road in Serbia. The analysis was conducted using five statistical models, i.e., Poisson, negative binomial, random effect negative binomial, zero-inflated Poisson, and zero-inflated negative binomial models. The results indicated that the random effect negative binomial model outperformed the other models in terms of goodness-of-fit measures; it was chosen as the accident prediction model for flat rural roads. Four explanatory variables—annual average daily traffic, segment length, number of horizontal curves, and access road density—were found to significantly affect accident frequency. The results of this research can help road authorities make decisions about interventions and investments in road networks, designing new roads, and reconstructing existing roads.
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Bae, Bumjoon, Changju Lee, Tae-Young Pak, and Sunghoon Lee. "Identifying Temporal Aggregation Effect on Crash-Frequency Modeling." Sustainability 13, no. 11 (May 31, 2021): 6214. http://dx.doi.org/10.3390/su13116214.

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Aggregation of spatiotemporal data can encounter potential information loss or distort attributes via individual observation, which would influence modeling results and lead to an erroneous inference, named the ecological fallacy. Therefore, deciding spatial and temporal resolution is a fundamental consideration in a spatiotemporal analysis. The modifiable temporal unit problem (MTUP) occurs when using data that is temporally aggregated. While consideration of the spatial dimension has been increasingly studied, the counterpart, a temporal unit, is rarely considered, particularly in the traffic safety modeling field. The purpose of this research is to identify the MTUP effect in crash-frequency modeling using data with various temporal scales. A sensitivity analysis framework is adopted with four negative binomial regression models and four random effect negative binomial models having yearly, quarterly, monthly, and weekly temporal units. As the different temporal unit was applied, the result of the model estimation also changed in terms of the mean and significance of the parameter estimates. Increasing temporal correlation due to using the small temporal unit can be handled with the random effect models.
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Zhu, Huirong, Sheng Luo, and Stacia M. DeSantis. "Zero-inflated count models for longitudinal measurements with heterogeneous random effects." Statistical Methods in Medical Research 26, no. 4 (June 24, 2015): 1774–86. http://dx.doi.org/10.1177/0962280215588224.

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Longitudinal zero-inflated count data arise frequently in substance use research when assessing the effects of behavioral and pharmacological interventions. Zero-inflated count models (e.g. zero-inflated Poisson or zero-inflated negative binomial) with random effects have been developed to analyze this type of data. In random effects zero-inflated count models, the random effects covariance matrix is typically assumed to be homogeneous (constant across subjects). However, in many situations this matrix may be heterogeneous (differ by measured covariates). In this paper, we extend zero-inflated count models to account for random effects heterogeneity by modeling their variance as a function of covariates. We show via simulation that ignoring intervention and covariate-specific heterogeneity can produce biased estimates of covariate and random effect estimates. Moreover, those biased estimates can be rectified by correctly modeling the random effects covariance structure. The methodological development is motivated by and applied to the Combined Pharmacotherapies and Behavioral Interventions for Alcohol Dependence (COMBINE) study, the largest clinical trial of alcohol dependence performed in United States with 1383 individuals.
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Stapleton, Steven Y., Timothy J. Gates, Raul Avelar, Srinivas R. Geedipally, and Ramin Saedi. "Safety Performance Functions for Low-Volume Rural Stop-Controlled Intersections." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 5 (April 12, 2019): 660–69. http://dx.doi.org/10.1177/0361198119840348.

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This study involved the development of safety performance functions for rural, low-volume, minor road stop-controlled intersections in Michigan. Facility types included three-leg stop-controlled (3ST) and four-leg stop-controlled (4ST) intersections under state or county jurisdiction and were sampled from each of Michigan’s 83 counties. To isolate lower-volume rural intersections, major roadway traffic volumes were limited to the range of 400–2,000 vehicles per day (vpd). Data were compiled from several sources for 2,023 intersections statewide. These data included traffic crashes, volumes, roadway classification, geometry, cross-sectional features, and other site characteristics covering the period of 2011–2015. Random effects negative binomial regression models were specified for each stop-controlled intersection type considering factors such as driveway density, lighting presence, turn lane presence, and intersection skew, in addition to volume. To account for the unobserved heterogeneity between counties, mixed effects negative binomial models with a county-specific random effect were utilized. Furthermore, unobserved temporal effects were controlled through the use of a year-specific random effect. Separate models were developed for fatal/injury crashes, property damage crashes, and select target crash types. The analysis found that skew angles of greater than five degrees led to significantly greater crash occurrence for both 3ST and 4ST intersections, while greater than two driveways near the intersection led to significantly greater angle crashes at 4ST intersections. Other factors were found to have little impact on crash occurrence. Comparison with the Highway Safety Manual (HSM) base models showed that the HSM models over-predict crashes on 4ST intersections and 3ST intersections with volumes between 1,200 and 2,000 vpd.
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Woolliams, J. A., Z. W. Luo, B. Villanueva, D. Waddington, P. J. Broadbent, W. A. C. McKelvey, and J. J. Robinson. "Analysis of factors affecting superovulatory responses in ruminants." Journal of Agricultural Science 124, no. 1 (February 1995): 61–70. http://dx.doi.org/10.1017/s0021859600071252.

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SUMMARYData on ovulation rate and numbers of ova and transferable embryos recovered from superovulated cattle and sheep were analysed using generalized linear models, quasi-likelihood, restricted maximum likelihood (REML) and generalized linear mixed models (GLMMS). The data pertained to the operation of nucleus breeding schemes in cattle and the commercial application of embryo transfer in sheep.Results of the analyses showed that generalized linear models involving Poisson and Binomial distributions were inappropriate because of over-dispersion, and that analyses using quasi-likelihood to model negative binomial and β-binomial distributions were more suitable. Factors identified as important in determining the results in cattle were the number of previous superovulations (a higher proportion of transferable embryos were obtained in the initial flush compared to subsequent recoveries in two out of three sets of data), the donor (significant in all analyses with repeated recoveries) and its mate (significant in some analyses). In sheep, the use of pFSH or hMG for superovulation increased embryo yields above those obtained with PMSG + GnRH. Analyses of a further data set for sheep showed the effect of breed was ambiguous.The effects of donors and their mates were treated as random effects in analyses involving REML and GLMMS. Results showed that the repeatability of the number of transferable embryos produced per donor ranged between 0·13 and 0·23 in three sets of data and was significant in all cases. In these analyses the variance among mates was not significantly different from zero.The results of analyses were used to develop a random generator to simulate the numbers of ova and embryos recovered from a cow following superovulation. By sampling from negative binomial distributions where the scale factor used for each cow was a normally distributed deviate, distributions were obtained which had the same mean, variance and repeatability as those observed.
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Champahom, Thanapong, Sajjakaj Jomnonkwao, Chinnakrit Banyong, Watanya Nambulee, Ampol Karoonsoontawong, and Vatanavongs Ratanavaraha. "Analysis of Crash Frequency and Crash Severity in Thailand: Hierarchical Structure Models Approach." Sustainability 13, no. 18 (September 9, 2021): 10086. http://dx.doi.org/10.3390/su131810086.

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Currently, research on the development of crash models in terms of crash frequency on road segments and crash severity applies the principles of spatial analysis and heterogeneity due to the methods’ suitability compared with traditional models. This study focuses on crash severity and frequency in Thailand. Moreover, this study aims to understand crash frequency and fatality. The result of the intra-class correlation coefficient found that the spatial approach should analyze the data. The crash frequency model’s best fit is a spatial zero-inflated negative binomial model (SZINB). The results of the random parameters of SZINB are insignificant, except for the intercept. The crash frequency model’s significant variables include the length of the segment and average annual traffic volume for the fixed parameters. Conversely, the study finds that the best fit model of crash severity is a logistic regression with spatial correlations. The variances of random effect are significant such as the intersection, sideswipe crash, and head-on crash. Meanwhile, the fixed-effect variables significant to fatality risk include motorcycles, gender, non-use of safety equipment, and nighttime collision. The paper proposes a policy applicable to agencies responsible for driver training, law enforcement, and those involved in crash-reduction campaigns.
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Rohmaniah, Siti Alfiatur, and Novita Eka Chandra. "PERHITUNGAN PREMI ASURANSI JIWA MENGGUNAKAN GENERALIZED LINEAR MIXED MODELS." Jurnal Ilmiah Teknosains 4, no. 2 (January 3, 2019): 80. http://dx.doi.org/10.26877/jitek.v4i2.3004.

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The price of life insurance premiums for each person depends on the probability of death, not only based on age and gender as offered by an Indonesian insurance company. The purpose of this study is to determine premium prices on underwriting factors and frailty factors using Generalized Linear Mixed Models (GLMM). GLMM is used for modeling a combination of fixed effect heterogeneity (underwriting factors) and random effects (frailty factors) between individuals. The data used longitudinal data about underwriting factors that have Binomial distribution are taken from the Health and Retirement Study and processed using R software. Because the data used by survey data within an interval of two years, so the probability of death is obtained for an interval the next two years. Underwriting factors that have a significant effect on the probability of death are age, alcoholic status, heart disease, and diabetes. As a result, is obtained the probability of death models each individual to determine life insurance premium prices. The premium price of each individual is different because depends on underwriting factors and frailty. If frailty is positive, it means that a person level of vulnerability when experiencing the risk of death is greater than negative frailty.
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Conference papers on the topic "Random effect negative binomial models (RENB)"

1

Awad, Hamid Ahmed, and Tony Parry. "Investigation the Effect of Pavement Condition Characteristics on Bend Segments Accident Frequency: Application of Fixed and Random Parameters Negative Binomial Models." In International Conference on Transportation and Development 2018. Reston, VA: American Society of Civil Engineers, 2018. http://dx.doi.org/10.1061/9780784481554.017.

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