Estimation and Testing of Binomial and Beta-Binomial Regression Models with and without Zero Inflation

Poisson and negative binomial regression procedures have proliferated, and now are available in virtually all statistical packages. Along with the regression procedures themselves are procedures for addressing issues related to the over-dispersion and excessive zeros commonly observed in count data. These approaches, zero-inflated Poisson and zero-inflated negative binomial models, use logit or probit models for the “excess” zeros and count regression models for the counted data. Although these models are often appropriate on statistical grounds, their interpretation may prove substantively difficult. This article explores this dilemma, using data from a study of individuals released from facilities maintained by the Massachusetts Department of Correction.

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Group regularization for zero-inflated negative binomial regression models with an application to health care demand in Germany

Statistics in Medicine ◽

10.1002/sim.7804 ◽

2018 ◽

Vol 37 (20) ◽

pp. 3012-3026 ◽

Cited By ~ 2

Author(s):

Saptarshi Chatterjee ◽

Shrabanti Chowdhury ◽

Himel Mallick ◽

Prithish Banerjee ◽

Broti Garai

Keyword(s):

Health Care ◽

Regression Models ◽

Negative Binomial ◽

Negative Binomial Regression ◽

Health Care Demand ◽

Binomial Regression

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6. Poisson and Negative Binomial Regression Models

Regression Models for Categorical, Count, and Related Variables ◽

10.1525/9780520965492-008 ◽

2019 ◽

pp. 131-158

Keyword(s):

Regression Models ◽

Negative Binomial ◽

Negative Binomial Regression ◽

Binomial Regression

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What Do We Know About Changing Economic Activity of Firms?

Studies in Microeconomics ◽

10.1177/2321022218869790 ◽

2019 ◽

pp. 232102221886979

Author(s):

Radhika Pandey ◽

Amey Sapre ◽

Pramod Sinha

Keyword(s):

Economic Activity ◽

Regression Models ◽

Statistical Approach ◽

Negative Binomial ◽

Large Data ◽

Poisson Model ◽

Negative Binomial Regression ◽

Data Set ◽

Binomial Regression ◽

History Of

Identification of primary economic activity of firms is a prerequisite for compiling several macro aggregates. In this paper, we take a statistical approach to understand the extent of changes in primary economic activity of firms over time and across different industries. We use the history of economic activity of over 46,000 firms spread over 25 years from CMIE Prowess to identify the number of times firms change the nature of their business. Using the count of changes, we estimate Poisson and Negative Binomial regression models to gain predictability over changing economic activity across industry groups. We show that a Poisson model accurately characterizes the distribution of count of changes across industries and that firms with a long history are more likely to have changed their primary economic activity over the years. Findings show that classification can be a crucial problem in a large data set like the MCA21 and can even lead to distortions in value addition estimates at the industry level. JEL Classifications: D22, E00, E01

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A comparison of some link functions for binomial regression models with application to school drop-out rates in East Java

10.1063/1.5139815 ◽

2019 ◽

Author(s):

Rindang Bangun Prasetyo ◽

Heri Kuswanto ◽

Nur Iriawan ◽

Brodjol Sutijo Suprih Ulama

Keyword(s):

Regression Models ◽

Drop Out ◽

Link Functions ◽

Binomial Regression ◽

School Drop Out ◽

Drop Out Rates

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Traffic accident modeling: some statistical issues

Canadian Journal of Civil Engineering ◽

10.1139/l06-056 ◽

2006 ◽

Vol 33 (9) ◽

pp. 1115-1124 ◽

Cited By ~ 53

Author(s):

Z Sawalha ◽

T Sayed

Keyword(s):

Road Safety ◽

Regression Models ◽

Model Building ◽

Negative Binomial ◽

Prediction Models ◽

Negative Binomial Regression ◽

Outlier Analysis ◽

Accident Prediction ◽

Binomial Regression ◽

Statistical Issues

Accident prediction models are invaluable tools that have many applications in road safety analysis. However, there are certain statistical issues related to accident modeling that either deserve further attention or have not been dealt with adequately in the road safety literature. This paper discusses and illustrates how to deal with two statistical issues related to modeling accidents using Poisson and negative binomial regression. The first issue is that of model building or deciding which explanatory variables to include in an accident prediction model. The study differentiates between applications for which it is advisable to avoid model over-fitting and other applications for which it is desirable to fit the model to the data as closely as possible. It then suggests procedures for developing parsimonious models, i.e., models that are not over-fitted, and best-fit models. The second issue discussed in the paper is that of outlier analysis. The study suggests a procedure for the identification and exclusion of extremely influential outliers from the development of Poisson and negative binomial regression models. The procedures suggested for model building and conducting outlier analysis are more straightforward to apply in the case of Poisson regression models because of an added complexity presented by the shape parameter of the negative binomial distribution. The paper, therefore, presents flowcharts detailing the application of the procedures when modeling is carried out using negative binomial regression. The described procedures are then applied in the development of negative binomial accident prediction models for the urban arterials of the cities of Vancouver and Richmond located in the province of British Columbia, Canada. Key words: accident prediction models, overfitting, parsimony, outlier analysis, Poisson regression, negative binomial regression.

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Association between recent overnight travel and use of long-lasting insecticidal nets in rural Uganda: a prospective cohort study in Tororo

Malaria Journal ◽

10.1186/s12936-020-03475-3 ◽

2020 ◽

Vol 19 (1) ◽

Author(s):

Emmanuel Arinaitwe ◽

Joaniter I. Nankabirwa ◽

Paul Krezanoski ◽

John Rek ◽

Victor Kamya ◽

...

Keyword(s):

Repeated Measures ◽

Generalized Estimating Equations ◽

Regression Models ◽

Estimating Equations ◽

Factors Associated ◽

Tororo District ◽

Binomial Regression ◽

Llin Use ◽

Long Lasting Insecticidal Nets ◽

At Home

Abstract Background The burden of malaria in Uganda remains high, but has become increasingly heterogenous following intensified malaria control. Travel within Uganda is recognized as a risk factor for malaria, but behaviours associated with travel are not well-understood. To address this knowledge gap, malaria-relevant behaviours of cohort participants were assessed during travel and at home in Uganda. Methods Residents from 80 randomly selected households in Nagongera sub-county, Tororo district were enrolled into a cohort to study malaria in rural Uganda. All participants were given long-lasting insecticidal nets (LLINs) at enrolment and were evaluated every 4 weeks at the study clinic. Participants were asked if they had travelled overnight from their home, and if so, a questionnaire was administered to capture information on travel details and behaviours. Behaviour while travelling was assessed within 4 weeks following travel during the study clinic visit. Behaviour while at home was assessed using a similar questionnaire during two-weekly home visits. Behaviours while travelling vs at home were compared using log binomial regression models with generalized estimating equations adjusting for repeated measures in the same individual. Analysis of factors associated with LLIN adherence, such as destination and duration of travel, time to bed during travel, gender and age at time of travel, were assessed using log binomial regression models with generalized estimating equations adjusting for repeated measures in the same individual. Results Between October 2017 and October 2019, 527 participants were enrolled and assessed for travel. Of these, 123 (23.2%) reported taking 211 overnight trips; 149 (70.6%) trips were within Tororo. Participants were less likely to use LLINs when travelling than when at home (41.0% vs. 56.2%, relative risk [RR] 0.73, 95% CI 0.60–0.89, p = 0.002); this difference was noted for women (38.8% vs 59.2%, RR 0.66, 95% CI 0.52–0.83, p = 0.001) but not men (48.3% vs 46.6%, RR 0.96, 95% CI 0.67–1.40, p = 0.85). In an adjusted analysis, factors associated with LLIN use when travelling included destination (travelling to districts not receiving indoor residual spraying [IRS] 65.8% vs Tororo district 32.2%, RR 1.80, 95% CI 1.31–2.46, p < 0.001) and duration of travel (> 7 nights 60.3% vs one night 24.4%, RR 1.97, 95% CI 1.07–3.64, p = 0.03). Conclusions Travellers, particularly women, were less likely to use LLINs when travelling than when at home. LLIN adherence was higher among those who travelled to non-IRS districts and for more than 1 week, suggesting that perceived malaria risk influences LLIN use. Strategies are needed to raise awareness of the importance of using LLINs while travelling.

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