Analysis of Vehicle Theft: A Case Study in Malaysia using Functional Forms of Negative Binomial Regression Models

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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Practical Methods for Imputing Follower Count Dynamics

Sociological Methods & Research ◽

10.1177/0049124120926210 ◽

2020 ◽

pp. 004912412092621

Author(s):

C. Ben Gibson ◽

Jeannette Sutton ◽

Sarah K. Vos ◽

Carter T. Butts

Keyword(s):

Missing Values ◽

Linear Models ◽

Negative Binomial ◽

Negative Binomial Regression ◽

Application Programming Interface ◽

System Failures ◽

Binomial Regression ◽

Application Programming ◽

Programming Interface

Microblogging sites have become important data sources for studying network dynamics and information transmission. Both areas of study, however, require accurate counts of indegree, or follower counts; unfortunately, collection of complete time series on follower counts can be limited by application programming interface constraints, system failures, or temporal constraints. In addition, there is almost always a time difference between the point at which follower counts are queried and the time a user posts a tweet. Here, we consider the use of three classes of simple, easily implemented methods for follower imputation: polynomial functions, splines, and generalized linear models. We evaluate the performance of each method via a case study of accounts from 236 health organizations during the 2014 Ebola outbreak. For accurate interpolation and extrapolation, we find that negative binomial regression, modeled separately for each account, using time as an interval variable, accurately recovers missing values while retaining narrow prediction intervals.

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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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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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