Heterogeneity and Number of Export Destinations of Italian Firms: A Hurdle Negative Binomial Regression Approach

2013 ◽  
Vol 13 (03n04) ◽  
pp. 391-416 ◽  
Author(s):  
Maria Rosaria Ferrante ◽  
Marco Novelli
Keyword(s):  
Count Data ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Outcome Variable ◽  
Export Activity ◽  
Multiple Markets ◽  
Binomial Regression ◽  
Regression Approach ◽  
Excess Of Zeros ◽  
Hurdle Regression

This article addresses on an aspect of firms internationalization so far little explored, the choice of the number of export destinations and a proxy of the complexity of the export activity. As the outcome variable is a count with an excess of zeros, we use a hurdle regression model for count data that also allow disentangling the aspect of heterogeneity related to the decision to export from those measuring the number of markets served. Some differences arise by the comparison between the estimates regarding the propensity to export model and those of the model describing the number of export destinations. Regarding the propensity to export, the estimated models support the familiar evidences already presented in literature: exporters are larger, more productive, more innovative and invest more. With reference to the number of export destinations, it seems that not only the larger the number of markets served the more productive, large and willing to invest is the firm but also firms engaged in multiple markets seem to be older, financially stable, and willing to support organizational and managerial innovations.

scholarly journals Statistical models for analyzing count data: predictors of length of stay among HIV patients in Portugal using a multilevel model

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ahmed Nabil Shaaban ◽  
Bárbara Peleteiro ◽  
Maria Rosario O. Martins
Keyword(s):  
Length Of Stay ◽  
Regression Model ◽  
Random Effects ◽  
Count Data ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Comprehensive Approach ◽  
Negative Binomial Regression Model ◽  
Hiv Patients ◽  
Binomial Regression

Abstract Background This study offers a comprehensive approach to precisely analyze the complexly distributed length of stay among HIV admissions in Portugal. Objective To provide an illustration of statistical techniques for analysing count data using longitudinal predictors of length of stay among HIV hospitalizations in Portugal. Method Registered discharges in the Portuguese National Health Service (NHS) facilities Between January 2009 and December 2017, a total of 26,505 classified under Major Diagnostic Category (MDC) created for patients with HIV infection, with HIV/AIDS as a main or secondary cause of admission, were used to predict length of stay among HIV hospitalizations in Portugal. Several strategies were applied to select the best count fit model that includes the Poisson regression model, zero-inflated Poisson, the negative binomial regression model, and zero-inflated negative binomial regression model. A random hospital effects term has been incorporated into the negative binomial model to examine the dependence between observations within the same hospital. A multivariable analysis has been performed to assess the effect of covariates on length of stay. Results The median length of stay in our study was 11 days (interquartile range: 6–22). Statistical comparisons among the count models revealed that the random-effects negative binomial models provided the best fit with observed data. Admissions among males or admissions associated with TB infection, pneumocystis, cytomegalovirus, candidiasis, toxoplasmosis, or mycobacterium disease exhibit a highly significant increase in length of stay. Perfect trends were observed in which a higher number of diagnoses or procedures lead to significantly higher length of stay. The random-effects term included in our model and refers to unexplained factors specific to each hospital revealed obvious differences in quality among the hospitals included in our study. Conclusions This study provides a comprehensive approach to address unique problems associated with the prediction of length of stay among HIV patients in Portugal.

Systemic Determinants in Surgery: Nonclinical Factors Affecting Time to Operation for Incarcerated Ventral Hernias

2021 ◽  
pp. 000313482110111
Author(s):  
David E. Wang ◽  
Paul J. Chung ◽  
Rafael Barrera ◽  
Gene F. Coppa ◽  
Antonio E. Alfonso ◽  
...  
Keyword(s):  
Operating Room ◽  
Negative Binomial ◽  
Private Insurance ◽  
Negative Binomial Regression ◽  
Outcome Variable ◽  
Hospital Size ◽  
Factors Affecting ◽  
Binomial Regression ◽  
Comorbidity Status ◽  
Ventral Hernias

Introduction We explore nonclinical factors affecting the amount of time from admission to the operating room for patients requiring nonelective repair of ventral hernias. Methods Using the 2005-2012 Nationwide Inpatient Sample, we identified adult patients with a primary diagnosis of ventral hernia without obstruction/gangrene, who underwent nonelective repair. The outcome variable of interest was time from admission to surgery. We performed univariate and multivariable analyses using negative binomial regression, adjusting for age, sex, race, income, insurance, admission day, comorbidity status (van Walraven score), diagnosis, procedure, hospital size, location/teaching status, and region. Results 7,253 patients met criteria, of which majority were women (n = 4,615) and white (n = 5,394). The majority of patients had private insurance (n = 3,015) followed by Medicare (n = 2,737). Median time to operation was 0 days. Univariate analysis comparing operation <1 day to ≥1 day identified significant differences in race, day of admission, insurance, length of stay, comorbidity status, hospital location, type, and size. Negative binomial regression showed that weekday admission (IRR 4.42, P < .0001), private insurance (IRR 1.53-2.66, P < .0001), rural location (IRR 1.39-1.76, P < .01), small hospital size (IRR 1.26-1.36, P < .05), white race (IRR 1.30-1.34, P < .01), healthier patients (van Walraven score IRR 1.05, P < .0001), and use of mesh (IRR 0.39-0.56, P < .02) were associated with shorter time until procedure. Conclusion Shorter time from admission to the operating room was associated with several nonclinical factors, which suggest disparities may exist. Further prospective studies are warranted to elucidate these disparities affecting patient care.

Managing Inflation

2016 ◽  
Vol 63 (1) ◽  
pp. 77-87 ◽  
Author(s):  
William H. Fisher ◽  
Stephanie W. Hartwell ◽  
Xiaogang Deng
Keyword(s):  
Count Data ◽  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Excess Zeros ◽  
Binomial Regression ◽  
Negative Binomial Models ◽  
Using Data ◽  
Over Dispersion ◽  
Binomial 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.

PEMODELAN DENGAN GEOGRAPHICALLY WEIGHTED NEGATIVE BINOMIAL REGRESSION (Studi kasus: Banyaknya Penderita Kusta di Jawa Barat)

2021 ◽  
Vol 10 (3) ◽  
pp. 226-236
Author(s):  
Khusnul Khotimah ◽  
Itasia Dina Sulvianti ◽  
Pika Silvianti
Keyword(s):  
Regression Model ◽  
Count Data ◽  
Poisson Regression ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Kernel Weight ◽  
Negative Binomial Regression Model ◽  
West Java ◽  
Binomial Regression ◽  
Spatial Heterogenity

The number of leper in West Java is an example of the count data case. The analyzes commonly used in count data is Poisson regression. This research will determine the variables that influence the number of leper in West Java. The data used is the number of leper in West Java in 2019. This data has an overdispersion condition and spatial heterogenity. To handle overdispersion, the negative binomial regression model can be employed. While spatial heterogenity is overcome by adding adaptive bisquare kernel weight. This research resulted Geographically Weighted Negative Binomial Regression (GWNBR) with a weighting adaptive bisquare kernel classifies regency/city in West Java into ten groups based on the variables that sigfinicantly influence the number of leper. In general, the variable in the percentage of households with Clean and Healthy Behavior (PHBS) has a significant effect in all regency/city in West Java. Especially for Bogor Regency, Depok City, Bogor City, and Pangandaran Regency, the variable of the percentage of people poverty does not have a significant effect on the number leper.

scholarly journals ComBat-seq: batch effect adjustment for RNA-seq count data

2020 ◽  
Vol 2 (3) ◽  
Author(s):  
Yuqing Zhang ◽  
Giovanni Parmigiani ◽  
W Evan Johnson
Keyword(s):  
Differential Expression ◽  
Count Data ◽  
Statistical Power ◽  
Negative Binomial ◽  
Genomic Data ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Rna Seq ◽  
Batch Effects ◽  
Binomial Regression

Abstract The benefit of integrating batches of genomic data to increase statistical power is often hindered by batch effects, or unwanted variation in data caused by differences in technical factors across batches. It is therefore critical to effectively address batch effects in genomic data to overcome these challenges. Many existing methods for batch effects adjustment assume the data follow a continuous, bell-shaped Gaussian distribution. However in RNA-seq studies the data are typically skewed, over-dispersed counts, so this assumption is not appropriate and may lead to erroneous results. Negative binomial regression models have been used previously to better capture the properties of counts. We developed a batch correction method, ComBat-seq, using a negative binomial regression model that retains the integer nature of count data in RNA-seq studies, making the batch adjusted data compatible with common differential expression software packages that require integer counts. We show in realistic simulations that the ComBat-seq adjusted data results in better statistical power and control of false positives in differential expression compared to data adjusted by the other available methods. We further demonstrated in a real data example that ComBat-seq successfully removes batch effects and recovers the biological signal in the data.

Using Poisson Class Regression To Analyze Count Data in Correctional and Forensic Psychology

2007 ◽  
Vol 34 (12) ◽  
pp. 1659-1674 ◽  
Author(s):  
Glenn D. Walters
Keyword(s):  
Count Data ◽  
Negative Binomial ◽  
Forensic Psychology ◽  
Poisson Model ◽  
Negative Binomial Regression ◽  
Ordinary Least Squares ◽  
Statistical Procedure ◽  
Least Squares Regression ◽  
Binomial Regression ◽  
Censored Models

The benchmark model for count data is the Poisson distribution, and the standard statistical procedure for analyzing count data is Poisson regression. However, highly restrictive assumptions lead to frequent misspecification of the Poisson model. Alternate approaches, such as negative binomial regression, zero modified procedures, and truncated and censored models are consequently required to handle count data in many social science contexts. Empirical examples from correctional and forensic psychology are provided to illustrate the importance of replacing ordinary least squares regression with Poisson class procedures in situations when count data are analyzed.

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