scholarly journals Freeway Incident Frequency Analysis Based on CART Method

2014 ◽  
Vol 26 (3) ◽  
pp. 191-199 ◽  
Author(s):  
Xuecai Xu ◽  
Željko Šarić ◽  
Ahmad Kouhpanejade
Keyword(s):  
Regression Model ◽  
Influencing Factors ◽  
Negative Binomial ◽  
Classification And Regression Tree ◽  
Future Research ◽  
Negative Binomial Regression Model ◽  
Classification And Regression ◽  
The Impact ◽  
The Relationship ◽  
Incident Frequency

Classification and Regression Tree (CART), one of the most widely applied data mining techniques, is based on the classification and regression model produced by binary tree structure. Based on CART method, this paper establishes the relationship between freeway incident frequency and roadway characteristics, traffic variables and environmental factors. The results of CART method indicate that the impact of influencing factors (weather, weekday/weekend, traffic flow and roadway characteristics) of incident frequency is not consistent for different incident types during different time periods. By comparing with Negative Binomial Regression model, CART method is demonstrated to be a good alternative method for analyzing incident frequency. Then the discussion about the relationship between incident frequency and influencing factors is provided, and the future research orientation is pointed out.

scholarly journals Geographically Weighted Negative Binomial Regression Model Predicts Wildfire Occurrence in the Great Xing’an Mountains Better Than Negative Binomial Model

Forests ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 377 ◽  
Author(s):  
Zhangwen Su ◽  
Haiqing Hu ◽  
Mulualem Tigabu ◽  
Guangyu Wang ◽  
Aicong Zeng ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Stable Model ◽  
Wildfire Occurrence ◽  
Great Xing’An Mountains ◽  
Binomial Regression ◽  
The Impact ◽  
The Relationship ◽  
Better Than

Wildfire is a major disturbance that affects large area globally every year. Thus, a better prediction of the likelihood of wildfire occurrence is essential to develop appropriate fire prevention measures. We applied a global negative Binomial (NB) and a geographically weighted negative Binomial regression (GWNBR) models to determine the relationship between wildfire occurrence and its drivers factors in the boreal forests of the Great Xing’an Mountains, northeast China. Using geo-weighted techniques to consider the geospatial information of meteorological, topographic, vegetation type and human factors, we aimed to verify whether the performance of the NB model can be improved. Our results confirmed that the model fitting and predictions of GWNBR model were better than the global NB model, produced more precise and stable model parameter estimation, yielded a more realistic spatial distribution of model predictions, and provided the detection of the impact hotpots of these predictor variables. We found slope, vegetation cover, average precipitation, average temperature, and average relative humidity as important predictors of wildfire occurrence in the Great Xing’an Mountains. Thus, spatially differing relations improves the explanatory power of the global NB model, which does not explain sufficiently the relationship between wildfire occurrence and its drivers. Thus, the GWNBR model can complement the global NB model in overcoming the issue of nonstationary variables, thereby enabling a better prediction of the occurrence of wildfires in large geographical areas and improving management practices of wildfire.

Negative Binomial regression model for analysis of the relationship between hospitalization and air pollution

2018 ◽  
Vol 9 (2) ◽  
pp. 333-341 ◽  
Author(s):  
Leda G. Ardiles ◽  
Yara S. Tadano ◽  
Silvano Costa ◽  
Viviana Urbina ◽  
Maurício N. Capucim ◽  
...  
Keyword(s):  
Air Pollution ◽  
Regression Model ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Binomial Regression ◽  
The Relationship

THE MODEL FOR MANAGING INVESTMENTS IN THE MOSCOW ENVIRONMENT BASED ON A POWER REGRESSION EQUATION

2020 ◽  
Vol 2 (7) ◽  
pp. 91-99
Author(s):  
E. V. KOSTYRIN ◽  
◽  
M. S. SINODSKAYA ◽  
Keyword(s):  
Solid Waste ◽  
Influencing Factors ◽  
Pair Correlation ◽  
Approximation Error ◽  
Regression Equation ◽  
Regression Equations ◽  
The Impact ◽  
The Relationship ◽  
Average Approximation ◽  
Average Approximation Error

The article analyzes the impact of certain factors on the volume of investments in the environment. Regression equations describing the relationship between the volume of investment in the environment and each of the influencing factors are constructed, the coefficients of the Pearson pair correlation between the dependent variable and the influencing factors, as well as pairwise between the influencing factors, are calculated. The average approximation error for each regression equation is determined. A correlation matrix is constructed and a conclusion is made. The developed econometric model is implemented in the program of separate collection of municipal solid waste (MSW) in Moscow. The efficiency of the model of investment management in the environment is evaluated on the example of the growth of planned investments in the activities of companies specializing in the export and processing of solid waste.

scholarly journals Evaluating the Relationship Between Initial Injury, Referral to A Pain Clinic, and Medical Retirement from the Army: A Retrospective Analysis

2021 ◽  
Vol 186 (Supplement_1) ◽  
pp. 502-505
Author(s):  
Justin J Stewart ◽  
Diane Flynn ◽  
Alana D Steffen ◽  
Dale Langford ◽  
Honor McQuinn ◽  
...  
Keyword(s):  
Chronic Pain ◽  
Retrospective Analysis ◽  
Extended Period ◽  
Pain Clinic ◽  
Outcome Variable ◽  
Future Research ◽  
Curvilinear Relationship ◽  
The Impact ◽  
Pain Care ◽  
The Relationship

ABSTRACT Introduction Soldiers are expected to deploy worldwide and must be medically ready in order to accomplish their mission. Soldiers unable to deploy for an extended period of time because of chronic pain or other conditions undergo an evaluation for medical retirement. A retrospective analysis of existing longitudinal data from an Interdisciplinary Pain Management Center (IPMC) was used to evaluate the temporal relationship between the time of initial duty restriction and referral for comprehensive pain care to being evaluated for medical retirement. Methods Patients were adults (>18 years old) and were cared for in an IPMC at least once between May 1, 2014 and February 28, 2018. A total of 1,764 patients were included in the final analysis. Logistic regression was used to evaluate the impact of duration between date of first duty restriction documentation and IPMC referral to the outcome variable of establishment of a permanent 3 (P3) profile. Results The duration between date of first duty restriction and IPMC referral showed a curvilinear relationship to probability of a P3 profile. According to our model, a longer duration before referral is associated with an increased probability of a subsequent P3 profile with the highest probability peaking at 19 months. The probability of P3 declines gradually for those who were referred later. Discussion This is the first time the relationship between time of initial duty restriction, referral to an IPMC, and subsequent P3 or higher profile has been tested. Future research is needed to examine medical conditions listed on the profile to see how they might contribute to the cause of referral to the IPMC. Conclusion A longer duration between initial duty restriction and referral to IPMC was associated with higher odds of subsequent P3 status for up to 19 months. Referral to an IPMC for comprehensive pain care early in the course of chronic pain conditions may reduce the likelihood of P3 profile and eventual medical retirement of soldiers.

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.

scholarly journals Association Between Federal Value-Based Incentive Program Implementation and Hospital-Onset C. difficile Infection Rates

2020 ◽  
Vol 41 (S1) ◽  
pp. s133-s133
Author(s):  
Mohammad Alrawashdeh ◽  
Chanu Rhee ◽  
Heather Hsu ◽  
Grace Lee
Keyword(s):  
Program Implementation ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Neutralization Assay ◽  
Nucleic Acid Amplification ◽  
Teaching Hospitals ◽  
Future Research ◽  
Testing Methods ◽  
Toxigenic Culture ◽  
The Impact

Background: The Hospital-Acquired Conditions Reduction Program (HACRP) and Hospital Value-Based Purchasing (HVBP) are federal value-based incentive programs that financially reward or penalize hospitals based on quality metrics. Hospital-onset C. difficile infection (HO-CDI) rates reported to the CDC NHSN became a target quality metric for both HACRP and HVBP in October 2016, but the impact of these programs on HO-CDI rates is unknown. Methods: We used an interrupted time-series design to examine the association between HACRP/HVBP implementation in October 2016 and quarterly rates of HO-CDI per 10,000 patient days among incentive-eligible acute-care hospitals conducting facility-wide HO-CDI NHSN surveillance between January 2013 and March 2019. Generalized estimating equations were used to fit negative binomial regression models to assess for immediate program impact (ie, level change) and changes in the slope of HO-CDI rates, controlling for each hospital’s predominant method for CDI testing (nucleic acid amplification including PCR (NAAT), enzyme immunoassay for toxin (EIA), or other testing method including cell cytotoxicity neutralization assay and toxigenic culture). Results: Of the 265 study hospitals studied, most were medium-sized (100–399 beds, 55%), not-for-profit (77%), teaching hospitals (70%), and were located in a metropolitan area (87%). Compared to EIA, rates of HO-CDI were higher when detected by NAAT (incidence rate ratio [IRR], 1.55; 95% CI, 1.41–1.70) or other testing methods (IRR, 1.47; 95% CI, 1.26–1.71). Controlling for CDI testing methods, HACRP/HVBP implementation was associated with an immediate 6% decline in HO-CDI rates (IRR, 0.94; 95% CI, 0.89–0.99) and a 4% decline in slope per year-quarter thereafter (IRR, 0.96; 95% CI, 0.95–0.97) (Fig. 1). Conclusions: HACRP/HVBP implementation was associated with both immediate and gradual improvements in HO-CDI rates, independent of CDI testing methods of differing sensitivity. Future research may evaluate the precise mechanisms underlying this improvement and if this impact is sustained in the long term.Funding: NoneDisclosures: None

The Relationship between Mentoring Functions and Employee Performance: Mediating Effects of Protégé Relational Self-Efficacy

2021 ◽  
Vol 2 (2) ◽  
pp. 187-204
Author(s):  
Muhammad Shoukat Malik ◽  
Muhammad Kashif Nawaz
Keyword(s):  
Self Efficacy ◽  
Employee Performance ◽  
Mediating Effect ◽  
Citizenship Behavior ◽  
Future Research ◽  
Relational Self ◽  
Mentoring Functions ◽  
Conventional Banks ◽  
The Impact ◽  
The Relationship

Organizational scholars concurred that positive workplace relationships with others can helps employee to gain from these relationships but, they lack insights into how or why this occurs. Moreover, the relationship dynamics focus on what the relationships provide without considering the how these relationships initiated, builds and maintains. To line of this, the current study aims to find the impact of mentoring functions (career, psychosocial, role modeling) and employee performance (career success, organization citizenship behavior, and job performance) via mediating effect of relational self-efficacy. For this purpose, the data were gathered from 310 branch banking employees of Pakistani conventional banks. PLS-SEM was used for data analysis. The results indicate that there is direct relationship between mentoring functions and employee’s performance. Moreover, the finding also shows that employee relational self-efficacy mediates the relationship between mentoring functions and employee performance. Theoretical and practical implications are discussed along with suggestions for future research.

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