Modified Mixed Generalized Ordered Response Model to Handle Misclassification in Injury Severity Data

2018 ◽  
Vol 2672 (30) ◽  
pp. 53-63 ◽  
Author(s):  
Lacramioara Balan ◽  
Rajesh Paleti
Keyword(s):  
Injury Severity ◽  
Random Variable ◽  
Parameter Estimates ◽  
Discrete Random Variable ◽  
Response Models ◽  
Ordinal Response ◽  
Proposed Model ◽  
Misclassification Errors ◽  
Misclassification Rates ◽  
Ordered Response

Traditional crash databases that record police-reported injury severity data are prone to misclassification errors. Ignoring these errors in discrete ordered response models used for analyzing injury severity can lead to biased and inconsistent parameter estimates. In this study, a mixed generalized ordered response (MGOR) model that quantifies misclassification rates in the injury severity variable and adjusts the bias in parameter estimates associated with misclassification was developed. The proposed model does this by considering the observed injury severity outcome as a realization from a discrete random variable that depends on true latent injury severity that is unobservable to the analyst. The model was used to analyze misclassification rates in police-reported injury severity in the 2014 General Estimates System (GES) data. The model found that only 68.23% and 62.75% of possible and non-incapacitating injuries were correctly recorded in the GES data. Moreover, comparative analysis with the MGOR model that ignores misclassification not only has lower data fit but also considerable bias in both the parameter and elasticity estimates. The model developed in this study can be used to analyze misclassification errors in ordinal response variables in other empirical contexts.

Modeling of unforced demand response programs

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Hassan Jalili ◽  
Pierluigi Siano
Keyword(s):  
Demand Response ◽  
Load Shifting ◽  
Response Models ◽  
Temperature Changes ◽  
Proposed Model ◽  
Equipment Failures ◽  
Demand Response Programs ◽  
Simultaneous Implementation ◽  
Long Term Studies

Abstract Demand response programs are useful options in reducing electricity price, congestion relief, load shifting, peak clipping, valley filling and resource adequacy from the system operator’s viewpoint. For this purpose, many models of these programs have been developed. However, the availability of these resources has not been properly modeled in demand response models making them not practical for long-term studies such as in the resource adequacy problem where considering the providers’ responding uncertainties is necessary for long-term studies. In this paper, a model considering providers’ unavailability for unforced demand response programs has been developed. Temperature changes, equipment failures, simultaneous implementation of demand side management resources, popular TV programs and family visits are the main reasons that may affect the availability of the demand response providers to fulfill their commitments. The effectiveness of the proposed model has been demonstrated by numerical simulation.

scholarly journals A note on characteristic function for Bernstein polynomials involving special numbers and polynomials

Filomat ◽  
2020 ◽  
Vol 34 (2) ◽  
pp. 543-549
Author(s):  
Buket Simsek
Keyword(s):  
Characteristic Function ◽  
Binomial Distribution ◽  
Bernstein Polynomials ◽  
Random Variable ◽  
Stirling Numbers ◽  
Bernoulli Numbers ◽  
Discrete Random Variable ◽  
Euler Numbers ◽  
Negative Order ◽  
Special Numbers And Polynomials

The aim of this present paper is to establish and study generating function associated with a characteristic function for the Bernstein polynomials. By this function, we derive many identities, relations and formulas relevant to moments of discrete random variable for the Bernstein polynomials (binomial distribution), Bernoulli numbers of negative order, Euler numbers of negative order and the Stirling numbers.

scholarly journals A Single Valued Neutrosophic Inventory Model with Neutrosophic Random Variable

2020 ◽  
pp. 52-63
Author(s):  
M. Mullai*, K. Sangeetha, R. Surya, G. Madhan kumar, R. Jeyabalan ◽  
◽  
◽  
S. Broumi
Keyword(s):  
Random Variables ◽  
Random Variable ◽  
Inventory Model ◽  
Newsvendor Problem ◽  
New Method ◽  
Optimal Sequence ◽  
Average Value ◽  
Proposed Model

This paper presents the problematic period of neutrosophic inventory in an inaccurate and unsafe mixed environment. The purpose of this paper is to present demand as a neutrosophic random variable. For this model, a new method is developed for determining the optimal sequence size in the presence of neutrosophic random variables. Where to get optimality by gradually expressing the average value of integration. The newsvendor problem is used to describe the proposed model.

scholarly journals Blossoming contusions: identifying factors contributing to the expansion of traumatic intracerebral hemorrhage

2018 ◽  
Vol 129 (5) ◽  
pp. 1305-1316 ◽  
Author(s):  
Joseph A. Carnevale ◽  
David J. Segar ◽  
Andrew Y. Powers ◽  
Meghal Shah ◽  
Cody Doberstein ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Glasgow Coma Scale ◽  
Injury Severity Score ◽  
Injury Severity ◽  
Expansion Rate ◽  
Blood Alcohol ◽  
Patient Disposition ◽  
Ordinal Response ◽  
Admission Glasgow Coma Scale

OBJECTIVETraumatic brain injury (TBI) remains a significant cause of neurological morbidity and mortality. Each year, more than 1.7 million patients present to the emergency department with TBI. The goal of this study was to evaluate the prognosis of traumatic cerebral intraparenchymal hemorrhage (tIPH), to develop subclassifications of these injuries that relate to prognosis, and to provide a more comprehensive assessment of hemorrhagic progression contusion (HPC) by analyzing the rate at which tIPH “blossom” (i.e., expansion), depending on a variety of intrinsic and modifiable factors.METHODSIn this retrospective study, 726 patients (age range 0–100 years) were admitted to a level 1 trauma center with tIPH during an 8-year period (2005–2013). Of these patients, 491 underwent both admission and follow-up head CT (HCT) within 72 hours. The change in tIPH volume over time, the expansion rate, was recorded for all 491 patients. Effects of prehospital and in-hospital variables were examined using ordinal response logistic regression analyses. These variables were further examined using multivariate linear regression analysis to accurately predict the extent to which a hemorrhage will progress.RESULTSOf the 491 (67.6%) patients who underwent both admission and follow-up HCT, 368 (74.9%) patients experienced HPC. These hemorrhages expanded on average by 61.6% (4.76 ml) with an average expansion rate of 0.71 ml per hour. On univariate analysis, certain patient characteristics were significantly (p < 0.05) related to HPC, including age (> 60 years), admission Glasgow Coma Scale score, blood alcohol level, international normalized ratio, absolute platelet count, transfusion of platelets, concomitant anticoagulation and antiplatelet medication, the initial tIPH volume on admission HCT, and ventriculostomy. Increased expansion rate was significantly associated with patient disposition to hospice or death (p < 0.001). To determine which factors most accurately predict overall patient disposition, an ordinal-response logistic regression identified systolic blood pressure, Injury Severity Score, admission Glasgow Coma Scale score, follow-up scan volume, transfusion of platelets, and ventriculostomy as predictors of patient discharge disposition following tIPH. A multivariate logistic regression identified several prehospital and in-hospital variables (age, Injury Severity Score, blood alcohol level, initial scan volume, concomitant epidural hematoma, presence of subarachnoid hemorrhage, transfusion of platelets, and ventriculostomy) that predicted the volumetric expansion of tIPH. Among these variables, the admission tIPH volume by HCT proved to be the factor most predictive of HPC.CONCLUSIONSSeveral factors contribute to the rate at which traumatic cerebral contusions blossom in the acute posttraumatic period. Identifying the intrinsic and modifiable aspects of cerebral contusions can help predict the rate of expansion and highlight potential therapeutic interventions to improve TBI-associated morbidity and mortality.

Multilevel Zero-inflated Censored Beta Regression Modeling for Proportions and Rate Data with Extra-zeros

2019 ◽  
Author(s):  
Leili Tapak ◽  
Omid Hamidi ◽  
Majid Sadeghifar ◽  
Hassan Doosti ◽  
Ghobad Moradi
Keyword(s):  
Regression Model ◽  
Simulation Study ◽  
Real Data ◽  
P Value ◽  
Parameter Estimates ◽  
Beta Regression ◽  
Rate Data ◽  
Data Set ◽  
Proposed Model ◽  
Beta Regression Model

Abstract Objectives Zero-inflated proportion or rate data nested in clusters due to the sampling structure can be found in many disciplines. Sometimes, the rate response may not be observed for some study units because of some limitations (false negative) like failure in recording data and the zeros are observed instead of the actual value of the rate/proportions (low incidence). In this study, we proposed a multilevel zero-inflated censored Beta regression model that can address zero-inflation rate data with low incidence.Methods We assumed that the random effects are independent and normally distributed. The performance of the proposed approach was evaluated by application on a three level real data set and a simulation study. We applied the proposed model to analyze brucellosis diagnosis rate data and investigate the effects of climatic and geographical position. For comparison, we also applied the standard zero-inflated censored Beta regression model that does not account for correlation.Results Results showed the proposed model performed better than zero-inflated censored Beta based on AIC criterion. Height (p-value <0.0001), temperature (p-value <0.0001) and precipitation (p-value = 0.0006) significantly affected brucellosis rates. While, precipitation in ZICBETA model was not statistically significant (p-value =0.385). Simulation study also showed that the estimations obtained by maximum likelihood approach had reasonable in terms of mean square error.Conclusions The results showed that the proposed method can capture the correlations in the real data set and yields accurate parameter estimates.

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