scholarly journals مقارنة طرائق تقدير معلمات أنموذج انحدار ثنائي الحدين السالب في ظل وجود مشكلة التعدد الخطي باستعمال المحاكاة

2019 ◽  
Vol 25 (110) ◽  
pp. 466
Author(s):  
سهيل نجم عبود ◽  
ايناس صلاح خورشيد
Keyword(s):  
Maximum Likelihood ◽  
Regression Model ◽  
Ridge Regression ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Liu Estimator ◽  
Binomial Regression

ناقش هذا البحث مقدر متحيز لأنموذج انحدار ثنائي الحدين السالب (Negative Binomial Regression Model) ومعرف بالمقدر ليو(Liu Estimator)، اذ استعمل هذا المقدر لتقليل التباين والتغلب على مشكلة التعدد الخطي بين المتغيرات التوضيحية، كما تم استخدام بعض التقديرات منها مقدر انحدار الحرف (Ridge Regression) ومقدر الامكان الاعظم (Maximum Likelihood)، اذ يهدف هذا البحث الى المقارنات النظرية بين مقدر (Liu Estimator) ومقدرات الامكان الاعظم (Maximum Likelihood) وانحدار الحرف (Ridge Regression) باستخدام معيار متوسط مربعات الخطأ (MSE)، اذ يكون تباين مقدر الامكان الاعظم (MLE) متضخم في ظل وجود مشكلة التعدد الخطي بين المتغيرات التوضيحية، وتم في هذا البحث تصميم المحاكاة (مونت كارلوا) لتقييم اداء المقدرات باستخدام معيار مقارنة متوسط مربعات الخطأ (MSE)، حيث اظهرت نتائج المحاكاة اهمية مقدر ليو وتفوقها على مقدري انحدار الحرف (RR) والامكان الاعظم (MLE) عندما يكون عدد المتغيرات التوضيحية (p=5)  ولحجم العينة (n=100)، اما عندما يكون عدد المتغيرات التوضيحية (p=3) ولكافة الحجوم، وكذلك عندما (p=5) ولكافة الحجوم ماعدا حجم العينة (n=100) طريقة انحدار الحرفRR  هي الافضل.  

scholarly journals مقارنة بين طرائق انحدار الحرف ونوع ليو في تقدير معلمات أنموذج انحدار ثنائي الحدين السالب في ظل وجود مشكلة التعدد الخطي باستخدام المحاكاة

2018 ◽  
Vol 24 (109) ◽  
pp. 515
Author(s):  
سهيل نجم عبود ◽  
ايناس صلاح خورشيد
Keyword(s):  
Regression Model ◽  
Ridge Regression ◽  
Count Data ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Regression Estimator ◽  
Binomial Regression ◽  
Ridge Regression Estimator

ان مشكلة  التعدد الخطي من المشاكل الشائعة والتي تتعامل الى حد كبير مع الارتباط الداخلي  بين المتغيرات التوضيحية وتظهر هذه المشكلة خصوصا في الاقتصاد والبحوث التطبيقية، ويكون لمشكلة التعدد الخطي تاثير سلبي على أنموذج الانحدار مثل وجود درجة تباين متضخم وتقدير معلمات تكون غير مستقرة عندما نستخدم مقدرات المربعات الصغرى الاعتيادية (OLS) ، لهذا تم اللجوء الى استخدام طرائق اخرى لتقدير معلمات أنموذج ثنائي الحدين السالب منها طريقة مقدر انحدار الحرف ومقدر نوع ليو، ويعتبر أنموذج  انحدار ثنائي الحدين السالب (Negative Binomial Regression Model) كأنموذج انحدار غير خطي او كجزء من العائلة الاسية المعممة و هذا ألانموذج  الهيكل الاساسي لتحليل بيانات العد (Count Data) و الذي استخدم كبديل لنموذج بواسون عندما تكون هناك مشكلة فوق التشتت (Overdisperison)  اي عندما تكون قيمة تباين متغير الاستجابة (Y) اكبر من وسطه الحسابي ، وتم تصميم دراسة محاكاة مونت كارلوا للمقارنة بين طريقتي تقدير انحدار الحرف (Ridge Regression Estimator) ومقدر نوع ليو (Liu Type Estimator) من خلال استخدام معيار مقارنة متوسط مربعات الخطأ (MSE)، حيث بينت نتيجة المحاكاة ان طريقة مقدر نوع ليو هي افضل من طريقة مقدر انحدار الحرف  اذ جاءت متوسط مربعات الخطأ لها اقل في صيغته التقديرية الثالثة والرابعة .

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 Approaching precision public health by automated syndromic surveillance in communities

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0254479
Author(s):  
Ta-Chien Chan ◽  
Jia-Hong Tang ◽  
Cheng-Yu Hsieh ◽  
Kevin J. Chen ◽  
Tsan-Hua Yu ◽  
...  
Keyword(s):  
Regression Model ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Health Department ◽  
Negative Binomial Regression Model ◽  
Negatively Associated ◽  
Pearson Residuals ◽  
Binomial Regression ◽  
Taipei City ◽  
Icd 10

Background Sentinel physician surveillance in communities has played an important role in detecting early signs of epidemics. The traditional approach is to let the primary care physician voluntarily and actively report diseases to the health department on a weekly basis. However, this is labor-intensive work, and the spatio-temporal resolution of the surveillance data is not precise at all. In this study, we built up a clinic-based enhanced sentinel surveillance system named “Sentinel plus” which was designed for sentinel clinics and community hospitals to monitor 23 kinds of syndromic groups in Taipei City, Taiwan. The definitions of those syndromic groups were based on ICD-10 diagnoses from physicians. Methods Daily ICD-10 counts of two syndromic groups including ILI and EV-like syndromes in Taipei City were extracted from Sentinel plus. A negative binomial regression model was used to couple with lag structure functions to examine the short-term association between ICD counts and meteorological variables. After fitting the negative binomial regression model, residuals were further rescaled to Pearson residuals. We then monitored these daily standardized Pearson residuals for any aberrations from July 2018 to October 2019. Results The results showed that daily average temperature was significantly negatively associated with numbers of ILI syndromes. The ozone and PM2.5 concentrations were significantly positively associated with ILI syndromes. In addition, daily minimum temperature, and the ozone and PM2.5 concentrations were significantly negatively associated with the EV-like syndromes. The aberrational signals detected from clinics for ILI and EV-like syndromes were earlier than the epidemic period based on outpatient surveillance defined by the Taiwan CDC. Conclusions This system not only provides warning signals to the local health department for managing the risks but also reminds medical practitioners to be vigilant toward susceptible patients. The near real-time surveillance can help decision makers evaluate their policy on a timely basis.

scholarly journals Expressway Traffic Accidents Involving Human Injuries and Fatalities in Ethiopia: Negative Binomial Regression Model

2021 ◽  
Author(s):  
Yesuf Abdela Mustefa ◽  
Addis Belayhun
Keyword(s):  
Regression Model ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Negative Binomial ◽  
Road Traffic ◽  
Weather Condition ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Level Of Education ◽  
Binomial Regression

Abstract Background: Road traffic accident is a major public health as well as economic challenge that rated the eighth leading cause of death. The severity became higher in developing countries. Ethiopian is among the most confronted countries in the world. We utilized the Ethiopian Toll Roads Enterprise data to provide insights and model significant determinants of accidents involving injuries and fatalities. Besides utilizing recent dataset, we applied the most appropriate but forwent statistical model. Moreover, we examined the significance of the effects of drivers’ age and gender that have not been the cases in the literatures.Methods: We made descriptive insights available on the basis of graphs from integrated traffic accident and flow datasets. We tested for the presence of over-dispersion in a total of 1824 observations of accident data recorded from September, 2014 to December, 2019 for inferential analysis. Finally, we modeled the effects of significant variables on the number of injuries using the negative binomial regression model. Results: we found that the number of injuries in accidents were significantly determined by type of vehicles, ownership status of vehicles, accident time weather condition, driver-vehicle relationship, drivers’ level of education, and drivers’ age.Conclusions: Heavy trucks were more likely to cause more number of injuries than medium or small vehicles. Hot and windy weather conditions were associated with higher probability of the number of injuries. The likelihood of the number of injuries were lower when drivers are owner of the vehicle; drivers level of education is above secondary school; and the age of the driver is between 18 and 23 years old. Moreover, due concern needs to be given for traffic road rules.

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