scholarly journals A Combined PLS and Negative Binomial Regression Model for Inferring Association Networks from Next-Generation Sequencing Count Data

2018 ◽  
Vol 15 (3) ◽  
pp. 760-773 ◽  
Author(s):  
Maiju Pesonen ◽  
Jaakko Nevalainen ◽  
Steven Potter ◽  
Somnath Datta ◽  
Susmita Datta
Keyword(s):  
Next Generation Sequencing ◽  
Regression Model ◽  
Count Data ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Next Generation ◽  
Binomial Regression ◽  
Generation Sequencing

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.

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.

Endogenous switching regression model and treatment effects of count-data outcome

2020 ◽  
Vol 20 (3) ◽  
pp. 627-646
Author(s):  
Takuya Hasebe
Keyword(s):  
Regression Model ◽  
Count Data ◽  
Treatment Effects ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Switching Regression ◽  
Latent Heterogeneity ◽  
Endogenous Switching ◽  
Binomial Regression

In this article, I describe the escount command, which implements the estimation of an endogenous switching model with count-data outcomes, where a potential outcome differs across two alternate treatment statuses. escount allows for either a Poisson or a negative binomial regression model with lognormal latent heterogeneity. After estimating the parameters of the switching regression model, one can estimate various treatment effects with the command teescount. I also describe the command lncount, which fits the Poisson or negative binomial regression model with lognormal latent heterogeneity.

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 ANALISIS REGRESI COUNT DATA UNTUK PEMODELAN JUMLAH KASUS PENYAKIT TUBERKULOSIS DI KABUPATEN BANYUMAS

2021 ◽  
Vol 13 (2) ◽  
pp. 57
Author(s):  
Kristy Kristy ◽  
Jajang Jajang ◽  
Nunung Nurhayati
Keyword(s):  
Regression Model ◽  
Count Data ◽  
Poisson Regression ◽  
Negative Binomial ◽  
Predictor Variable ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Binomial Regression ◽  
The Mean ◽  
Generalized Poisson Regression

Tuberculosis is an infectious disease caused by Mycobacterium tuberculosis. Banyumas Regency is one of the districts with quite high Tuberculosis cases in Central Java. This study aims to analyze the factors that affect the number of tuberculosis cases in Banyumas Regency using regression analysis of count data. Poisson regression is the simplest count data regression model that has the assumption of equidispersion, that is, the mean value equal to the variance. However, in its application, these assumption is often not fulfilled, for example, there are cases of overdispersion (variance value is greater than the mean). In this study, to overcome the case of overdispersion, an approach was used using Generalized Poisson Regression (GPR) and negative binomial regression. The results showed that the data on the number of tuberculosis cases in Banyumas Regency in 2019 was overdispersion. The data modeling of the number of tuberculosis cases in Banyumas Regency with the negative binomial regression model is better than the GPR model. Meanwhile, the only predictor variable that affects the number of tuberculosis cases in Banyumas Regency is the sex ratio of productive age (15-49 years).

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