Maximum Likelihood Estimation - Negative Binomial Regression Traffic Conflict Prediction Model Considering Variable Interactivity

2019 ◽  
Author(s):  
Wei Jiang ◽  
Yutian Tang ◽  
Leiyu Ge ◽  
Ming Li
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Prediction Model ◽  
Negative Binomial ◽  
Likelihood Estimation ◽  
Negative Binomial Regression ◽  
Binomial Regression ◽  
Traffic Conflict ◽  
Conflict Prediction

scholarly journals Estimasi Model Regresi Binomial Negatif Bivariat (BNBR) Pada Penderita Kusta di Jawa Timur

2019 ◽  
Vol 5 (2) ◽  
pp. 29-38
Author(s):  
WIGID HARIADI ◽  
Sulantari Sulantari
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Regression Model ◽  
Negative Binomial ◽  
Likelihood Estimation ◽  
Negative Binomial Regression ◽  
Predictor Variables ◽  
Negative Binomial Regression Model ◽  
Variable Ratio ◽  
Newton Raphson

Abstract. One of the methods used to overcome overdispersion in poisson regression model is a bivariate negative binomial regression model also known as BNBR Model. Leprosy is a dangerous infectious disease, because it can cause paralysis. Leprosy is divided into 2 types, namely is a leprosy Pausibasilier(PB) type and leprosy Multibasilier (MB) type. Where PB type leprosy is a dry leprosy and MB type leprosy is a wet leprosy. Analysis of the data used to model the number of PB leprosy and MB leprosy cases and find out what factor influence it in East Java, the writer uses the BNBR models. Parameter estimation of the BNBR model uses to Maximum likelihood estimation (MLE) methods with Newton-Raphson iteration as well as testing the hypothesis using MLRT methods. After regression analysis, the results are obtained that of the 10 predictor variables tested, both in PB leprosy and MB leprosy, there are 3 predictor variables that are not significant in the model, namely are: variable percentage of poor population, variable ratio of population who did not graduated SMA, and variable ratio of health facilities. Abstrak. Salah satu metode yang digunakan untuk mengatasi overdispersi dalam regresi Poisson yakni dengan regresi binomial negatif bivariat atau dikenal juga dengan model regresi BNBR. Penyakit Kusta adalah salah satu penyakit menular yang berbahaya, karena dapat menyebabkan kelumpuhan. Jenis penyakit kusta terbagi menjadi 2, yakni Kusta tipe Pausibasiler (PB) dan tipe Multibasiler.(MB). Dimana kusta tipe PB merupakan Kusta kering, dan kusta tipe MB adalah kusta basah. Analisis data yang digunakan untuk memodelkan besarnya jumlah kasus kusta tipePB dan tipe MB, kemudian untuk mengetahui faktor apa saja yang mempengaruhinya di Jawa Timur, penulis menggunakan model BNBR. Penaksiran parameter model BNBR menggunakan Maximum Likelihood Estimation (MLE) dengan iterasi Newton-Raphson serta melakukan pengujian hipotesis menggunakan metode MLRT. Setelah dilakukan analisis regresi, diperoleh hasil bahwa dari 10 variabel prediktor yang diujikan, baik pada kusta tipe PB maupun tipe MB, terdapat 3 variabel prediktor yang tidak signifikan dalam model, yakni: variabel presentase penduduk miskin, variabel rasio penduduk yang tidak tamat SMA, dan variabel rasio sarana kesehatan.

scholarly journals Identifying Stabilising Effects on Survey Based Life Satisfaction Using Quasi-maximum Likelihood Estimation

2021 ◽  
Author(s):  
Johannes Klement
Keyword(s):  
Life Satisfaction ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Negative Binomial ◽  
Measurement Instrument ◽  
Likelihood Estimation ◽  
Satisfaction Scale ◽  
Quasi Maximum Likelihood ◽  
The Stability

AbstractTo which extent do happiness correlates contribute to the stability of life satisfaction? Which method is appropriate to provide a conclusive answer to this question? Based on life satisfaction data of the German SOEP, we show that by Negative Binomial quasi-maximum likelihood estimation statements can be made as to how far correlates of happiness contribute to the stabilisation of life satisfaction. The results show that happiness correlates which are generally associated with a positive change in life satisfaction, also stabilise life satisfaction and destabilise dissatisfaction with life. In such as they lower the probability of leaving positive states of life satisfaction and increase the probability of leaving dissatisfied states. This in particular applies to regular exercise, volunteering and living in a marriage. We further conclude that both patterns in response behaviour and the quality of the measurement instrument, the life satisfaction scale, have a significant effect on the variation and stability of reported life satisfaction.

Prevalence data—models, likelihood, and binomial regression

2020 ◽  
pp. 47-63
Author(s):  
Bendix Carstensen
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Statistical Model ◽  
Likelihood Estimation ◽  
Data Models ◽  
Epidemiological Methods ◽  
Prevalence Data ◽  
Binomial Regression ◽  
Disease Condition ◽  
Age And Sex

This chapter examines prevalence data, using a dataset which contains the number of diabetes patients and the total number of persons in Denmark as of January 1, 2010, classified by age and sex. Prevalence of a disease condition in a population is merely the proportion of affected people. The chapter uses prevalence to illustrate core modelling concepts: the model itself, the likelihood, the maximum likelihood estimation principle, and the properties of the results, all of which underlies most modern epidemiological methods. It also explains the concept of a statistical model leading to the distinction between empirical and theoretical prevalences. The chapter then focuses on the task of comparing different models for the same data, models that describe data in various degrees of detail.

gidm: A command for generalized inflated discrete models

2019 ◽  
Vol 19 (3) ◽  
pp. 698-718
Author(s):  
Yiwei Xia ◽  
Yisu Zhou ◽  
Tianji Cai
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Negative Binomial ◽  
Likelihood Estimation ◽  
Discrete Distributions ◽  
Discrete Models

In this article, we describe the gidm command for fitting generalized inflated discrete models that deal with multiple inflated values in a distribution. Based on the work of Cai, Xia, and Zhou (Forthcoming, Sociological Methods & Research: Generalized inflated discrete models: A strategy to work with multimodal discrete distributions), generalized inflated discrete models are fit via maximum likelihood estimation. Specifically, the gidm command fits Poisson, negative binomial, multinomial, and ordered outcomes with more than one inflated value. We illustrate this command through examples for count and categorical outcomes.

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.  

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