scholarly journals ANALISIS SURVIVAL MODEL REGRESI SEMIPARAMETRIK PADA LAMA STUDI MAHASISWA

2020 ◽  
Vol 5 (2) ◽  
pp. 94
Author(s):  
Novita Eka Chandra ◽  
Siti Alfiatur Rohmaniah
Keyword(s):  
Regression Model ◽  
Survival Data ◽  
Semiparametric Regression ◽  
Organizational Factors ◽  
Proportional Hazard ◽  
Academic Field ◽  
Semiparametric Regression Model ◽  
Cox Proportional Hazard ◽  
Regression Parameters ◽  
The University

In survival analysis to determine the relationship between variables is used a regression model, one of which uses the semiparametric regression model. The semiparametric regression model is a model that does not require assumptions or information on survival data distribution. That way, this model is more flexible in its use. In this study, the semiparametric regression model used the Cox Proportional Hazard (Cox PH) regression model. Estimation of Cox PH regression parameters can be done without determining the function baseline hazard. The purpose of this study is to determine the factors that influence the duration of student studies. If there are many students whose studies have not been on time, it shows that there is a lack of professionalism in the academic field of the educator. Thus, the community will assess the low quality of the university, resulting in a decrease in the number of students who want to study at the university. The samples in this study were students of class 2014 Universitas Islam Darul Ulum Lamongan. The variables have used the length of study for students, gender, GPA, school origin, organization, and work. Based on the results of the assumption Proportional Hazard (PH) conducted, all independent variables have fulfilled these assumptions, so that these variables can be used in Cox PH regression. After parameter estimation by Cox PH regression, the GPA and organizational factors significantly influence the duration of student study. Students with high GPA and participating in organizations more quickly complete their studies.

The Zero-Inflated Negative Binomial Semiparametric Regression Model: Application to Number of Failing Grades Data

2021 ◽  
Author(s):  
Elton G. Aráujo ◽  
Julio C. S. Vasconcelos ◽  
Denize P. dos Santos ◽  
Edwin M. M. Ortega ◽  
Dalton de Souza ◽  
...  
Keyword(s):  
Regression Model ◽  
Negative Binomial ◽  
Semiparametric Regression ◽  
Model Application ◽  
Semiparametric Regression Model

scholarly journals PERBANDINGAN MODEL REGRESI COX PROPORTIONAL HAZARD MENGGUNAKAN METODE BRESLOW DAN EFRON (Studi Kasus: Penderita Stroke di RSUD Tugurejo Kota Semarang)

2019 ◽  
Vol 8 (1) ◽  
pp. 93-105
Author(s):  
Eri Setiani ◽  
Sudarno Sudarno ◽  
Rukun Santoso
Keyword(s):  
Blood Pressure ◽  
Systolic Blood Pressure ◽  
Regression Model ◽  
Diastolic Blood Pressure ◽  
Proportional Hazard ◽  
Hazard Regression ◽  
Cox Proportional Hazard ◽  
History Of ◽  
Cox Proportional Hazard Regression ◽  
Sugar Levels

Cox proportional hazard regression is a regression model that is often used in survival analysis. Survival analysis is phrase used to describe analysis of data in the form of times from a well-defined time origin until occurrence of some particular even or end-point. In analysis survival sometimes ties are found, namely there are two or more individual that have together event. This study aims to apply Cox model on ties event using two methods, Breslow and Efron and determine factors that affect survival of stroke patients in Tugurejo Hospital Semarang. Dependent variable in this study is length of stay, then independent variables are gender, age, type of stroke, history of hypertension, systolic blood pressure, diastolic blood pressure, blood sugar levels, and BMI. The two methods give different result, Breslow has four significant variables there are type of stroke, history of hypertension, systolic blood pressure, and diastolic blood pressure, while Efron contains five significant variables such as type of stroke, history of hypertension, systolic blood pressure, diastolic blood pressure and blood sugar levels. From the smallest AIC criteria obtained the best Cox proportional hazard regression model is Efron method. Keywords: Stroke, Cox Proportional Hazard Regression model, Breslow method, Efron method.

scholarly journals Estimation of Heteroskedasticity Semiparametric Regression Curve Using Fourier Series Approach

2020 ◽  
Vol 2 (1) ◽  
pp. 14-20
Author(s):  
Rahmawati Pane ◽  
Sutarman
Keyword(s):  
Fourier Series ◽  
Regression Model ◽  
Variable Parameter ◽  
Predictor Variable ◽  
Semiparametric Regression ◽  
Least Square ◽  
Parameter Vector ◽  
Regression Curve ◽  
Semiparametric Regression Model ◽  
Main Components

A heteroskedastic semiparametric regression model consists of two main components, i.e. parametric component and nonparametric component. The model assumes that any data (x̰ i′ , t i , y i ) follows y i = x̰ i′ β̰+ f(t i ) + σ i ε i , where i = 1,2, … , n , x̰ i′ = (1, x i1 , x i2 , … , x ir ) and t i is the predictor variable. Parameter vector β̰ = (β 1 , β 2 , … , β r ) ′ ∈ ℜ r is unknown and f(t i ) is also unknown and is assumed to be in interval of C[0,π] . Random error ε i is independent on zero mean and varianceσ 2 . Estimation of the heteroskedastic semiparametric regression model was conducted to evaluate the parametric and nonparametric components. The nonparametric component f(t i ) regression was approximated by Fourier series F(t) = bt + 12 α 0 + ∑ α k 𝑐 𝑜𝑠 kt Kk=1 . The estimation was obtained by means of Weighted Penalized Least Square (WPLS): min f∈C(0,π) {n −1 (y̰− Xβ̰−f̰) ′ W −1 (y̰− Xβ̰− f̰) + λ ∫ 2π [f ′′ (t)] 2 dt π0 } . The WPLS solution provided nonparametric component f̰̂ λ (t) = M(λ)y̰ ∗ for a matrix M(λ) and parametric component β̰̂ = [X ′ T(λ)X] −1 X ′ T(λ)y̰

Sign in / Sign up

Export Citation Format

Share Document