Abstract
Survival analysis is a statistical method that accommodates the collection of censored data. One of popular method in survival analysis is the Cox Proportional Hazard Regression. The Cox Proportional Hazard Regression can be used to see old looking for work where data may contain censored data. This article aims investigate the characteristics of job seekers and the variables that affect old looking for work. To establish the best model using Stepwise Selection method. Prior to that the assumption of Cox Proportional Hazards Regression is tested using log minus log curve. The results obtained from Cox Proportional Hazards Regression model is as follows
Highlights
Survival analysis is a statistical method that accommodates the collection of censored data
The Cox Proportional Hazard Regression can be used to see old looking for work where data may contain censored data
This article aims investigate the characteristics of job seekers and the variables that affect old looking for work
Summary
Analisis sintasan adalah nama modern yang diberikan untuk kumpulan metode statistika yang mengakomodasi data tersensor waktu kejadian (Tableman [5]). Salah satu pemodelan yang umum dan populer digunakan pada analisis sintasan adalah Regresi Cox Proportional Hazard. Regresi Cox Proportional Hazard dapat diterapkan pada bidang ketenagakerjaan. Karena dalam data lama mencari kerja terdapat data yang tersensor. Diperlukan analisis khusus untuk menangani kasus lama mencari kerja yaitu menggunakan analisis sintasan dengan metode Regresi Cox Proportional Hazard. Pada analisis sintasan secara khusus mengacu pada kejadian sebagai kegagalan, karena kejadian biasanya berhubungan dengan kematian, terjadinya penyakit, atau suatu pengalaman negatif individu. Penerapan Regresi Cox Proportional Hazard operasi bedah elektif”, yang mana dalam beberapa kasus kegagalan adalah kejadian yang positif (Perrigot [4]).Sebagian besar analisis sintasan harus mempertimbangkan kunci analisis masalah yaitu data tersensor. Nilai-nilai dugaan β diperoleh dengan memaksimumkan fungsi parsial likelihood (Collet [2])
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