Two-parameter estimation for Tobit model: An application to national health and nutrition examination survey dataset

ÖZBAY N., ÜSTÜNDAĞ ŞİRAY G., TOKER KUTAY S., Yenilmez I.

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, cilt.34, sa.18, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 34 Sayı: 18
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1002/cpe.7014
  • Dergi Adı: CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Anahtar Kelimeler: censored data, multicollinearity, Tobit Liu estimator, Tobit maximum likelihood estimator, Tobit two-parameter estimator, CENSORED REGRESSION-MODEL, LIU-TYPE ESTIMATOR, URINARY ALBUMIN EXCRETION, RIDGE-REGRESSION, SHRINKAGE PARAMETERS, PROBIT REGRESSION, BIASED-ESTIMATION, PERFORMANCE, EFFICIENCY, MIXTURE
  • Çukurova Üniversitesi Adresli: Evet

Özet

Fixed censorship is usually encountered in medical fields, as well as biological, econometric, and engineering researches. It is quite possible to come across censored data with multicollinearity in real-life studies. The multicollinearity affects traditional Tobit maximum likelihood estimation, which is often used in presence of censored data. Biased estimators such as Tobit Liu estimator with one biasing parameter can be used for solving the multicollinearity. In this study, Tobit two-parameter estimator with two biasing parameters is proposed as an alternative to the classical Tobit maximum likelihood and Tobit Liu estimators for the censored data. This new estimator provides advantages from two different aspects thanks to having two biasing parameters. With a simulation study consisting of several scenarios where various levels of factors like the number of independent variables, sample size, correlation degree, variance, and censorship are examined, an in-depth experimental analysis is conducted. In these scenarios, two different biasing parameters' selection methods where one of the biasing parameters is fixed to determine the other one are used and their corresponding outputs are obtained. In addition, a real-life application is carried out with the National Health and Nutrition Examination Survey public-use dataset which includes some demographic and health-related laboratory measurements. The simulation study and data analysis results show that the proposed estimator is more advantageous to its competitors.