Universal Point Estimation, with Applications in Economics, Business and Decision Sciences


  • Buu-Chau Truong Faculty of Mathematics and Statistics, Ton Duc Thang University, Ho Chi Minh City
  • Thi Diem-Chinh Ho Faculty of Mathematics and Statistics, University of Sciences, Ho Chi Minh City, Vietnam; General Faculty, Binh Duong Economics & Technology University, Binh Duong
  • Thu-Quang Luu Faculty of Finance, Banking University of Ho Chi Minh City
  • Michael McAleer Department of Finance, Asia University




Universal approach, Maximum likelihood, Moment method, Bayesian method.


Estimation is used widely in numerous disciplines, including Mathematics, Statistics, Economics, Business, and Decision Sciences, among others. Estimation is a process for determining an approximation, which is a value that can be used for a number of purposes, even if input data are sufficient, incomplete, missing or unsecure. In practice, estimation relates to “using the value of a statistic inferred from a sample to estimate the value of a corresponding population parameterâ€. Estimation is usually separated into two categories, namely point estimation and interval estimation. The main purpose of this paper is to present a universal approach to the theory and practice of three methods in statistical inference to obtain point estimates, namely the moment, maximum likelihood, and Bayesian methods. The paper also discusses the advantages and disadvantages of the three universal approaches in practical applications in Economics, Business and Decision Sciences.