Access Restriction

Author Abbas, Nasir ♦ Aslam, Muhammad
Source CiteSeerX
Content type Text
File Format PDF
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Prior Distribution ♦ Paired Comparison Model ♦ Current Information ♦ Elicitation Purpose ♦ Population Parameter ♦ Pc Model ♦ Bayesian Approach ♦ Real Data Set ♦ Paired Comparison ♦ Subjective Assessment ♦ Certain Judge ♦ Bayesian Inference ♦ Minimum Chi-square ♦ Modern Statistical Technique ♦ Prior Information ♦ Posterior Distribution ♦ Formal Utilization ♦ Work Bench ♦ Bayesian Statistician ♦ Different Method ♦ Classical Estimation Technique ♦ Entire Elicitation Procedure
Abstract In the study of paired comparisons (PC), items may be ranked or issues may be prioritized through subjective assessment of certain judges. PC models are developed and then used to serve the purpose of ranking. The PC models may be studied through classical or Bayesian approach. Bayesian inference is a modern statistical technique used to draw conclusions about the population parameters. Its beauty lies in incorporating prior information about the parameters into the analysis in addition to current information (i.e. data). The prior and current information are formally combined to yield a posterior distribution about the population parameters, which is the work bench of the Bayesian statisticians. However, the problems the Bayesians face correspond with the selection and formal utilization of prior distribution. Once the type of prior distribution is decided to be used, the problem of estimating the parameters of the prior distribution (i.e. elicitation) still persists. Different methods are devised to serve the purpose. In this study an attempt is made to use Minimum Chi-square (hence forth MCS) for the elicitation purpose. Though it is a classical estimation technique, it is used here for the elicitation purpose. The entire elicitation procedure is illustrated by a real data set.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study