• LINEAR MODEL WITH TWO PARAMETER DOUBLY TRUNCATED NEW SYMMETRIC DISTRIBUTED ERRORS

Abstract

Regression model is one of the most important statistical tool for analyzing data. In regression analysis it is customarily to consider that the error term follows Gaussian distribution which is mesokurtic and having infinite range. But in many data sets arising at places like agricultural experiments, biological experiments, financial analysis, space experiments, etc., the error term may not have mesokurtic in nature and its range is finite. As a result of it, the regression model with Gaussian errors may badly fit such data. To have accurate analysis for this type of data we develop and analyze a two variable regression model with doubly truncated new symmetric distributed errors. The model parameters are estimated by driving maximum likelihood estimators. The properties of this estimators are also discussed. Simulation study is conducted to compare the efficiency of proposed model with that of new symmetric distributed errors and Gaussian errors. It is observed that the proposed model performs much better than other two models when the variable are platykurtic and having constraints on tail ends.