I want to do comprehensive study of errors in variables from both numerical analysis and statistical viewpoint and compare the results with regression for selected parameter estimation problems in my domain where it is expected to perform better in terms of accuracy. These problems are of type linear and non linear regression. I want to check if the method under study is an improvement over generalized least squares. I am including multiple factors like accuracy, computational efficiency, robustness, sensitivity in my study under different combinations of stochastic models. What kind of statistical analysis/ experimental design/metric/hypothesis test is required for a study of this nature to establish superiority of one method over another(to make a recommendation of one method over another for a particular class of problems)
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See this link for previous research on your question: https://www.google.com/search?rlz=1C1CHBF_enUS874US874&biw=1584&bih=812&ei=mqpAXv_VGZOC9PwP5ZuNmAU&q=errors+in+variables+regression&oq=errors+in+variables+regression&gs_l=psy-ab.3..0l2j0i22i30l8.2142647.2159973..2160593...0.2..3.280.5074.52j5j1......0....1..gws-wiz.....6..0i71j0i67j0i362i308i154i357j0i131j0i273j0i3j0i10.orz9RHNAmgE&ved=0ahUKEwj_isvZ48XnAhUTAZ0JHeVNA1MQ4dUDCAs&uact=5
Best wishes, David Booth
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