In the present study, we have developed new estimators for the estimation of finite population variance by using auxiliary information as combination of conventional and non-conventional measures. Bias and mean square error has been worked out up to the first order of approximation. The empirical study has been carried out through numerical demonstration, under which improved estimators have performed better than the other existing estimators. | Impact of robust estimators on variance estimation in survey sampling using conventional and non-conventional parameters as auxiliary information
