## A new estimation procedure for linear combinations of exponentials |

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Admissible Estimates applied approximation asymptotic efficiencies asymptotic variances bias bias/X calculated Chapter coefficients combination of exponentials computed confidence interval confidence limits constant continuous converges in probability data in Table defined degrees of freedom denominator denotes determined distributed with mean Distributions for Large elementary symmetric functions elements equal errors estimation procedure estimators obtained evaluate expected experimental extension fitting held fixed Hence identically distributed variates Illustration inadmissible solutions increases instance inversely iterative least squares limiting distributions linear combination logarithms matrix maximum likelihood estimators mean zero MEANS AND VARIANCES mn s2 t2 necessary neighborhood number of observations number of parameters number of points observation points order partial derivatives polynomial Prony method Prony's regression respectively SAMPLE MEANS sample variances second order partial Section 3.3 set of equations shown Slutsky's theorem standard deviations Sturm's theorem substitution Taylor series values vector zero means